Comparing the Math Anxiety of Secondary School Female Students in Groups (Science and Mathematical Physics) Public Schools

ERIC Educational Resources Information Center

Vakili, Khatoon; Pourrazavy, Zinat alsadat

2017-01-01

The aim of this study is comparing math anxiety of secondary school female students in groups (Science and Mathematical Physics) Public Schools, district 2, city of Sari. The purpose of the research is applied research, it is a development branch, and in terms of the nature and method, it is a causal-comparative research. The statistical…

Causal Inference and the Comparative Interrupted Time Series Design: Findings from Within-Study Comparisons

ERIC Educational Resources Information Center

St. Clair, Travis; Hallberg, Kelly; Cook, Thomas D.

2014-01-01

Researchers are increasingly using comparative interrupted time series (CITS) designs to estimate the effects of programs and policies when randomized controlled trials are not feasible. In a simple interrupted time series design, researchers compare the pre-treatment values of a treatment group time series to post-treatment values in order to…

Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report--Part II.

PubMed

Cox, Emily; Martin, Bradley C; Van Staa, Tjeerd; Garbe, Edeltraut; Siebert, Uwe; Johnson, Michael L

2009-01-01

The goal of comparative effectiveness analysis is to examine the relationship between two variables, treatment, or exposure and effectiveness or outcome. Unlike data obtained through randomized controlled trials, researchers face greater challenges with causal inference with observational studies. Recognizing these challenges, a task force was formed to develop a guidance document on methodological approaches to addresses these biases. The task force was commissioned and a Chair was selected by the International Society for Pharmacoeconomics and Outcomes Research Board of Directors in October 2007. This report, the second of three reported in this issue of the Journal, discusses the inherent biases when using secondary data sources for comparative effectiveness analysis and provides methodological recommendations to help mitigate these biases. The task force report provides recommendations and tools for researchers to mitigate threats to validity from bias and confounding in measurement of exposure and outcome. Recommendations on design of study included: the need for data analysis plan with causal diagrams; detailed attention to classification bias in definition of exposure and clinical outcome; careful and appropriate use of restriction; extreme care to identify and control for confounding factors, including time-dependent confounding. Design of nonrandomized studies of comparative effectiveness face several daunting issues, including measurement of exposure and outcome challenged by misclassification and confounding. Use of causal diagrams and restriction are two techniques that can improve the theoretical basis for analyzing treatment effects in study populations of more homogeneity, with reduced loss of generalizability.

The discourse of causal explanations in school science

NASA Astrophysics Data System (ADS)

Slater, Tammy Jayne Anne

Researchers and educators working from a systemic functional linguistic perspective have provided a body of work on science discourse which offers an excellent starting point for examining the linguistic aspects of the development of causal discourse in school science, discourse which Derewianka (1995) claimed is critical to success in secondary school. No work has yet described the development of causal language by identifying the linguistic features present in oral discourse or by comparing the causal discourse of native and non-native (ESL) speakers of English. The current research responds to this gap by examining the oral discourse collected from ESL and non-ESL students at the primary and high school grades. Specifically, it asks the following questions: (1) How do the teachers and students in these four contexts develop causal explanations and their relevant taxonomies through classroom interactions? (2) What are the causal discourse features being used by the students in these four contexts to construct oral causal explanations? The findings of the social practice analysis showed that the teachers in the four contexts differed in their approaches to teaching, with the primary school mainstream teacher focusing largely on the hands-on practice , the primary school ESL teacher moving from practice to theory, the high school mainstream teacher moving from theory to practice, and the high school ESL teacher relying primarily on theory. The findings from the quantitative, small corpus approach suggest that the developmental path of cause which has been identified in the writing of experts shows up not only in written texts but also in the oral texts which learners construct. Moreover, this move appears when the discourse of high school ESL and non-ESL students is compared, suggesting a developmental progression in the acquisition of these features by these students. The findings also reveal that the knowledge constructed, as shown by the concept maps created from the discourse, follows a developmental path similar to the linguistic causal path, from the concrete, hands-on, observable items to more abstract, theoretical concepts. This study is the first systemic functional comparison of the oral discourse of primary and secondary learners as well as the first to compare ESL and non-ESL speakers in this way, and as such it helps map general trends in causal discourse development. (Abstract shortened by UMI.)

Opening the Black Box and Searching for Smoking Guns: Process Causality in Qualitative Research

ERIC Educational Resources Information Center

Bennett, Elisabeth E.; McWhorter, Rochell R.

2016-01-01

Purpose: The purpose of this paper is to explore the role of qualitative research in causality, with particular emphasis on process causality. In one paper, it is not possible to discuss all the issues of causality, but the aim is to provide useful ways of thinking about causality and qualitative research. Specifically, a brief overview of the…

Non-manipulation quantitative designs.

PubMed

Rumrill, Phillip D

2004-01-01

The article describes non-manipulation quantitative designs of two types, correlational and causal comparative studies. Both of these designs are characterized by the absence of random assignment of research participants to conditions or groups and non-manipulation of the independent variable. Without random selection or manipulation of the independent variable, no attempt is made to draw causal inferences regarding relationships between independent and dependent variables. Nonetheless, non-manipulation studies play an important role in rehabilitation research, as described in this article. Examples from the contemporary rehabilitation literature are included. Copyright 2004 IOS Press

Enhancing causal interpretations of quality improvement interventions

PubMed Central

Cable, G

2001-01-01

In an era of chronic resource scarcity it is critical that quality improvement professionals have confidence that their project activities cause measured change. A commonly used research design, the single group pre-test/post-test design, provides little insight into whether quality improvement interventions cause measured outcomes. A re-evaluation of a quality improvement programme designed to reduce the percentage of bilateral cardiac catheterisations for the period from January 1991 to October 1996 in three catheterisation laboratories in a north eastern state in the USA was performed using an interrupted time series design with switching replications. The accuracy and causal interpretability of the findings were considerably improved compared with the original evaluation design. Moreover, the re-evaluation provided tangible evidence in support of the suggestion that more rigorous designs can and should be more widely employed to improve the causal interpretability of quality improvement efforts. Evaluation designs for quality improvement projects should be constructed to provide a reasonable opportunity, given available time and resources, for causal interpretation of the results. Evaluators of quality improvement initiatives may infrequently have access to randomised designs. Nonetheless, as shown here, other very rigorous research designs are available for improving causal interpretability. Unilateral methodological surrender need not be the only alternative to randomised experiments. Key Words: causal interpretations; quality improvement; interrupted time series design; implementation fidelity PMID:11533426

In Support of Clinical Case Reports: A System of Causality Assessment

PubMed Central

Hamre, Harald J.; Kienle, Gunver S.

2013-01-01

The usefulness of clinical research depends on an assessment of causality. This assessment determines what constitutes clinical evidence. Case reports are an example of evidence that is frequently overlooked because it is believed they cannot address causal links between treatment and outcomes. This may be a mistake. Clarity on the topic of causality and its assessment will be of benefit for researchers and clinicians. This article outlines an overall system of causality and causality assessment. The system proposed involves two dimensions: horizontal and vertical; each of these dimensions consists of three different types of causality and three corresponding types of causality assessment. Included in this system are diverse forms of case causality illustrated with examples from everyday life and clinical medicine. Assessing case causality can complement conventional clinical research in an era of personalized medicine. PMID:24416665

Comparing Distance vs. Campus-Based Delivery of Research Methods Courses

ERIC Educational Resources Information Center

Girod, Mark; Wojcikiewicz, Steve

2009-01-01

A causal-comparative pre-test, post-test design was used to investigate differences in learning in a research methods course for face-to-face and web-based delivery models. Analyses of participant achievement (N = 205) revealed almost no differences but post-hoc analyses revealed important differences in pedagogy between delivery models despite…

Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions.

PubMed

Snowden, Jonathan M; Tilden, Ellen L; Odden, Michelle C

2018-06-08

In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect. © 2018 by the American College of Nurse-Midwives.

Applying causal mediation analysis to personality disorder research.

PubMed

Walters, Glenn D

2018-01-01

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Translating context to causality in cardiovascular disparities research.

PubMed

Benn, Emma K T; Goldfeld, Keith S

2016-04-01

Moving from a descriptive focus to a comprehensive analysis grounded in causal inference can be particularly daunting for disparities researchers. However, even a simple model supported by the theoretical underpinnings of causality gives researchers a better chance to make correct inferences about possible interventions that can benefit our most vulnerable populations. This commentary provides a brief description of how race/ethnicity and context relate to questions of causality, and uses a hypothetical scenario to explore how different researchers might analyze the data to estimate causal effects of interest. Perhaps although not entirely removed of bias, these causal estimates will move us a step closer to understanding how to intervene. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Using Propensity Score Analysis for Making Causal Claims in Research Articles

ERIC Educational Resources Information Center

Bai, Haiyan

2011-01-01

The central role of the propensity score analysis (PSA) in observational studies is for causal inference; as such, PSA is often used for making causal claims in research articles. However, there are still some issues for researchers to consider when making claims of causality using PSA results. This summary first briefly reviews PSA, followed by…

Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

PubMed Central

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given. PMID:25505432

High School Learning Environments: Hybrid versus Traditional Formats

ERIC Educational Resources Information Center

Clifton, Mary Beth

2017-01-01

This research study examined the effects of hybrid course format as compared to face-to-face instruction format in a high school setting. At this time, there is little research on hybrid courses in the secondary schools. The quantitative portion of this ex post facto study utilized causal comparative design. Student data was collected from teacher…

Is the Allegiance Effect an Epiphenomenon of True Efficacy Differences between Treatments? A Meta-Analysis

ERIC Educational Resources Information Center

Munder, Thomas; Fluckiger, Christoph; Gerger, Heike; Wampold, Bruce E.; Barth, Jurgen

2012-01-01

Many meta-analyses of comparative outcome studies found a substantial association of researcher allegiance (RA) and relative treatment effects. Therefore, RA is regarded as a biasing factor in comparative outcome research (RA bias hypothesis). However, the RA bias hypothesis has been criticized as causality might be reversed. That is, RA might be…

Comparing the Cognitive Process of Circular Causality in Two Patients with Strokes through Qualitative Analysis.

PubMed

Derakhshanrad, Seyed Alireza; Piven, Emily; Ghoochani, Bahareh Zeynalzadeh

2017-10-01

Walter J. Freeman pioneered the neurodynamic model of brain activity when he described the brain dynamics for cognitive information transfer as the process of circular causality at intention, meaning, and perception (IMP) levels. This view contributed substantially to establishment of the Intention, Meaning, and Perception Model of Neuro-occupation in occupational therapy. As described by the model, IMP levels are three components of the brain dynamics system, with nonlinear connections that enable cognitive function to be processed in a circular causality fashion, known as Cognitive Process of Circular Causality (CPCC). Although considerable research has been devoted to study the brain dynamics by sophisticated computerized imaging techniques, less attention has been paid to study it through investigating the adaptation process of thoughts and behaviors. To explore how CPCC manifested thinking and behavioral patterns, a qualitative case study was conducted on two matched female participants with strokes, who were of comparable ages, affected sides, and other characteristics, except for their resilience and motivational behaviors. CPCC was compared by matrix analysis between two participants, using content analysis with pre-determined categories. Different patterns of thinking and behavior may have happened, due to disparate regulation of CPCC between two participants.

Enhancing causal interpretations of quality improvement interventions.

PubMed

Cable, G

2001-09-01

In an era of chronic resource scarcity it is critical that quality improvement professionals have confidence that their project activities cause measured change. A commonly used research design, the single group pre-test/post-test design, provides little insight into whether quality improvement interventions cause measured outcomes. A re-evaluation of a quality improvement programme designed to reduce the percentage of bilateral cardiac catheterisations for the period from January 1991 to October 1996 in three catheterisation laboratories in a north eastern state in the USA was performed using an interrupted time series design with switching replications. The accuracy and causal interpretability of the findings were considerably improved compared with the original evaluation design. Moreover, the re-evaluation provided tangible evidence in support of the suggestion that more rigorous designs can and should be more widely employed to improve the causal interpretability of quality improvement efforts. Evaluation designs for quality improvement projects should be constructed to provide a reasonable opportunity, given available time and resources, for causal interpretation of the results. Evaluators of quality improvement initiatives may infrequently have access to randomised designs. Nonetheless, as shown here, other very rigorous research designs are available for improving causal interpretability. Unilateral methodological surrender need not be the only alternative to randomised experiments.

Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

A Causal Model of Faculty Research Productivity.

ERIC Educational Resources Information Center

Bean, John P.

A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…

Causal Mediation in Educational Research: An Illustration Using International Assessment Data

ERIC Educational Resources Information Center

Caro, Daniel H.

2015-01-01

This paper applies the causal mediation framework proposed by Kosuke Imai and colleagues (Imai, Keele, & Tingley, 2010) to educational research by examining the causal mediating role of early literacy activities in parental education influences on reading performance. The paper argues that the study of causal mediation is particularly relevant…

Analogy in causal inference: rethinking Austin Bradford Hill's neglected consideration.

PubMed

Weed, Douglas L

2018-05-01

The purpose of this article was to rethink and resurrect Austin Bradford Hill's "criterion" of analogy as an important consideration in causal inference. In epidemiology today, analogy is either completely ignored (e.g., in many textbooks), or equated with biologic plausibility or coherence, or aligned with the scientist's imagination. None of these examples, however, captures Hill's description of analogy. His words suggest that there may be something gained by contrasting two bodies of evidence, one from an established causal relationship, the other not. Coupled with developments in the methods of systematic assessments of evidence-including but not limited to meta-analysis-analogy can be restructured as a key component in causal inference. This new approach will require that a collection-a library-of known cases of causal inference (i.e., bodies of evidence involving established causal relationships) be developed. This library would likely include causal assessments by organizations such as the International Agency for Research on Cancer, the National Toxicology Program, and the United States Environmental Protection Agency. In addition, a process for describing key features of a causal relationship would need to be developed along with what will be considered paradigm cases of causation. Finally, it will be important to develop ways to objectively compare a "new" body of evidence with the relevant paradigm case of causation. Analogy, along with all other existing methods and causal considerations, may improve our ability to identify causal relationships. Copyright © 2018 Elsevier Inc. All rights reserved.

Omission of Causal Indicators: Consequences and Implications for Measurement

ERIC Educational Resources Information Center

Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.

2016-01-01

One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…

Change, Self-Organization and the Search for Causality in Educational Research and Practice

ERIC Educational Resources Information Center

Koopmans, Matthijs

2014-01-01

Causality is an inextricable part of the educational process, as our understanding of what works in education depends on our ability to make causal attributions. Yet, the research and policy literature in education tends to define causality narrowly as the attribution of educational outcomes to intervention effects in a randomized control trial…

Model Ambiguities in Configurational Comparative Research

ERIC Educational Resources Information Center

Baumgartner, Michael; Thiem, Alrik

2017-01-01

For many years, sociologists, political scientists, and management scholars have readily relied on Qualitative Comparative Analysis (QCA) for the purpose of configurational causal modeling. However, this article reveals that a severe problem in the application of QCA has gone unnoticed so far: model ambiguities. These arise when multiple causal…

The Relationship between Principal Leadership Practices and Teacher Morale

ERIC Educational Resources Information Center

Williams, Fabre K.

2013-01-01

This research explores the relationship of principal leadership practices and teacher morale. Six schools in a West Tennessee school system participated in the study. The participants in the study were executive principals and classroom teachers. The study was a descriptive, causal-comparative research design chosen to examine the possible…

Student Participation in Dual Enrollment and College Success

ERIC Educational Resources Information Center

Jones, Stephanie J.

2014-01-01

The study investigated the impact of dual enrollment participation on the academic preparation of first-year full-time college students at a large comprehensive community college and a large research university. The research design was causal-comparative and utilized descriptive and inferential statistics. Multivariate analysis of variances were…

Do Leadership Styles Influence Organizational Health? A Study in Educational Organizations

ERIC Educational Resources Information Center

Toprak, Mustafa; Inandi, Bulent; Colak, Ahmet Levent

2015-01-01

This research aims to investigate the effect of leadership styles of school principals on organizational health. Causal-comparative research model was used to analyze the relationships between leadership types and organizational health. For data collection, a Likert type Multifactor Leadership scale questionnaire and Organizational Health scale…

A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research

ERIC Educational Resources Information Center

Rohlfing, Ingo; Schneider, Carsten Q.

2018-01-01

The combination of Qualitative Comparative Analysis (QCA) with process tracing, which we call set-theoretic multimethod research (MMR), is steadily becoming more popular in empirical research. Despite the fact that both methods have an elected affinity based on set theory, it is not obvious how a within-case method operating in a single case and a…

An Action Research Study on the Effect of Interactive Technology and Active Learning on Student Performance

ERIC Educational Resources Information Center

Bear, Teresa J.

2013-01-01

This quantitative action science research study utilized a causal-comparative experimental research design in order to determine if the use of student response systems (clickers), as an active learning strategy in a community college course, improved student performance in the course. Students in the experimental group (n = 26) used clickers to…

Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure

PubMed Central

Verma, Ajay K.; Xu, Da; Garg, Amanmeet; Cote, Anita T.; Goswami, Nandu; Blaber, Andrew P.; Tavakolian, Kouhyar

2017-01-01

Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to −40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR↔SBP and RR↔MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR→SBP and RR→MAP) showed a significantly (p < 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP→RR and MAP→RR). In response to moderate category hemorrhage (−30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR→SBP (p = 0.76), RR→MAP (p = 0.60), and SBP→RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP→RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP→RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression. PMID:29114227

The Teacher, the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs

PubMed Central

Boerebach, Benjamin C. M.; Lombarts, Kiki M. J. M. H.; Scherpbier, Albert J. J.; Arah, Onyebuchi A.

2013-01-01

Background In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty’s teaching performance on their role modeling. Therefore, there is a need for robust frameworks and methods for transparent formal presentation of the underlying causal assumptions used in assessing the causal effects of teaching performance on role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes. Methods This study revisits a previously published study about the influence of faculty’s teaching performance on their role modeling (as teacher-supervisor, physician and person). We drew eight directed acyclic graphs (DAGs) to visually represent different plausible causal relationships between the variables under study. These DAGs were subsequently translated into corresponding statistical models, and regression analyses were performed to estimate the associations between teaching performance and role modeling. Results The different causal models were compatible with major differences in the magnitude of the relationship between faculty’s teaching performance and their role modeling. Odds ratios for the associations between teaching performance and the three role model types ranged from 31.1 to 73.6 for the teacher-supervisor role, from 3.7 to 15.5 for the physician role, and from 2.8 to 13.8 for the person role. Conclusions Different sets of assumptions about causal relationships in role modeling research can be visually depicted using DAGs, which are then used to guide both statistical analysis and interpretation of results. Since study conclusions can be sensitive to different causal assumptions, results should be interpreted in the light of causal assumptions made in each study. PMID:23936020

A Comparative Study of Academic Achievement and Participation in a High School Freshman Academy

ERIC Educational Resources Information Center

Seng, Mark Patrick

2014-01-01

The transition to high school can be problematic for many ninth graders. Researchers and administrators have sought ways to improve academic performance and promotion rates while reducing dropout rates. A quantitative causal-comparative (ex post facto) and correlation study using a two-group design compared two freshman classes at separate…

From "At Risk" to "At Promise": An Evaluation of an Early Reading First Project

ERIC Educational Resources Information Center

Zoll, Susan Marie

2012-01-01

This study demonstrates the impact of an Early Reading First intervention on preschool children's language and literacy development using an ex post facto, causal-comparative research design. The project's professional development model was evaluated to produce a process and outcome evaluation to answer two overarching research questions: (1) What…

Opportunities and Challenges in Using Studies without a Control Group in Comparative Effectiveness Reviews

ERIC Educational Resources Information Center

Paulus, Jessica K.; Dahabreh, Issa J.; Balk, Ethan M.; Avendano, Esther E.; Lau, Joseph; Ip, Stanley

2014-01-01

When examining the evidence on therapeutic interventions to answer a comparative effectiveness research question, one should consider all studies that are informative on the interventions' causal effects. "Single group studies" evaluate outcomes longitudinally in cohorts of subjects who are managed with a single treatment strategy.…

Factors Influencing Teacher Absenteeism in a Middle Tennessee School System

ERIC Educational Resources Information Center

Shockley, Kristy

2012-01-01

Using a causal-comparative research design, this study analyzed and compared absence data of teachers in relation to specific teacher demographics to determine if certain teachers were more susceptible to absenteeism than others. Additionally, school level placement and whether or not a teacher was teaching in a Title I school were analyzed to…

The Effects of Differentiated Instruction Support Inclusion Services on Fifth Grade Reading/Language Arts Achievement

ERIC Educational Resources Information Center

Wendt, Stephanie L.

2012-01-01

Using a causal-comparative research design, this study investigated the effectiveness of Differentiated Instruction Support Inclusion Services on fifth grade regular education and gifted students' Reading/Language Arts achievement. The study analyzed and compared the achievement of the regular education students who received no inclusion support…

Does Causal Action Facilitate Causal Perception in Infants Younger than 6 Months of Age?

ERIC Educational Resources Information Center

Rakison, David H.; Krogh, Lauren

2012-01-01

Previous research has established that infants are unable to perceive causality until 6 1/4 months of age. The current experiments examined whether infants' ability to engage in causal action could facilitate causal perception prior to this age. In Experiment 1, 4 1/2-month-olds were randomly assigned to engage in causal action experience via…

What is the nature of causality in the brain? - Inherently probabilistic. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

NASA Astrophysics Data System (ADS)

Dhamala, Mukesh

2015-12-01

Understanding cause-and-effect (causal) relations from observations concerns all sciences including neuroscience. Appropriately defining causality and its nature, though, has been a topic of active discussion for philosophers and scientists for centuries. Although brain research, particularly functional neuroimaging research, is now moving rapidly beyond identification of brain regional activations towards uncovering causal relations between regions, the nature of causality has not be been thoroughly described and resolved. In the current review article [1], Mannino and Bressler take us on a beautiful journey into the history of the work on causality and make a well-reasoned argument that the causality in the brain is inherently probabilistic. This notion is consistent with brain anatomy and functions, and is also inclusive of deterministic cases of inputs leading to outputs in the brain.

Using genetic data to strengthen causal inference in observational research.

PubMed

Pingault, Jean-Baptiste; O'Reilly, Paul F; Schoeler, Tabea; Ploubidis, George B; Rijsdijk, Frühling; Dudbridge, Frank

2018-06-05

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Exploratory Causal Analysis in Bivariate Time Series Data

NASA Astrophysics Data System (ADS)

McCracken, James M.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data sets, but little research exists of how these tools compare to each other in practice. This work introduces and defines exploratory causal analysis (ECA) to address this issue along with the concept of data causality in the taxonomy of causal studies introduced in this work. The motivation is to provide a framework for exploring potential causal structures in time series data sets. ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

CauseMap: fast inference of causality from complex time series.

PubMed

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

Sufficiency and Necessity Assumptions in Causal Structure Induction

ERIC Educational Resources Information Center

Mayrhofer, Ralf; Waldmann, Michael R.

2016-01-01

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…

Interpretational confounding is due to misspecification, not to type of indicator: comment on Howell, Breivik, and Wilcox (2007).

PubMed

Bollen, Kenneth A

2007-06-01

R. D. Howell, E. Breivik, and J. B. Wilcox (2007) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal (formative) indicators rests on several claims: (a) A latent variable exists apart from the model when there are effect (reflective) indicators but not when there are causal (formative) indicators, (b) causal (formative) indicators need not have the same consequences, (c) causal (formative) indicators are inherently subject to interpretational confounding, and (d) a researcher cannot detect interpretational confounding when using causal (formative) indicators. This article shows that each claim is false. Rather, interpretational confounding is more a problem of structural misspecification of a model combined with an underidentified model that leaves these misspecifications undetected. Interpretational confounding does not occur if the model is correctly specified whether a researcher has causal (formative) or effect (reflective) indicators. It is the validity of a model not the type of indicator that determines the potential for interpretational confounding. Copyright 2007 APA, all rights reserved.

Identifying causal linkages between environmental variables and African conflicts

NASA Astrophysics Data System (ADS)

Nguy-Robertson, A. L.; Dartevelle, S.

2017-12-01

Environmental variables that contribute to droughts, flooding, and other natural hazards are often identified as factors contributing to conflict; however, few studies attempt to quantify these causal linkages. Recent research has demonstrated that the environment operates within a dynamical system framework and the influence of variables can be identified from convergent cross mapping (CCM) between shadow manifolds. We propose to use CCM to identify causal linkages between environmental variables and incidences of conflict. This study utilizes time series data from Climate Forecast System ver. 2 and MODIS satellite sensors processed using Google Earth Engine to aggregate country and regional trends. These variables are then compared to Armed Conflict Location & Event Data Project observations at similar scales. Results provide relative rankings of variables and their linkage to conflict. Being able to identify which factors contributed more strongly to a conflict can allow policy makers to prepare solutions to mitigate future crises. Knowledge of the primary environmental factors can lead to the identification of other variables to examine in the causal network influencing conflict.

What causes breast cancer? A systematic review of causal attributions among breast cancer survivors and how these compare to expert-endorsed risk factors.

PubMed

Dumalaon-Canaria, Jo Anne; Hutchinson, Amanda D; Prichard, Ivanka; Wilson, Carlene

2014-07-01

The aim of this paper was to review published research that analyzed causal attributions for breast cancer among women previously diagnosed with breast cancer. These attributions were compared with risk factors identified by published scientific evidence in order to determine the level of agreement between cancer survivors' attributions and expert opinion. A comprehensive search for articles, published between 1982 and 2012, reporting studies on causal attributions for breast cancer among patients and survivors was undertaken. Of 5,135 potentially relevant articles, 22 studies met the inclusion criteria. Two additional articles were sourced from reference lists of included studies. Results indicated a consistent belief among survivors that their own breast cancer could be attributed to family history, environmental factors, stress, fate, or chance. Lifestyle factors were less frequently identified, despite expert health information highlighting the importance of these factors in controlling and modifying cancer risk. This review demonstrated that misperceptions about the contribution of modifiable lifestyle factors to the risk of breast cancer have remained largely unchanged over the past 30 years. The findings of this review indicate that beliefs about the causes of breast cancer among affected women are not always consistent with the judgement of experts. Breast cancer survivors did not regularly identify causal factors supported by expert consensus such as age, physical inactivity, breast density, alcohol consumption, and reproductive history. Further research examining psychological predictors of attributions and the impact of cancer prevention messages on adjustment and well-being of cancer survivors is warranted.

Squeezing observational data for better causal inference: Methods and examples for prevention research.

PubMed

Garcia-Huidobro, Diego; Michael Oakes, J

2017-04-01

Randomised controlled trials (RCTs) are typically viewed as the gold standard for causal inference. This is because effects of interest can be identified with the fewest assumptions, especially imbalance in background characteristics. Yet because conducting RCTs are expensive, time consuming and sometimes unethical, observational studies are frequently used to study causal associations. In these studies, imbalance, or confounding, is usually controlled with multiple regression, which entails strong assumptions. The purpose of this manuscript is to describe strengths and weaknesses of several methods to control for confounding in observational studies, and to demonstrate their use in cross-sectional dataset that use patient registration data from the Juan Pablo II Primary Care Clinic in La Pintana-Chile. The dataset contains responses from 5855 families who provided complete information on family socio-demographics, family functioning and health problems among their family members. We employ regression adjustment, stratification, restriction, matching, propensity score matching, standardisation and inverse probability weighting to illustrate the approaches to better causal inference in non-experimental data and compare results. By applying study design and data analysis techniques that control for confounding in different ways than regression adjustment, researchers may strengthen the scientific relevance of observational studies. © 2016 International Union of Psychological Science.

Causal Inferences with Large Scale Assessment Data: Using a Validity Framework

ERIC Educational Resources Information Center

Rutkowski, David; Delandshere, Ginette

2016-01-01

To answer the calls for stronger evidence by the policy community, educational researchers and their associated organizations increasingly demand more studies that can yield causal inferences. International large scale assessments (ILSAs) have been targeted as a rich data sources for causal research. It is in this context that we take up a…

What Doesn't Go without Saying: Communication, Induction, and Exploration

ERIC Educational Resources Information Center

Muentener, Paul; Schulz, Laura

2012-01-01

Although prior research on the development of causal reasoning has focused on inferential abilities within the individual child, causal learning often occurs in a social and communicative context. In this paper, we review recent research from our laboratory and look at how linguistic communication may influence children's causal reasoning. First,…

Child Care Subsidy Use and Child Development: Potential Causal Mechanisms

ERIC Educational Resources Information Center

Hawkinson, Laura E.

2011-01-01

Research using an experimental design is needed to provide firm causal evidence on the impacts of child care subsidy use on child development, and on underlying causal mechanisms since subsidies can affect child development only indirectly via changes they cause in children's early experiences. However, before costly experimental research is…

Functional ability loss in sensory impaired and sensory unimpaired very old adults: analyzing causal relations with positive affect across four years.

PubMed

Wahl, Hans-Werner; Drapaniotis, Philipp M; Heyl, Vera

2014-11-01

This paper focuses on the relationship between functional ability (FA) and positive affect (PA), a major component of well-being, in sensory impaired very old adults (SI) compared with sensory unimpaired individuals (UI). Previous research mostly suggests a robust causal impact of FA on PA. However, some research, drawing from Fredrickson's broaden-and-build theory, also points to the possibility of an inverse causality between FA and PA. We examine in this paper both of these causal directions in SI as well as UI individuals across a 4year observation period. Additionally, we checked for the role of negative affect (NA). The T1-T2 sample comprised 81 out of 237 SI individuals (visually or hearing impaired) assessed at T1, with a mean age at T1 of 81.8years, and 87 UI individuals out of 150 assessed at T1, with a mean age at T1 of 81.5years. Established scales were used to assess FA, PA, and NA. Using cross-lagged panel analysis to examine the direction of causality, our findings indicate that FA has significant impact on PA in both the SI and the UI group, whereas the alternative causal pathway was not confirmed. Both cross-lagged relationships between FA and NA were non-significant. No group differences in path strengths between SI and UI were present. Our study provides evidence that FA is a key competence for successful emotional aging in vulnerable groups of very old adults such as SI as well as in UI adults in advanced old age. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparing Types of Student Placement and the Effect on Achievement for Students with Disabilities

ERIC Educational Resources Information Center

Mason, Patricia Lynn

2013-01-01

Since implementing No Child Left Behind, schools have improved student achievement while also preparing students for the 21st century. Schools continue to strive for 100% proficiency in all subgroups by 2014, but achievement gap exists for students with disabilities. This study used a causal comparative research design to test the concept of…

The Effects of an Academic Alternative High School on Academically At-Risk Students

ERIC Educational Resources Information Center

Winningham, Mark L.

2012-01-01

In a causal-comparative research design, this study investigated the effectiveness of an academic alternative school in improving at-risk student outcomes in a selected county school system in the Upper Cumberland region of Tennessee. The academic alternative high school was compared to a traditional high school serving at-risk populations.…

Examination of Moral Decision-Making Attitudes of Elite Male Basketball Players and Wrestlers According to Variables of Sport Experience and Mother, Father Education

ERIC Educational Resources Information Center

Arslan, Fethi; Ziyagil, Mehmet Akif; Bastik, Canan

2018-01-01

The purpose of this research was to examine the extent to which sport moral decision-making attitudes were applied by the athletes, and the factors that caused it. The research was based on the causal comparative research model. The research group consisted of a total of 475 athletes, of which 195 were basketball athletes randomly selected from…

Attributions of Social Causality and Responsibility.

DTIC Science & Technology

The paper reviews relevant research on attributions of causality and attributions of responsibility . It is suggested that inconsistencies among...findings in the attribution literature may be due to discrepancies between the meaning of ’ responsibility ’ and ’causality’. Definitions for the two terms...opposed to responsibility attribution may serve to eliminate some of the problems in attribution research. (Author)

A call for theory to support the use of causal-formative indicators: A commentary on Bollen and Diamantopoulos (2017).

PubMed

Hardin, Andrew

2017-09-01

In this issue, Bollen and Diamantopoulos (2017) defend causal-formative indicators against several common criticisms leveled by scholars who oppose their use. In doing so, the authors make several convincing assertions: Constructs exist independently from their measures; theory determines whether indicators cause or measure latent variables; and reflective and causal-formative indicators are both subject to interpretational confounding. However, despite being a well-reasoned, comprehensive defense of causal-formative indicators, no single article can address all of the issues associated with this debate. Thus, Bollen and Diamantopoulos leave a few fundamental issues unresolved. For example, how can researchers establish the reliability of indicators that may include measurement error? Moreover, how should researchers interpret disturbance terms that capture sources of influence related to both the empirical definition of the latent variable and to the theoretical definition of the construct? Relatedly, how should researchers reconcile the requirement for a census of causal-formative indicators with the knowledge that indicators are likely missing from the empirically estimated latent variable? This commentary develops 6 related research questions to draw attention to these fundamental issues, and to call for future research that can lead to the development of theory to guide the use of causal-formative indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Using Qualitative Comparative Analysis (QCA) of Key Informant Interviews in Health Services Research: Enhancing a Study of Adjuvant Therapy Use in Breast Cancer Care

PubMed Central

McAlearney, Ann Scheck; Walker, Daniel; Moss, Alexandra DeNardis; Bickell, Nina A.

2015-01-01

Background Qualitative Comparative Analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. Objective To explore the strengths and weaknesses of using QCA for HSR. Research Design Using data from semi-structured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Subjects Ninety administrative and clinical key informants interviewed across ten NYC area safety net hospitals. Measures Transcribed interviews were coded by three investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Results Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Conclusions Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods. PMID:26908085

What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation.

PubMed

Magaard, Julia Luise; Schulz, Holger; Brütt, Anna Levke

2017-01-01

Patients' causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients' causal beliefs about their mental disorder. (a) To qualitatively explore patients' causal beliefs of their mental disorder, (b) to explore frequencies of patients stating causal beliefs, and (c) to investigate differences of causal beliefs according to patients' primary diagnoses. Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341), adjustment disorder (N = 75), reaction to severe stress (N = 57) and anxiety disorders (N = 40). Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses. The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to "problems at work" (47%) and "problems in social environment" (46%) were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to "self/internal states". Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression. There was no opportunity for further exploration, because we analysed written documents. These results add a detailed insight to mentally ill patients' causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients' causal beliefs and the chosen treatment.

What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation

PubMed Central

Magaard, Julia Luise; Schulz, Holger; Brütt, Anna Levke

2017-01-01

Background Patients’ causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients’ causal beliefs about their mental disorder. Objectives (a) To qualitatively explore patients’ causal beliefs of their mental disorder, (b) to explore frequencies of patients stating causal beliefs, and (c) to investigate differences of causal beliefs according to patients’ primary diagnoses. Method Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341), adjustment disorder (N = 75), reaction to severe stress (N = 57) and anxiety disorders (N = 40). Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses. Results The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to “problems at work” (47%) and “problems in social environment” (46%) were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to “self/internal states”. Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression. Limitations There was no opportunity for further exploration, because we analysed written documents. Conclusions These results add a detailed insight to mentally ill patients’ causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients’ causal beliefs and the chosen treatment. PMID:28056066

Separated at Birth: Statisticians, Social Scientists, and Causality in Health Services Research

PubMed Central

Dowd, Bryan E

2011-01-01

Objective Health services research is a field of study that brings together experts from a wide variety of academic disciplines. It also is a field that places a high priority on empirical analysis. Many of the questions posed by health services researchers involve the effects of treatments, patient and provider characteristics, and policy interventions on outcomes of interest. These are causal questions. Yet many health services researchers have been trained in disciplines that are reluctant to use the language of causality, and the approaches to causal questions are discipline specific, often with little overlap. How did this situation arise? This paper traces the roots of the division and some recent attempts to remedy the situation. Data Sources and Settings Existing literature. Study Design Review of the literature. PMID:21105867

Reasoning about Causal Relationships: Inferences on Causal Networks

PubMed Central

Rottman, Benjamin Margolin; Hastie, Reid

2013-01-01

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658

On the factorial and construct validity of the Intrinsic Motivation Inventory: conceptual and operational concerns.

PubMed

Markland, D; Hardy, L

1997-03-01

The Intrinsic Motivation Inventory (IMI) has been gaining acceptance in the sport and exercise domain since the publication of research by McAuley, Duncan, and Tammen (1989) and McAuley, Wraith, and Duncan (1991), which reported confirmatory support for the factorial validity of a hierarchical model of intrinsic motivation. Authors of the present study argue that the results of these studies did not conclusively support the hierarchical model and that the model did not accurately reflect the tenets of cognitive evaluation theory (Deci & Ryan, 1985) from which the IMI is drawn. It is also argued that a measure of perceived locus of causality is required to model intrinsic motivation properly. The development of a perceived locus of causality for exercise scale is described, and alternative models, in which perceived competence and perceived locus of causality are held to have causal influences on intrinsic motivation, are compared with an oblique confirmatory factor analytic model in which the constructs are held at the same conceptual level. Structural equation modeling showed support for a causal model in which perceived locus of causality mediates the effects of perceived competence on pressure-tension, interest-enjoyment, and effort-importance. It is argued that conceptual and operational problems with the IMI, as currently used, should be addressed before it becomes established as the instrument of choice for assessing levels of intrinsic motivation.

Best Practices for Gauging Evidence of Causality in Air Pollution Epidemiology.

PubMed

Dominici, Francesca; Zigler, Corwin

2017-12-15

The contentious political climate surrounding air pollution regulations has brought some researchers and policy-makers to argue that evidence of causality is necessary before implementing more stringent regulations. Recently, investigators in an increasing number of air pollution studies have purported to have used "causal analysis," generating the impression that studies not explicitly labeled as such are merely "associational" and therefore less rigorous. Using 3 prominent air pollution studies as examples, we review good practices for how to critically evaluate the extent to which an air pollution study provides evidence of causality. We argue that evidence of causality should be gauged by a critical evaluation of design decisions such as 1) what actions or exposure levels are being compared, 2) whether an adequate comparison group was constructed, and 3) how closely these design decisions approximate an idealized randomized study. We argue that air pollution studies that are more scientifically rigorous in terms of the decisions made to approximate a randomized experiment are more likely to provide evidence of causality and should be prioritized among the body of evidence for regulatory review accordingly. Our considerations, although presented in the context of air pollution epidemiology, can be broadly applied to other fields of epidemiology. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Perceived Causal Relations: Novel Methodology for Assessing Client Attributions about Causal Associations between Variables Including Symptoms and Functional Impairment

ERIC Educational Resources Information Center

Frewen, Paul A.; Allen, Samantha L.; Lanius, Ruth A.; Neufeld, Richard W. J.

2012-01-01

Researchers have argued that the investigation of causal interrelationships between symptoms may help explain the high comorbidity rate between certain psychiatric disorders. Clients' own attributions concerning the causal interrelationships linking the co-occurrence of their symptoms represent data that may inform their clinical case…

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

ERIC Educational Resources Information Center

Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L.

2016-01-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…

New Insights into Signed Path Coefficient Granger Causality Analysis.

PubMed

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

A qualitative study evaluating causality attribution for serious adverse events during early phase oncology clinical trials.

PubMed

Mukherjee, Som D; Coombes, Megan E; Levine, Mitch; Cosby, Jarold; Kowaleski, Brenda; Arnold, Andrew

2011-10-01

In early phase oncology trials, novel targeted therapies are increasingly being tested in combination with traditional agents creating greater potential for enhanced and new toxicities. When a patient experiences a serious adverse event (SAE), investigators must determine whether the event is attributable to the investigational drug or not. This study seeks to understand the clinical reasoning, tools used and challenges faced by the researchers who assign causality to SAE's. Thirty-two semi-structured interviews were conducted with medical oncologists and trial coordinators at six Canadian academic cancer centres. Interviews were recorded and transcribed verbatim. Individual interview content analysis was followed by thematic analysis across the interview set. Our study found that causality assessment tends to be a rather complex process, often without complete clinical and investigational data at hand. Researchers described using a common processing strategy whereby they gather pertinent information, eliminate alternative explanations, and consider whether or not the study drug resulted in the SAE. Many of the interviewed participants voiced concern that causality assessments are often conducted quickly and tend to be highly subjective. Many participants were unable to identify any useful tools to help in assigning causality and welcomed more objectivity in the overall process. Attributing causality to SAE's is a complex process. Clinical trial researchers apply a logical system of reasoning, but feel that the current method of assigning causality could be improved. Based on these findings, future research involving the development of a new causality assessment tool specifically for use in early phase oncology clinical trials may be useful.

The Importance of Specifying and Studying Causal Mechanisms in School-Based Randomised Controlled Trials: Lessons from Two Studies of Cross-Age Peer Tutoring

ERIC Educational Resources Information Center

Morris, Stephen P.; Edovald, Triin; Lloyd, Cheryl; Kiss, Zsolt

2016-01-01

Based on the experience of evaluating 2 cross-age peer-tutoring interventions, we argue that researchers need to pay greater attention to causal mechanisms within the context of school-based randomised controlled trials. Without studying mechanisms, researchers are less able to explain the underlying causal processes that give rise to results from…

How Is the Ideal Gas Law Explanatory?

ERIC Educational Resources Information Center

Woody, Andrea I.

2013-01-01

Using the ideal gas law as a comparative example, this essay reviews contemporary research in philosophy of science concerning scientific explanation. It outlines the inferential, causal, unification, and erotetic conceptions of explanation and discusses an alternative project, the functional perspective. In each case, the aim is to highlight…

Automated Writing Evaluation Program's Effect on Student Writing Achievement

ERIC Educational Resources Information Center

Holman, Lester Donnie

2011-01-01

In an ex post facto causal-comparative research design, this study investigated the effectiveness of Automated Writing Evaluation (AWE) programs on raising the student writing achievement. Tennessee Comprehensive Assessment Program (TCAP) writing achievement scores from the 2010 administration were utilized for this study. The independent variable…

Triangulation in aetiological epidemiology

PubMed Central

Lawlor, Debbie A; Tilling, Kate; Davey Smith, George

2016-01-01

Abstract Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points. PMID:28108528

Afro-Caribbean and African American Students, Family Factors, and the Influence on Science Performance in the United States: The Untold Story

ERIC Educational Resources Information Center

Pinder, Patrice Juliet

2012-01-01

The primary objectives of this research were to explore achievement pattern differences and the influence of family factors on the achievement patterns of Afro-Caribbean and African American students within the United States (U.S.). The study utilized two research designs; a causal-comparative and a correlational design. A student family…

Begin with the End in Mind: Measuring the Effectiveness of Remediation on Student Performance on the Test of Essential Academic Skills (TEAS-V)

ERIC Educational Resources Information Center

Cumberbatch-Sullivan, Karen J.

2013-01-01

This quantitative, causal-comparative research study investigated the effectiveness of a strategy to address concerns in nursing education about the high attrition rates and poor retention rates of pre-licensure nursing students, particularly in the first semester of nursing school. Two research questions guided this study and focused on the…

A Multidimensional View of Resistance to Organizational Change: Exploring Cognitive, Emotional, and Intentional Responses to Planned Change across Perceived Change Leadership Strategies

ERIC Educational Resources Information Center

Szabla, David B.

2007-01-01

In this survey research study, the researcher employed a causal-comparative, or ex post facto, design to explore the relationship between how union employees of a U.S. county government perceived implementation of a new electronic performance appraisal process and how they responded to the planned organizational change along cognitive, emotional,…

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly.

PubMed

Moura, Lidia Mvr; Westover, M Brandon; Kwasnik, David; Cole, Andrew J; Hsu, John

2017-01-01

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

Using Qualitative Comparative Analysis of Key Informant Interviews in Health Services Research: Enhancing a Study of Adjuvant Therapy Use in Breast Cancer Care.

PubMed

McAlearney, Ann Scheck; Walker, Daniel; Moss, Alexandra D; Bickell, Nina A

2016-04-01

Qualitative comparative analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. To explore the strengths and weaknesses of using QCA for HSR. Using data from semistructured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Ninety administrative and clinical key informants interviewed across 10 NYC area safety net hospitals. Transcribed interviews were coded by 3 investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods.

The psychophysics of comic: Effects of incongruity in causality and animacy.

PubMed

Parovel, Giulia; Guidi, Stefano

2015-07-01

According to several theories of humour (see Berger, 2012; Martin, 2007), incongruity - i.e., the presence of two incompatible meanings in the same situation - is a crucial condition for an event being evaluated as comical. The aim of this research was to test with psychophysical methods the role of incongruity in visual perception by manipulating the causal paradigm (Michotte, 1946/1963) to get a comic effect. We ran three experiments. In Experiment 1, we tested the role of speed ratio between the first and the second movement, and the effect of animacy cues (i.e. frog-like and jumping-like trajectories) in the second movement; in Experiment 2, we manipulated the temporal delay between the movements to explore the relationship between perceptual causal contingencies and comic impressions; in Experiment 3, we compared the strength of the comic impressions arising from incongruent trajectories based on animacy cues with those arising from incongruent trajectories not based on animacy cues (bouncing and rotating) in the second part of the causal event. General findings showed that the paradoxical juxtaposition of a living behaviour in the perceptual causal paradigm is a powerful factor in eliciting comic appreciations, coherently with the Bergsonian perspective in particular (Bergson, 2003), and with incongruity theories in general. Copyright © 2015 Elsevier B.V. All rights reserved.

The Influence of Mother Tongue and Gender on the Acquisition of English (L2). The Case of Afrikaans in Windhoek Schools, Namibia

ERIC Educational Resources Information Center

van Wyk, Jacolynn; Mostert, Maria Louise

2016-01-01

This study investigated the effect of mother tongue instruction and gender on second language acquisition using a causal-comparative quantitative research design. The two distinguishing groups compared were (i) learners that were taught in their mother tongue (Afrikaans) and (ii) learners that were not taught in their mother tongue but in English,…

Comparing the Self-Efficacy of Dual Enrollment Students Taking Classes at the High School, at the College, and Online

ERIC Educational Resources Information Center

Wallace, Tyler Lewis

2017-01-01

This quantitative causal comparative study investigated how the modality of course content delivery impacts the self-efficacy of dual enrollment students. The problem was that it is unclear how the benefits of dual enrollment impact different student groups based on the location of the course. The purpose was to verify existing research linking…

The Influence of Local Politics on Educational Decisions

ERIC Educational Resources Information Center

Bigham, Gary; Ray, Jan

2012-01-01

This ex post facto, causal-comparative research study examined student reading performance data within a school district before and after a school district-wide decision to alter the reading curriculum in response to local political pressure from parents. Data analysis revealed that test scores dropped to a significantly lower level, especially…

The Effects of Write Score Formative Assessment on Student Achievement

ERIC Educational Resources Information Center

Fox, Janice M.

2013-01-01

In an "ex post facto" causal-comparative research design, this study investigated the effectiveness of a formative writing assessment program, Write Score, on increasing student writing achievement. Tennessee Comprehensive Assessment Program (TCAP) reading language arts and writing scores from 2012 were utilized for this study. The…

The Importance of Qualitative Research for Causal Explanation in Education

ERIC Educational Resources Information Center

Maxwell, Joseph A.

2012-01-01

The concept of causation has long been controversial in qualitative research, and many qualitative researchers have rejected causal explanation as incompatible with an interpretivist or constructivist approach. This rejection conflates causation with the positivist "theory" of causation, and ignores an alternative understanding of causation,…

Chronometric Electrical Stimulation of Right Inferior Frontal Cortex Increases Motor Braking

PubMed Central

Conner, Christopher R.; Aron, Adam R.; Tandon, Nitin

2013-01-01

The right inferior frontal cortex (rIFC) is important for stopping responses. Recent research shows that it is also activated when response emission is slowed down when stopping is anticipated. This suggests that rIFC also functions as a goal-driven brake. Here, we investigated the causal role of rIFC in goal-driven braking by using computer-controlled, event-related (chronometric), direct electrical stimulation (DES). We compared the effects of rIFC stimulation on trials in which responses were made in the presence versus absence of a stopping-goal (“Maybe Stop” [MS] vs “No Stop” [NS]). We show that DES of rIFC slowed down responses (compared with control-site stimulation) and that rIFC stimulation induced more slowing when motor braking was required (MS) compared with when it was not (NS). Our results strongly support a causal role of a rIFC-based network in inhibitory motor control. Importantly, the results extend this causal role beyond externally driven stopping to goal-driven inhibitory control, which is a richer model of human self-control. These results also provide the first demonstration of double-blind chronometric DES of human prefrontal cortex, and suggest that—in the case of rIFC—this could lead to augmentation of motor braking. PMID:24336725

Inferential reasoning by exclusion in children (Homo sapiens).

PubMed

Hill, Andrew; Collier-Baker, Emma; Suddendorf, Thomas

2012-08-01

The cups task is the most widely adopted forced-choice paradigm for comparative studies of inferential reasoning by exclusion. In this task, subjects are presented with two cups, one of which has been surreptitiously baited. When the empty cup is shaken or its interior shown, it is possible to infer by exclusion that the alternative cup contains the reward. The present study extends the existing body of comparative work to include human children (Homo sapiens). Like chimpanzees (Pan troglodytes) that were tested with the same equipment and near-identical procedures, children aged three to five made apparent inferences using both visual and auditory information, although the youngest children showed the least-developed ability in the auditory modality. However, unlike chimpanzees, children of all ages used causally irrelevant information in a control test designed to examine the possibility that their apparent auditory inferences were the product of contingency learning (the duplicate cups test). Nevertheless, the children's ability to reason by exclusion was corroborated by their performance on a novel verbal disjunctive syllogism test, and we found preliminary evidence consistent with the suggestion that children used their causal-logical understanding to reason by exclusion in the cups task, but subsequently treated the duplicate cups information as symbolic or communicative, rather than causal. Implications for future comparative research are discussed. 2012 APA, all rights reserved

When Work is Related to Disease, What Establishes Evidence for a Causal Relation?

PubMed

Verbeek, Jos

2012-06-01

Establishing a causal relationship between factors at work and disease is difficult for occupational physicians and researchers. This paper seeks to provide arguments for the judgement of evidence of causality in observational studies that relate work factors to disease. I derived criteria for the judgement of evidence of causality from the following sources: the criteria list of Hill, the approach by Rothman, the methods used by International Agency for Research on Cancer (IARC), and methods used by epidemiologists. The criteria are applied to two cases of putative occupational diseases; breast cancer caused by shift work and aerotoxic syndrome. Only three of the Hill criteria can be applied to an actual study. Rothman stresses the importance of confounding and alternative explanations than the putative cause. IARC closely follows Hill, but they also incorporate other than epidemiological evidence. Applied to shift work and breast cancer, these results have found moderate evidence for a causal relationship, but applied to the aerotoxic syndrome, there is an absence of evidence of causality. There are no ready to use algorithms for judgement of evidence of causality. Criteria from different sources lead to similar results and can make a conclusion of causality more or less likely.

When Work is Related to Disease, What Establishes Evidence for a Causal Relation?

PubMed Central

2012-01-01

Establishing a causal relationship between factors at work and disease is difficult for occupational physicians and researchers. This paper seeks to provide arguments for the judgement of evidence of causality in observational studies that relate work factors to disease. I derived criteria for the judgement of evidence of causality from the following sources: the criteria list of Hill, the approach by Rothman, the methods used by International Agency for Research on Cancer (IARC), and methods used by epidemiologists. The criteria are applied to two cases of putative occupational diseases; breast cancer caused by shift work and aerotoxic syndrome. Only three of the Hill criteria can be applied to an actual study. Rothman stresses the importance of confounding and alternative explanations than the putative cause. IARC closely follows Hill, but they also incorporate other than epidemiological evidence. Applied to shift work and breast cancer, these results have found moderate evidence for a causal relationship, but applied to the aerotoxic syndrome, there is an absence of evidence of causality. There are no ready to use algorithms for judgement of evidence of causality. Criteria from different sources lead to similar results and can make a conclusion of causality more or less likely. PMID:22993715

New Insights into Signed Path Coefficient Granger Causality Analysis

PubMed Central

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of “signed path coefficient Granger causality,” a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an “excitatory” or “inhibitory” influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation. PMID:27833547

[Causal analysis approaches in epidemiology].

PubMed

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Expert Causal Reasoning and Explanation.

ERIC Educational Resources Information Center

Kuipers, Benjamin

The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

Simplifying Causal Complexity: How Interactions between Modes of Causal Induction and Information Availability Lead to Heuristic-Driven Reasoning

ERIC Educational Resources Information Center

Grotzer, Tina A.; Tutwiler, M. Shane

2014-01-01

This article considers a set of well-researched default assumptions that people make in reasoning about complex causality and argues that, in part, they result from the forms of causal induction that we engage in and the type of information available in complex environments. It considers how information often falls outside our attentional frame…

Causal reasoning with forces

PubMed Central

Wolff, Phillip; Barbey, Aron K.

2015-01-01

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

Causal pathways linking Farm to School to childhood obesity prevention.

PubMed

Joshi, Anupama; Ratcliffe, Michelle M

2012-08-01

Farm to School programs are rapidly gaining attention as a potential strategy for preventing childhood obesity; however, the causal linkages between Farm to School activities and health outcomes are not well documented. To capitalize on the increased interest in and momentum for Farm to School, researchers and practitioners need to move from developing and implementing evidence informed programs and policies to ones that are evidence-based. The purpose of this article is to outline a framework for facilitating an evidence base for Farm to School programs and policies through a systematic and coordinated approach. Employing the concepts of causal pathways, the authors introduce a proposed framework for organizing and systematically testing out multiple hypotheses (or potential causal links) for how, why, and under what conditions Farm to School Inputs and Activities may result in what Outputs, Effects, and Impacts. Using the causal pathways framework may help develop and test competing hypotheses, identify multicausality, strength, and interactions of causes, and discern the difference between catalysts and causes. In this article, we introduce causal pathways, present menus of potential independent and dependent variables from which to create and test causal pathways linking Farm to School interventions and their role in preventing childhood obesity, discuss their applicability to Farm to School research and practice, and outline proposed next steps for developing a coordinated research framework for Farm to School programs.

Learning to Understand the Forms of Causality Implicit in Scientifically Accepted Explanations

ERIC Educational Resources Information Center

Grotzer, Tina A.

2003-01-01

Considerable research illuminates the development of causal understanding. However, the research base is hardly a coherent whole. Some is based in research on children's understanding of particular science concepts. Some grows out of social psychology and considers how one attributes intentions and behaviors. Some comes from the developmental…

Theory and Contrastive Explanation in Ethnography

ERIC Educational Resources Information Center

Lichterman, Paul; Reed, Isaac Ariail

2015-01-01

We propose three interlinked ways that theory helps researchers build causal claims from ethnographic research. First, theory guides the casing and re-casing of a topic of study. Second, theoretical work helps craft a clear causal question via the construction of a contrast space of the topic of investigation. Third, the researcher uses theory to…

Volcanic Experimentation: Using Mentos and Soda to Teach Causal Research in a Marketing Research Class

ERIC Educational Resources Information Center

Love, Edwin; Stelling, Pete

2012-01-01

The reaction that occurs when Mentos are added to bottled soft drinks has become a staple demonstration in earth science courses to explain how volcanoes erupt. This paper presents how this engaging exercise can be used in a marketing research course to provide hands-on experience with problem formation, hypothesis testing, and causal research. A…

Triangulation in aetiological epidemiology.

PubMed

Lawlor, Debbie A; Tilling, Kate; Davey Smith, George

2016-12-01

Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association.

Attaining Reading Success through School-Wide and Content-Based Literacy

ERIC Educational Resources Information Center

Joseph Watts, Martha

2013-01-01

Reading performance among Grade 11 students has been low in the local school district under study. Schools within the boundaries of that setting have implemented research-based interventions to curb this problem of poor reading performance. A quasi-experimental, causal-comparative study was conducted to investigate the effect of Marzano's…

The Relationship between Teaching Presence and Student Course Outcomes in an Online International Population

ERIC Educational Resources Information Center

Wendt, Jillian; Courduff, Jennifer

2018-01-01

A causal comparative research design was utilized in this study to examine the relationship between international students' perceptions of teacher presence in the online learning environment and students' achievement as measured by end of course grades. Spearman's analysis indicated no statistically significant correlation between the composite…

Influence of Metacognitive Awareness on Motivation and Performance in High School Precalculus

ERIC Educational Resources Information Center

Reed, Jane Frazier

2015-01-01

Students who actively engage in metacognitive thinking and self-regulation and are self-motivating appear to be more successful than those who take a more passive role in learning. This causal comparative research study explored whether increasing metacognitive awareness through participating in metacognitive surveys outside of class improved…

Perceptions of Collegiate Student-Athletes' Programming Needs Based on Gender, Ethnicity, and Academic Class

ERIC Educational Resources Information Center

Arvan, April Anita

2010-01-01

Student-athletes are often disengaged in campus programming due to their academic and athletic commitments. Previous research explored various facets of student-athlete development, particularly academic development in relation to NCAA Division I student-athletes. The purpose of this quantitative, causal-comparative study was to determine…

The Effect of Single Gender Instruction on Eighth Grade Students' Mathematics Achievement

ERIC Educational Resources Information Center

Hammel, David Michael

2013-01-01

In the research study, this investigator utilized a non-experimental, causal-comparative design (ex post facto) with archival data to determine the real impact single gender instruction had on eighth grade students' mathematics achievement. The purpose of this study was to quantitatively analyze the benefits of single gender mathematics…

The Agent-based Approach: A New Direction for Computational Models of Development.

ERIC Educational Resources Information Center

Schlesinger, Matthew; Parisi, Domenico

2001-01-01

Introduces the concepts of online and offline sampling and highlights the role of online sampling in agent-based models of learning and development. Compares the strengths of each approach for modeling particular developmental phenomena and research questions. Describes a recent agent-based model of infant causal perception. Discusses limitations…

Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA

ERIC Educational Resources Information Center

Braumoeller, Bear F.

2017-01-01

Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal complexity, especially for scholars with small- and medium-"N" data sets. This research note explores two key assumptions in fsQCA's methodology for testing for necessary and sufficient…

Small Learning Communities Sense of Belonging to Reach At-Risk Students of Promise

ERIC Educational Resources Information Center

Hackney, Debbie

2011-01-01

The research design is a quantitative causal comparative method. The Florida Comprehensive Assessment Test (FCAT) which measures student scores included assessments in mathematics and reading. The design study called for an examination of how type of small learning community (SLC) or the type non-SLC high school environment affected student…

Student Perceptions of Teacher Characteristics on Math Achievement for Middle School African American Students

ERIC Educational Resources Information Center

Clayton, Otis, Jr.

2013-01-01

This causal-comparative research explored how African American students' perceptions of their math teachers affected their academic performance on the Math Tennessee Comprehensive Assessment Program (TCAP) Test during 2009-2010 academic year. When considering possible measures of teacher effectiveness in K-12 education, it can be argued that…

Methodological Limitations of the Application of Expert Systems Methodology in Reading.

ERIC Educational Resources Information Center

Willson, Victor L.

Methodological deficiencies inherent in expert-novice reading research make it impossible to draw inferences about curriculum change. First, comparisons of intact groups are often used as a basis for making causal inferences about how observed characteristics affect behaviors. While comparing different groups is not by itself a useless activity,…

The Relationship between Student Achievement and Multilingualism: A Quantitative Causal-Comparative Study

ERIC Educational Resources Information Center

Thomson, Jennifer Barbara

2010-01-01

Student performance in basic math and reading skills in the United States trails behind other developed countries, providing the rationale for more research to determine how performance might be improved. Following evidence to conclude that multilingualism enhances cognitive, neuro-linguistic and meta-linguistic development, it is proposed that…

Ethnic Differences in Completion Rates as a Function of School Size in Texas High Schools

ERIC Educational Resources Information Center

Fitzgerald, Kim; Gordon, Teandra; Canty, Antoinette; Stitt, Ruth E.; Onwuegbuzie, Anthony J.; Frels, Rebecca K.

2013-01-01

The purpose of this study was to investigate differences in high school completion rates among White, African American, and Hispanic students enrolled in different school sizes--small, medium, and large. For this causal-comparative research design, this study utilized archival data from the Texas Education Association's Academic Excellence…

Burnout in Relation to Specific Contributing Factors and Health Outcomes among Nurses: A Systematic Review

PubMed Central

Khamisa, Natasha; Peltzer, Karl; Oldenburg, Brian

2013-01-01

Nurses have been found to experience higher levels of stress-related burnout compared to other health care professionals. Despite studies showing that both job satisfaction and burnout are effects of exposure to stressful working environments, leading to poor health among nurses, little is known about the causal nature and direction of these relationships. The aim of this systematic review is to identify published research that has formally investigated relationships between these variables. Six databases (including CINAHL, COCHRANE, EMBASE, MEDLINE, PROQUEST and PsyINFO) were searched for combinations of keywords, a manual search was conducted and an independent reviewer was asked to cross validate all the electronically identified articles. Of the eighty five articles that were identified from these databases, twenty one articles were excluded based on exclusion criteria; hence, a total of seventy articles were included in the study sample. The majority of identified studies exploring two and three way relationships (n = 63) were conducted in developed countries. Existing research includes predominantly cross-sectional studies (n = 68) with only a few longitudinal studies (n = 2); hence, the evidence base for causality is still very limited. Despite minimal availability of research concerning the small number of studies to investigate the relationships between work-related stress, burnout, job satisfaction and the general health of nurses, this review has identified some contradictory evidence for the role of job satisfaction. This emphasizes the need for further research towards understanding causality. PMID:23727902

Causal Inference for fMRI Time Series Data with Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat.

PubMed

Sobel, Michael E; Lindquist, Martin A

2014-07-01

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects (Rosenbaum, 1984), here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured "noise covariates" , using a linear mixed model to estimate the effects and the systematic error. Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we re-analyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.

Playing the electric light orchestra—how electrical stimulation of visual cortex elucidates the neural basis of perception

PubMed Central

Cicmil, Nela; Krug, Kristine

2015-01-01

Vision research has the potential to reveal fundamental mechanisms underlying sensory experience. Causal experimental approaches, such as electrical microstimulation, provide a unique opportunity to test the direct contributions of visual cortical neurons to perception and behaviour. But in spite of their importance, causal methods constitute a minority of the experiments used to investigate the visual cortex to date. We reconsider the function and organization of visual cortex according to results obtained from stimulation techniques, with a special emphasis on electrical stimulation of small groups of cells in awake subjects who can report their visual experience. We compare findings from humans and monkeys, striate and extrastriate cortex, and superficial versus deep cortical layers, and identify a number of revealing gaps in the ‘causal map′ of visual cortex. Integrating results from different methods and species, we provide a critical overview of the ways in which causal approaches have been used to further our understanding of circuitry, plasticity and information integration in visual cortex. Electrical stimulation not only elucidates the contributions of different visual areas to perception, but also contributes to our understanding of neuronal mechanisms underlying memory, attention and decision-making. PMID:26240421

Category transfer in sequential causal learning: the unbroken mechanism hypothesis.

PubMed

Hagmayer, York; Meder, Björn; von Sydow, Momme; Waldmann, Michael R

2011-07-01

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. Copyright © 2011 Cognitive Science Society, Inc.

Children's Counterfactual Reasoning About Causally Overdetermined Events.

PubMed

Nyhout, Angela; Henke, Lena; Ganea, Patricia A

2017-08-07

In two experiments, one hundred and sixty-two 6- to 8-year-olds were asked to reason counterfactually about events with different causal structures. All events involved overdetermined outcomes in which two different causal events led to the same outcome. In Experiment 1, children heard stories with either an ambiguous causal relation between events or causally unrelated events. Children in the causally unrelated version performed better than chance and better than those in the ambiguous condition. In Experiment 2, children heard stories in which antecedent events were causally connected or causally disconnected. Eight-year-olds performed above chance in both conditions, whereas 6-year-olds performed above chance only in the connected condition. This work provides the first evidence that children can reason counterfactually in causally overdetermined contexts by age 8. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Blaming Leaders for Organizational Accidents: Proxy Logic in Collective- versus Individual-Agency Cultures

ERIC Educational Resources Information Center

Zemba, Yuriko; Young, Maia J.; Morris, Michael W.

2006-01-01

The current research investigates whether observers blame leaders for organizational accidents even when these managers are known to be causally uninvolved. Past research finds that the public blames managers for organizational harm if the managers are perceived to have personally played a causal role. The present research argues that East Asian…

Propensity Score Techniques and the Assessment of Measured Covariate Balance to Test Causal Associations in Psychological Research

ERIC Educational Resources Information Center

Harder, Valerie S.; Stuart, Elizabeth A.; Anthony, James C.

2010-01-01

There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelines are available to aid applied researchers in their understanding, use, and evaluation. In this study, the authors give an overview of available…

Regression Discontinuity Design: A Guide for Strengthening Causal Inference in HRD

ERIC Educational Resources Information Center

Chambers, Silvana

2016-01-01

Purpose: Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about…

Thinking Fast and Slow about Causality: Response to Palinkas

ERIC Educational Resources Information Center

Marsh, Jeanne C.

2014-01-01

Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…

Causal Inferences in the Campbellian Validity System

ERIC Educational Resources Information Center

Lund, Thorleif

2010-01-01

The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies.

PubMed

Meng, Xiang-He; Shen, Hui; Chen, Xiang-Ding; Xiao, Hong-Mei; Deng, Hong-Wen

2018-03-01

Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell's method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.

Effects of the Afterschool Program on Student Achievement of Students with Disabilities in a Rural Georgia Middle School

ERIC Educational Resources Information Center

Franklin, Constance

2017-01-01

The purpose of this quantitative nonexperimental causal comparative research study is to determine if there is a statistically significant difference in reading and math achievement as measured by the Georgia Criterion-Referenced Competency Test (CRCT) for sixth, seventh, and eighth grade students with disabilities (SWD) who attended the…

Investigating the Effects of the Academy of Reading Program on Middle School Reading Achievement

ERIC Educational Resources Information Center

Myers, Brenda Gail

2016-01-01

Using a quantitative ex post facto causal comparative research design, this study analyzed the effects of the Academy of Reading software program on students' reading achievement. Tennessee Comprehensive Assessment Program (TCAP) reading scale scores of students in the fourth, fifth, and sixth grades from 2013-2014 were utilized in this study. The…

The Impact of Professional Learning Communities on Urban Teachers and Their Students' Reading and Math Achievement

ERIC Educational Resources Information Center

Williams, Deborah Johnson

2011-01-01

This study followed a causal comparative research design that utilized mixed methods. This non-experimental investigation sought to identify potential cause and effect relationships between variations in achievement data among schools. The purpose of the study was to determine if urban students' reading and math achievement increased as a result…

A Causal-Comparative Analysis of the Effects of a Student Support Team (SST) Intervention Model at a Secondary School

ERIC Educational Resources Information Center

Johnson, Mid D.

2010-01-01

The purpose of this research was to identify and examine the effectiveness of a "Student Support Team" (SST) intervention model designed to increase the performance of struggling secondary students and to help them achieve prescribed state standards on the mathematics "Texas Assessment of Knowledge and Skills (TAKS)"…

Effect of Balanced Math Instruction on Math Performance of Grade 1 and Grade 2 English Language Learners

ERIC Educational Resources Information Center

Cavanaugh, Gary Scott

2017-01-01

Research affirmed that instructional strategies that promote English Language Learners' (ELLs) Academic Language Proficiency (ALP) are essential in the primary grades for ELLs to succeed in school. This quantitative causal-comparative study relied on the premise of Vygotsky's sociocultural theory and addressed to what extent Balanced Math…

The Relationship between Early Literacy Assessment and First-Grade Reading Achievement for Native American Students

ERIC Educational Resources Information Center

Coats-Kitsopoulos, Gloria Jean

2011-01-01

The purpose of this study was to determine the relationship between the Dynamic Indicators of Basic Early Literacy Skills (DIBELS), the Reading Recovery Observation Survey (RROS) early reading sub-tests, and the reading achievement of Native American first-graders as measured by the Stanford 10. A causal-comparative correlation research design…

On-Time Graduation of Career and Technical Education Concentrators in Arizona

ERIC Educational Resources Information Center

Jaime, Laura Eileen

2017-01-01

The purpose of this quantitative causal-comparative study was to examine the effect that Career and Technical Education (CTE) concentrators, non-CTE concentrators and academic concentrators have on the on-time graduation of 1035 high school students in 7 high schools in Arizona for the 2015--2016 school year. There were three research questions…

Weighing in on Education: A Study of Childhood Obesity and Student Achievement

ERIC Educational Resources Information Center

Guindon, John R., Sr.

2014-01-01

This quantitative causal comparative study looked to see if there was a relationship between childhood obesity and student achievement. Because of the many conflicting results in the research available, it was not known if there was a relationship between childhood obesity and student achievement among inner-city middle school students in a school…

An Examination of Adjunct Faculty Job Satisfaction and Loyalty in Christian Higher Education

ERIC Educational Resources Information Center

Couch, Jeremy J.

2014-01-01

In order to address the deficiency of research regarding the job attitudes of adjunct faculty members in Christian higher education, a quantitative causal-comparative study was conducted for the purpose of examining the influence of six extrinsic and three intrinsic variables on the job satisfaction and loyalty of 388 adjuncts teaching at seven…

Minority Dropouts: Do Rural, Urban, and Suburban Administrators Perceive Causes Affecting Minorities as Priority Items?

ERIC Educational Resources Information Center

Bull, Kay S.; And Others

This study compared the perceptions of a national sample of urban, suburban, and rural administrators (N=891, a 72% response) about minority dropout indicators to what current research literature identifies as highly-ranked causal variables related to minority dropout rates. The literature review identified the following causes of dropping out,…

Impact of Curriculum Training on State-Funded Prekindergarten Teachers' Knowledge, Beliefs, and Practices

ERIC Educational Resources Information Center

Breffni, Lorraine

2011-01-01

The number of state-funded prekindergarten programs continues to grow in the United States. The quality of these early childhood programs, however, often depends on the type of professional development provided. In this investigative study, an experimental pre-post causal-comparative research design was employed to evaluate the impact of an 8-week…

ACSPRI 2014 4th International Social Science Methodology Conference Report

DTIC Science & Technology

2015-04-01

Validity, trustworthiness and rigour: quality and the idea of qualitative research . Journal of Advanced Nursing, 304-310. Spencer, L., Ritchie, J...increasing data quality; the Total Survey Error framework; multi-modal on-line surveying, quality frameworks for assessing qualitative research ; and...provided an overview of the current perspectives on causal claims in qualitative research . Three approaches to generating plausible causal

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Research Dilemmas with Behavioral Big Data.

PubMed

Shmueli, Galit

2017-06-01

Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of researchers in social science and management fields acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data. Behavioral researchers using BBD face not only methodological and technical challenges but also ethical and moral dilemmas. In this article, we discuss several dilemmas, challenges, and trade-offs related to acquiring and analyzing BBD for causal behavioral research.

Earthquake prediction: the interaction of public policy and science.

PubMed Central

Jones, L M

1996-01-01

Earthquake prediction research has searched for both informational phenomena, those that provide information about earthquake hazards useful to the public, and causal phenomena, causally related to the physical processes governing failure on a fault, to improve our understanding of those processes. Neither informational nor causal phenomena are a subset of the other. I propose a classification of potential earthquake predictors of informational, causal, and predictive phenomena, where predictors are causal phenomena that provide more accurate assessments of the earthquake hazard than can be gotten from assuming a random distribution. Achieving higher, more accurate probabilities than a random distribution requires much more information about the precursor than just that it is causally related to the earthquake. PMID:11607656

Cause and Event: Supporting Causal Claims through Logistic Models

ERIC Educational Resources Information Center

O'Connell, Ann A.; Gray, DeLeon L.

2011-01-01

Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify…

Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

ERIC Educational Resources Information Center

von Eye, Alexander; Wiedermann, Wolfgang

2015-01-01

Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…

Causal Connections in Beginning Reading: The Importance of Rhyme.

ERIC Educational Resources Information Center

Goswami, Usha

2000-01-01

Discusses the implications of Goswami and Bryant's (1990) theory about important causal connections in reading for classroom teaching, and reviews more recent "rhyme and analogy" research within this framework. Discusses new research on the nature of the English spelling system and the representation of linguistic knowledge. Emphasizes the…

Resources, Instruction, and Research

ERIC Educational Resources Information Center

Cohen, David K.; Raudenbush, Stephen W.; Ball, Deborah Loewenberg

2003-01-01

Many researchers who study the relations between school resources and student achievement have worked from a causal model, which typically is implicit. In this model, some resource or set of resources is the causal variable and student achievement is the outcome. In a few recent, more nuanced versions, resource effects depend on intervening…

Learning What Works in Sensory Disabilities: Establishing Causal Inference

ERIC Educational Resources Information Center

Cooney, John B.; Young, John, III; Luckner, John L.; Ferrell, Kay Alicyn

2015-01-01

This article is intended to assist teachers and researchers in designing studies that examine the efficacy of a particular intervention or strategy with students with sensory disabilities. Ten research designs that can establish causal inference (the ability to attribute any effects to the intervention) with and without randomization are discussed.

Situation models and memory: the effects of temporal and causal information on recall sequence.

PubMed

Brownstein, Aaron L; Read, Stephen J

2007-10-01

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.

Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.

PubMed

Spertus, Jacob V; Normand, Sharon-Lise T

2018-04-23

High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Healthcare Systems in Comparative Perspective: Classification, Convergence, Institutions, Inequalities, and Five Missed Turns

PubMed Central

Beckfield, Jason; Olafsdottir, Sigrun; Sosnaud, Benjamin

2017-01-01

This essay reviews and evaluates recent comparative social science scholarship on healthcare systems. We focus on four of the strongest themes in current research: (1) the development of typologies of healthcare systems, (2) assessment of convergence among healthcare systems, (3) problematization of the shifting boundaries of healthcare systems, and (4) the relationship between healthcare systems and social inequalities. Our discussion seeks to highlight the central debates that animate current scholarship and identify unresolved questions and new opportunities for research. We also identify five currents in contemporary sociology that have not been incorporated as deeply as they might into research on healthcare systems. These five “missed turns” include an emphasis on social relations, culture, postnational theory, institutions, and causal mechanisms. We conclude by highlighting some key challenges for comparative research on healthcare systems. PMID:28769148

Causality networks from multivariate time series and application to epilepsy.

PubMed

Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2015-08-01

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

Research designs and making causal inferences from health care studies.

PubMed

Flannelly, Kevin J; Jankowski, Katherine R B

2014-01-01

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.

Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation

ERIC Educational Resources Information Center

Austin, Peter C.

2012-01-01

Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one…

Agency, time, and causality

PubMed Central

Widlok, Thomas

2014-01-01

Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683

Undergraduates' Ability to Recognize Correlational and Causal Language before and after Explicit Instruction

ERIC Educational Resources Information Center

Mueller, Jon F.; Coon, Heather M.

2013-01-01

The ability to distinguish between correlational and causal claims is core knowledge for scientific literacy. News reports of scientific research prominently feature these claims. Thus, this knowledge has significant real-world application, and distinguishing among claims is critical to making sense of the reported research. We constructed an…

Prediction versus aetiology: common pitfalls and how to avoid them.

PubMed

van Diepen, Merel; Ramspek, Chava L; Jager, Kitty J; Zoccali, Carmine; Dekker, Friedo W

2017-04-01

Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand.In both scientific and clinical practice, however, the two are often confused, resulting in poor-quality publications with limited interpretability and applicability. A major problem is the frequently encountered aetiological interpretation of prediction results, where individual variables in a prediction model are attributed causal meaning. This article stresses the differences in use and interpretation of aetiological and prediction studies, and gives examples of common pitfalls. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

NSF Workshop Report: Discovering General Principles of Nervous System Organization by Comparing Brain Maps across Species

PubMed Central

Striedter, Georg F.; Belgard, T. Grant; Chen, Chun-Chun; Davis, Fred P.; Finlay, Barbara L.; Güntürkün, Onur; Hale, Melina E.; Harris, Julie A.; Hecht, Erin E.; Hof, Patrick R.; Hofmann, Hans A.; Holland, Linda Z.; Iwaniuk, Andrew N.; Jarvis, Erich D.; Karten, Harvey J.; Katz, Paul S.; Kristan, William B.; Macagno, Eduardo R.; Mitra, Partha P.; Moroz, Leonid L.; Preuss, Todd M.; Ragsdale, Clifton W.; Sherwood, Chet C.; Stevens, Charles F.; Stüttgen, Maik C.; Tsumoto, Tadaharu; Wilczynski, Walter

2014-01-01

Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system ‘maps’ comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of ‘reference species’ to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution. PMID:24603302

Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

A Study of the Relationship between Multicultural Teaching Training Methods and Teacher Attitudes about Diversity in Rural Schools

ERIC Educational Resources Information Center

Gray, Russell E.

2010-01-01

The lack of research about the efficacy of the three primary methods for training teachers to work with students from diverse cultures makes it difficult for universities to provide the proper training. The following quantitative, non-experimental, causal-comparative, ex-post facto study gathered and analyzed data concerning the efficacy of the…

Land and Discover! A Case Study Investigating the Cultural Context of Plagiarism

ERIC Educational Resources Information Center

Handa, Neera; Power, Clare

2005-01-01

Despite a growing body of evidence, the common causal factors of plagiarism among international students are still widely seen to be poor language skills or a lack of academic integrity on the part of the students. This research uses the experiences of a particular cohort of students to explore these assumptions. It investigates and compares the…

The Effect of Face-to-Face versus Online Pedagogy-Based Professional Development on Student Learning Outcomes

ERIC Educational Resources Information Center

Rock, Heidi Marie

2017-01-01

The purpose of this quantitative retrospective causal-comparative study was to determine to what extent the form of professional development (face-to-face or online) or the level of instruction (elementary or high school) has on classroom teaching practices as measured by student learning outcomes. The first research question sought to determine…

Mathematics Achievement with Digital Game-Based Learning in High School Algebra 1 Classes

ERIC Educational Resources Information Center

Ferguson, Terri Lynn Kurley

2014-01-01

This study examined the impact of digital game-based learning (DGBL) on mathematics achievement in a rural high school setting in North Carolina. A causal comparative research design was used in this study to collect data to determine the effectiveness of DGBL in high school Algebra 1 classes. Data were collected from the North Carolina…

[Clinical research. XIII. Research design contribution in the structured revision of an article].

PubMed

Talavera, Juan O; Rivas-Ruiz, Rodolfo

2013-01-01

The quality of information obtained in accordance to research design is integrated to the revision structured in relation to the causality model, used in the article "Reduction in the Incidence of Nosocomial Pneumonia Poststroke by Using the 'Turn-mob' Program", which corresponds to a clinical trial design. Points to identify and analyze are ethical issues in order to safeguard the security and respect for patients, randomization that seek to create basal homogeneous groups, subjects with the same probability of receiving any of the maneuvers in comparison, with the same pre maneuver probability of adherence, and which facilitate the blinding of outcome measurement and the distribution between groups of subjects with the same probability of leaving the study for reasons beyond the maneuvers. Other aspects are the relativity of comparison, the blinding of the maneuver, the parallel application of comparative maneuver, early stopping, and analysis according to the degree of adherence. The analysis in accordance with the design is complementary, since it is done based on the architectural model of causality, and the statistical and clinical relevance consideration.

The relationship between the belief in a genetic cause for breast cancer and bilateral mastectomy.

PubMed

Petrie, Keith J; Myrtveit, Solbjørg Makalani; Partridge, Ann H; Stephens, Melika; Stanton, Annette L

2015-05-01

Most women develop causal beliefs following diagnosis with breast cancer and these beliefs can guide decisions around their care and management. Bilateral mastectomy rates are increasing, although the benefits of this surgery are only established in a small percentage of women. In this study we investigated the relationship between causal beliefs and the decision to have a bilateral mastectomy. Women (N = 2,269) from the Army of Women's breast cancer research registry completed an online survey. Women were asked what they believed caused their cancer and responses were coded into 8 causal categories. Participants were also asked about the type of surgery they underwent following their breast cancer diagnosis. The odds ratios for having a double mastectomy were calculated for each causal category using random/bad luck as a referent category. Hormonal factors (22%) and genetics (19%) were the most common causal belief, followed by don't know (19%), environmental toxins (11%), negative emotions (9%), poor health behavior (8%), other (6%) and random/bad luck (6%). Compared with the referent category, the odds ratio of having a bilateral mastectomy was significantly higher in both the genetics and hormonal causal belief groups (OR = 2.36, 95% CI [1.38, 4.02] and OR = 1.98, 95% CI [1.16, 3.38], respectively). Beliefs in a genetic cause for breast cancer are common and are associated with high rates of bilateral mastectomy. This is despite evidence that the actual genetic contribution to breast cancer is much lower than perceived and that bilateral mastectomy is, in most cases, unlikely to improve survival. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

PubMed

Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

2017-05-01

This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO 2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.

Causal inference from observational data.

PubMed

Listl, Stefan; Jürges, Hendrik; Watt, Richard G

2016-10-01

Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Causal attribution for success and failure in mathematics among MDAB pre-diploma students

NASA Astrophysics Data System (ADS)

Maidinsah, Hamidah; Embong, Rokiah; Wahab, Zubaidah Abd

2014-07-01

The Program Mengubah Destini Anak Bangsa (MDAB) is a pre-diploma programme catering to SPM school leavers who do not meet the minimum requirement to enter any of UiTM diploma programmes. The study aims to evaluate the perceptions of MDAB students toward the main causal attribution factors underlying students' success and failure in mathematics. Research sample comprised of 482 students from five UiTM branch campuses. Research instrument used was a set of GALUS questionnaire consisting of 36 items based on the Weiner Attribution Theory. Four causal attributions factors for success and failures evaluated are ability, effort, question difficulty and environment. GALUS reliability index was 0.93. The research found that effort appears to be the main causal attribution factor in students' success and failure in mathematics, followed by environment, question difficulty and ability. High achiever students strongly agree that the ability factor influenced their success while low achiever students strongly agree that all attributing factors influenced their failures in mathematics.

Architecture of Explanatory Inference in the Human Prefrontal Cortex

PubMed Central

Barbey, Aron K.; Patterson, Richard

2011-01-01

Causal reasoning is a ubiquitous feature of human cognition. We continuously seek to understand, at least implicitly and often explicitly, the causal scenarios in which we live, so that we may anticipate what will come next, plan a potential response and envision its outcome, decide among possible courses of action in light of their probable outcomes, make midstream adjustments in our goal-related activities as our situation changes, and so on. A considerable body of research shows that the lateral prefrontal cortex (PFC) is crucial for causal reasoning, but also that there are significant differences in the manner in which ventrolateral PFC, dorsolateral PFC, and anterolateral PFC support causal reasoning. We propose, on the basis of research on the evolution, architecture, and functional organization of the lateral PFC, a general framework for understanding its roles in the many and varied sorts of causal reasoning carried out by human beings. Specifically, the ventrolateral PFC supports the generation of basic causal explanations and inferences; dorsolateral PFC supports the evaluation of these scenarios in light of some given normative standard (e.g., of plausibility or correctness in light of real or imagined causal interventions); and anterolateral PFC supports explanation and inference at an even higher level of complexity, coordinating the processes of generation and evaluation with further cognitive processes, and especially with computations of hedonic value and emotional implications of possible behavioral scenarios – considerations that are often critical both for understanding situations causally and for deciding about our own courses of action. PMID:21845182

The Russo-Williamson Theses in the social sciences: causal inference drawing on two types of evidence.

PubMed

Claveau, François

2012-12-01

This article examines two theses formulated by Russo and Williamson (2007) in their study of causal inference in the health sciences. The two theses are assessed against evidence from a specific case in the social sciences, i.e., research on the institutional determinants of the aggregate unemployment rate. The first Russo-Williamson Thesis is that a causal claim can only be established when it is jointly supported by difference-making and mechanistic evidence. This thesis is shown not to hold. While researchers in my case study draw extensively on both types of evidence, one causal claim out of the three analyzed is established even though it is exclusively supported by mechanistic evidence. The second Russo-Williamson Thesis is that standard accounts of causality fail to handle the dualist epistemology highlighted in the first Thesis. I argue that a counterfactual-manipulationist account of causality--which is endorsed by many philosophers as well as many social scientists--can perfectly make sense of the typical strategy in my case study to draw on both difference-making and mechanistic evidence; it is just an instance of the common strategy of increasing evidential variety. Copyright © 2012 Elsevier Ltd. All rights reserved.

Moving Matters: The Causal Effect of Moving Schools on Student Performance. Working Paper #01-15

ERIC Educational Resources Information Center

Schwartz, Amy Ellen; Stiefel, Leanna; Cordes, Sarah A.

2015-01-01

The majority of existing research on mobility indicates that students do worse in the year of a school move. This research, however, has been unsuccessful in isolating the causal effects of mobility and often fails to distinguish the heterogeneous impacts of moves, conflating structural moves (mandated by a school's terminal grade) and…

City Connects: Building an Argument for Effects on Student Achievement with a Quasi-Experimental Design

ERIC Educational Resources Information Center

Walsh, Mary; Raczek, Anastasia; Sibley, Erin; Lee-St. John, Terrence; An, Chen; Akbayin, Bercem; Dearing, Eric; Foley, Claire

2015-01-01

While randomized experimental designs are the gold standard in education research concerned with causal inference, non-experimental designs are ubiquitous. For researchers who work with non-experimental data and are no less concerned for causal inference, the major problem is potential omitted variable bias. In this presentation, the authors…

Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

ERIC Educational Resources Information Center

Riegg, Stephanie K.

2008-01-01

This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…

A Causal Model of Career Development and Quality of Life of College Students with Disabilities

ERIC Educational Resources Information Center

Chun, Jina

2017-01-01

Researchers have assumed that social cognitive factors play significant roles in the career development of transition youth and young adults with disabilities and those without disabilities. However, research on the influence of the career decision-making process as a primary causal agent in one's psychosocial outcomes such as perceived level of…

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

PubMed

Cox, Louis Anthony Tony; Popken, Douglas A

2015-03-01

Between 2000 and 2010, air pollutant levels in counties throughout the United States changed significantly, with fine particulate matter (PM2.5) declining over 30% in some counties and ozone (O3) exhibiting large variations from year to year. This history provides an opportunity to compare county-level changes in average annual ambient pollutant levels to corresponding changes in all-cause (AC) and cardiovascular disease (CVD) mortality rates over the course of a decade. Past studies have demonstrated associations and subsequently either interpreted associations causally or relied on subjective judgments to infer causation. This article applies more quantitative methods to assess causality. This article examines data from these "natural experiments" of changing pollutant levels for 483 counties in the 15 most populated US states using quantitative methods for causal hypothesis testing, such as conditional independence and Granger causality tests. We assessed whether changes in historical pollution levels helped to predict and explain changes in CVD and AC mortality rates. A causal relation between pollutant concentrations and AC or CVD mortality rates cannot be inferred from these historical data, although a statistical association between them is well supported. There were no significant positive associations between changes in PM2.5 or O3 levels and corresponding changes in disease mortality rates between 2000 and 2010, nor for shorter time intervals of 1 to 3 years. These findings suggest that predicted substantial human longevity benefits resulting from reducing PM2.5 and O3 may not occur or may be smaller than previously estimated. Our results highlight the potential for heterogeneity in air pollution health effects across regions, and the high potential value of accountability research comparing model-based predictions of health benefits from reducing air pollutants to historical records of what actually occurred. Copyright © 2015 Elsevier Inc. All rights reserved.

Improving Causal Inferences in Meta-analyses of Longitudinal Studies: Spanking as an Illustration.

PubMed

Larzelere, Robert E; Gunnoe, Marjorie Lindner; Ferguson, Christopher J

2018-05-24

To evaluate and improve the validity of causal inferences from meta-analyses of longitudinal studies, two adjustments for Time-1 outcome scores and a temporally backwards test are demonstrated. Causal inferences would be supported by robust results across both adjustment methods, distinct from results run backwards. A systematic strategy for evaluating potential confounds is also introduced. The methods are illustrated by assessing the impact of spanking on subsequent externalizing problems (child age: 18 months to 11 years). Significant results indicated a small risk or a small benefit of spanking, depending on the adjustment method. These meta-analytic methods are applicable for research on alternatives to spanking and other developmental science topics. The underlying principles can also improve causal inferences in individual studies. © 2018 Society for Research in Child Development.

Designing Better Scaffolding in Teaching Complex Systems with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na

Complex systems are an important topic in science education today, but they are usually difficult for secondary-level students to learn. Although graphic simulations have many advantages in teaching complex systems, scaffolding is a critical factor for effective learning. This dissertation study was conducted around two complementary research questions on scaffolding: (1) How can we chunk and sequence learning activities in teaching complex systems? (2) How can we help students make connections among system levels across learning activities (level bridging)? With a sample of 123 seventh-graders, this study employed a 3x2 experimental design that factored sequencing methods (independent variable 1; three levels) with level-bridging scaffolding (independent variable 2; two levels) and compared the effectiveness of each combination. The study measured two dependent variables: (1) knowledge integration (i.e., integrating and connecting content-specific normative concepts and providing coherent scientific explanations); (2) understanding of the deep causal structure (i.e., being able to grasp and transfer the causal knowledge of a complex system). The study used a computer-based simulation environment as the research platform to teach the ideal gas law as a system. The ideal gas law is an emergent chemical system that has three levels: (1) experiential macro level (EM) (e.g., an aerosol can explodes when it is thrown into the fire); (2) abstract macro level (AM) (i.e., the relationships among temperature, pressure and volume); (3) micro level (Mi) (i.e., molecular activity). The sequencing methods of these levels were manipulated by changing the order in which they were delivered with three possibilities: (1) EM-AM-Mi; (2) Mi-AM-EM; (3) AM-Mi-EM. The level-bridging scaffolding variable was manipulated on two aspects: (1) inserting inter-level questions among learning activities; (2) two simulations dynamically linked in the final learning activity. Addressing the first research question, the Experiential macro-Abstract macro-Micro (EM-AM-Mi) sequencing method, following the "concrete to abstract" principle, produced better knowledge integration while the Micro-Abstract macro-Experiential macro (Mi-AM-EM) sequencing method, congruent with the causal direction of the emergent system, produced better understanding of the deep causal structure only when level-bridging scaffolding was provided. The Abstract macro-Micro-Experiential macro (AM-Mi-EM) sequencing method produced worse performance in general, because it did not follow the "concrete to abstract" principle, nor did it align with the causal structure of the emergent system. As to the second research question, the results showed that level-bridging scaffolding was important for both knowledge integration and understanding of the causal structure in learning the ideal gas law system.

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation.

PubMed

Vineis, Paolo; Illari, Phyllis; Russo, Federica

2017-01-01

In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing-notably, the "sufficient-component-cause framework" and the "mark transmission" approach; (b) new acquisitions about disease pathogenesis, e.g. the "branched model" in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of "signals" and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of "cancer causes". We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called "evidential pluralism". According to this view, causal reasoning is based on both "evidence of difference-making" (e.g. associations) and on "evidence of underlying biological mechanisms". We conceptualize the way scientists detect and trace signals in terms of information transmission , which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social-are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

Implementation and Assessment of an Intervention to Debias Adolescents against Causal Illusions

PubMed Central

Barberia, Itxaso; Blanco, Fernando; Cubillas, Carmelo P.; Matute, Helena

2013-01-01

Researchers have warned that causal illusions are at the root of many superstitious beliefs and fuel many people’s faith in pseudoscience, thus generating significant suffering in modern society. Therefore, it is critical that we understand the mechanisms by which these illusions develop and persist. A vast amount of research in psychology has investigated these mechanisms, but little work has been done on the extent to which it is possible to debias individuals against causal illusions. We present an intervention in which a sample of adolescents was introduced to the concept of experimental control, focusing on the need to consider the base rate of the outcome variable in order to determine if a causal relationship exists. The effectiveness of the intervention was measured using a standard contingency learning task that involved fake medicines that typically produce causal illusions. Half of the participants performed the contingency learning task before participating in the educational intervention (the control group), and the other half performed the task after they had completed the intervention (the experimental group). The participants in the experimental group made more realistic causal judgments than did those in the control group, which served as a baseline. To the best of our knowledge, this is the first evidence-based educational intervention that could be easily implemented to reduce causal illusions and the many problems associated with them, such as superstitions and belief in pseudoscience. PMID:23967189

Students' reasons for preferring teleological explanations

NASA Astrophysics Data System (ADS)

Trommler, Friederike; Gresch, Helge; Hammann, Marcus

2018-01-01

The teleological bias, a major learning obstacle, involves explaining biological phenomena in terms of purposes and goals. To probe the teleological bias, researchers have used acceptance judgement tasks and preference judgement tasks. In the present study, such tasks were used with German high school students (N = 353) for 10 phenomena from human biology, that were explained both teleologically and causally. A sub-sample (n = 26) was interviewed about the reasons for their preferences. The results showed that the students favoured teleological explanations over causal explanations. Although the students explained their preference judgements etiologically (i.e. teleologically and causally), they also referred to a wide range of non-etiological criteria (i.e. familiarity, complexity, relevance and five more criteria). When elaborating on their preference for causal explanations, the students often focused not on the causality of the phenomenon, but on mechanisms whose complexity they found attractive. When explaining their preference for teleological explanations, they often focused not teleologically on purposes and goals, but rather on functions, which they found familiar and relevant. Generally, students' preference judgements rarely allowed for making inferences about causal reasoning and teleological reasoning, an issue that is controversial in the literature. Given that students were largely unaware of causality and teleology, their attention must be directed towards distinguishing between etiological and non-etiological reasoning. Implications for educational practice as well as for future research are discussed.

Educational Outcomes of Synchronous and Asynchronous High School Students: A Quantitative Causal-Comparative Study of Online Algebra 1

ERIC Educational Resources Information Center

Berry, Sharon

2017-01-01

This study used a quantitative, causal-comparative design. It compared educational outcome data from online Algebra 1 courses to determine if a significant difference existed between synchronous and asynchronous students for end-of-course grades, state assessments scores, and student perceptions of their course. The study found that synchronous…

Evaluating distance learning in health informatics education.

PubMed

Russell, Barbara L; Barefield, Amanda C; Turnbull, Diane; Leibach, Elizabeth; Pretlow, Lester

2008-04-24

The purpose of this study was to compare academic performance between distance-learning and on-campus health informatics students. A quantitative causal-comparative research design was utilized, and academic performance was measured by final GPA scores and Registered Health Information Administrator certification exam scores. Differences in previous academic performance between the two groups were also determined by comparing overall admission GPA and math/science admission GPA. The researchers found no difference in academic performance between the two groups when final GPA scores and total certification scores were compared. However, there were statistically significant differences between the two groups in 4 of the 17 sub-domains of the certification examination, with the on-campus students scoring slightly higher than the distance students. Correlation studies were also performed, and the researchers found significant correlations between overall admission GPA, math/science admission GPA, final GPA, and certification scores.

Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

PubMed

Hernán, Miguel A; Robins, James M

2016-04-15

Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development and Inter-Rater Reliability of the Liverpool Adverse Drug Reaction Causality Assessment Tool

PubMed Central

Gallagher, Ruairi M.; Kirkham, Jamie J.; Mason, Jennifer R.; Bird, Kim A.; Williamson, Paula R.; Nunn, Anthony J.; Turner, Mark A.; Smyth, Rosalind L.; Pirmohamed, Munir

2011-01-01

Aim To develop and test a new adverse drug reaction (ADR) causality assessment tool (CAT). Methods A comparison between seven assessors of a new CAT, formulated by an expert focus group, compared with the Naranjo CAT in 80 cases from a prospective observational study and 37 published ADR case reports (819 causality assessments in total). Main Outcome Measures Utilisation of causality categories, measure of disagreements, inter-rater reliability (IRR). Results The Liverpool ADR CAT, using 40 cases from an observational study, showed causality categories of 1 unlikely, 62 possible, 92 probable and 125 definite (1, 62, 92, 125) and ‘moderate’ IRR (kappa 0.48), compared to Naranjo (0, 100, 172, 8) with ‘moderate’ IRR (kappa 0.45). In a further 40 cases, the Liverpool tool (0, 66, 81, 133) showed ‘good’ IRR (kappa 0.6) while Naranjo (1, 90, 185, 4) remained ‘moderate’. Conclusion The Liverpool tool assigns the full range of causality categories and shows good IRR. Further assessment by different investigators in different settings is needed to fully assess the utility of this tool. PMID:22194808

Principal stratification in causal inference.

PubMed

Frangakis, Constantine E; Rubin, Donald B

2002-03-01

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

The Relative Predictive Contribution and Causal Role of Phoneme Awareness, Rhyme Awareness, and Verbal Short-Term Memory in Reading Skills: A Review

ERIC Educational Resources Information Center

Melby-Lervag, Monica

2012-01-01

The acknowledgement that educational achievement is highly dependent on successful reading development has led to extensive research on its underlying factors. A strong argument has been made for a causal relationship between reading and phoneme awareness; similarly, causal relations have been suggested for reading with short-term memory and rhyme…

Integrating Genetic and Functional Genomic Data to Elucidate Common Disease Tra

NASA Astrophysics Data System (ADS)

Schadt, Eric

2005-03-01

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here I present a statistical procedure for inferring causal relationships between gene expression traits and more classic clinical traits, including complex disease traits. This procedure has been generalized to the gene network reconstruction problem, where naturally occurring genetic variations in segregating mouse populations are used as a source of perturbations to elucidate tissue-specific gene networks. Differences in the extent of genetic control between genders and among four different tissues are highlighted. I also demonstrate that the networks derived from expression data in segregating mouse populations using the novel network reconstruction algorithm are able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data. This approach to causal inference in large segregating mouse populations over multiple tissues not only elucidates fundamental aspects of transcriptional control, it also allows for the objective identification of key drivers of common human diseases.

Causal modeling in international migration research: a methodological prolegomenon.

PubMed

Papademetriou, D G; Hopple, G W

1982-10-01

The authors examine the value of using models to study the migration process. In particular, they demonstrate the potential utility of a partial least squares modeling approach to the causal analysis of international migration.

A Quantitative Causal-Comparative Nonexperimental Research Study of English Language Learner and Non-English Language Learner Students' Oral Reading Fluency Growth

ERIC Educational Resources Information Center

O'Loughlin, Tricia Ann

2017-01-01

Beginning learners of English progress through the same stages to acquire language. However, the length of time each student spends at a particular stage may vary greatly. Under the current educational policies, ELL students are expected to participate in the general education curriculum while developing their proficiency in the English language.…

The Effects of a School-Family-Community Partnership on the Academic Achievement, High School Graduation, and College Enrollment Rate of Economically Disadvantaged Youth

ERIC Educational Resources Information Center

Morgan, Yvette

2012-01-01

A school-family-community partnership to improve student achievement was examined at a comprehensive high school located in a low income urban community in Long Island City, New York. In this causal comparative analyses study, the researcher examines the effect of a school-family-community partnership on the educational outcomes of economically…

The Effect of National Board Certified Teachers on Mathematics Achievement for Students in a Title I School

ERIC Educational Resources Information Center

Harris, Watress Lashun

2013-01-01

The purpose of the study was to determine if there is a difference in mathematics mean scale score growth on the MCT2 mathematics assessment between students taught by national board certified teachers (NBCTs) and those taught by non-NBCTs in a low socioeconomic, high minority, Title I school. For this study, a causal-comparative research design…

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Causal Inference in Educational Effectiveness Research: A Comparison of Three Methods to Investigate Effects of Homework on Student Achievement

ERIC Educational Resources Information Center

Gustafsson, Jan-Eric

2013-01-01

In educational effectiveness research, it frequently has proven difficult to make credible inferences about cause and effect relations. The article first identifies the main categories of threats to valid causal inference from observational data, and discusses designs and analytic approaches which protect against them. With the use of data from 22…

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use.

PubMed

Edwards, Alexis C; Maes, Hermine H; Prescott, Carol A; Kendler, Kenneth S

2015-02-01

Alcohol consumption is typically correlated with the alcohol use behaviors of one's peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. This study uses data from a sample of male twins (N = 1,790) who provided retrospective reports of their own alcohol consumption and their peers' alcohol-related behaviors, from adolescence into young adulthood (ages 12 to 25). Structural equation modeling was employed to compare 3 plausible models of genetic and environmental influences on the relationship between phenotypes over time. Model fitting indicated that one's own alcohol consumption and the alcohol use of one's peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Peers' alcohol use behaviors and one's own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. Copyright © 2015 by the Research Society on Alcoholism.

Transformational Leadership, Transactional Contingent Reward, and Organizational Identification: The Mediating Effect of Perceived Innovation and Goal Culture Orientations.

PubMed

Xenikou, Athena

2017-01-01

Purpose: The aim of this research was to investigate the effect of transformational leadership and transactional contingent reward as complementary, but distinct, forms of leadership on facets of organizational identification via the perception of innovation and goal organizational values. Design/Methodology/Approach: Three studies were carried out implementing either a measurement of mediation or experimental-causal-chain design to test for the hypothesized effects. Findings: The measurement of mediation study showed that transformational leadership had a positive direct and indirect effect, via innovation value orientation, on cognitive identification, whereas transactional contingent reward was more strongly related to affective, rather than cognitive, identification, and goal orientation was a mediator of their link. The findings of the two experimental-causal-chain studies further supported the hypothesized effects. Transformational leadership was found to lead subordinates to perceive the culture as more innovative compared to transactional contingent reward, whereas transactional contingent reward led employees to perceive the culture as more goal, than innovation, oriented. Finally, innovation, compared to goal, value orientation increased cognitive identification, while goal orientation facilitated affective, rather than cognitive, identification. Implications: The practical implications involve the development of strategies organizations can apply, such as leadership training programs, to strengthen their ties with their employees, which, in turn, may have a positive impact on in-role, as well as extra-role, behaviors. Originality/Value: The originality of this research concerns the identification of distinct mechanisms explaining the effect of transformational leadership and transactional contingent reward on cognitive and affective identification applying an organizational culture perspective and a combination of measurement and causal mediation designs.

Transformational Leadership, Transactional Contingent Reward, and Organizational Identification: The Mediating Effect of Perceived Innovation and Goal Culture Orientations

PubMed Central

Xenikou, Athena

2017-01-01

Purpose: The aim of this research was to investigate the effect of transformational leadership and transactional contingent reward as complementary, but distinct, forms of leadership on facets of organizational identification via the perception of innovation and goal organizational values. Design/Methodology/Approach: Three studies were carried out implementing either a measurement of mediation or experimental-causal-chain design to test for the hypothesized effects. Findings: The measurement of mediation study showed that transformational leadership had a positive direct and indirect effect, via innovation value orientation, on cognitive identification, whereas transactional contingent reward was more strongly related to affective, rather than cognitive, identification, and goal orientation was a mediator of their link. The findings of the two experimental-causal-chain studies further supported the hypothesized effects. Transformational leadership was found to lead subordinates to perceive the culture as more innovative compared to transactional contingent reward, whereas transactional contingent reward led employees to perceive the culture as more goal, than innovation, oriented. Finally, innovation, compared to goal, value orientation increased cognitive identification, while goal orientation facilitated affective, rather than cognitive, identification. Implications: The practical implications involve the development of strategies organizations can apply, such as leadership training programs, to strengthen their ties with their employees, which, in turn, may have a positive impact on in-role, as well as extra-role, behaviors. Originality/Value: The originality of this research concerns the identification of distinct mechanisms explaining the effect of transformational leadership and transactional contingent reward on cognitive and affective identification applying an organizational culture perspective and a combination of measurement and causal mediation designs. PMID:29093688

Moral asymmetries in judgments of agency withstand ludicrous causal deviance.

PubMed

Sousa, Paulo; Holbrook, Colin; Swiney, Lauren

2015-01-01

Americans have been shown to attribute greater intentionality to immoral than to amoral actions in cases of causal deviance, that is, cases where a goal is satisfied in a way that deviates from initially planned means (e.g., a gunman wants to hit a target and his hand slips, but the bullet ricochets off a rock into the target). However, past research has yet to assess whether this asymmetry persists in cases of extreme causal deviance. Here, we manipulated the level of mild to extreme causal deviance of an immoral versus amoral act. The asymmetry in attributions of intentionality was observed at all but the most extreme level of causal deviance, and, as we hypothesized, was mediated by attributions of blame/credit and judgments of action performance. These findings are discussed as they support a multiple-concepts interpretation of the asymmetry, wherein blame renders a naïve concept of intentional action (the outcome matches the intention) more salient than a composite concept (the outcome matches the intention and was brought about by planned means), and in terms of their implications for cross-cultural research on judgments of agency.

Causal diagrams for empirical legal research: a methodology for identifying causation, avoiding bias and interpreting results

PubMed Central

VanderWeele, Tyler J.; Staudt, Nancy

2014-01-01

In this paper we introduce methodology—causal directed acyclic graphs—that empirical researchers can use to identify causation, avoid bias, and interpret empirical results. This methodology has become popular in a number of disciplines, including statistics, biostatistics, epidemiology and computer science, but has yet to appear in the empirical legal literature. Accordingly we outline the rules and principles underlying this new methodology and then show how it can assist empirical researchers through both hypothetical and real-world examples found in the extant literature. While causal directed acyclic graphs are certainly not a panacea for all empirical problems, we show they have potential to make the most basic and fundamental tasks, such as selecting covariate controls, relatively easy and straightforward. PMID:25685055

On the origin of Hill's causal criteria.

PubMed

Morabia, A

1991-09-01

The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

Can chance cause cancer? A causal consideration.

PubMed

Stensrud, Mats Julius; Strohmaier, Susanne; Valberg, Morten; Aalen, Odd Olai

2017-04-01

The role of randomness, environment and genetics in cancer development is debated. We approach the discussion by using the potential outcomes framework for causal inference. By briefly considering the underlying assumptions, we suggest that the antagonising views arise due to estimation of substantially different causal effects. These effects may be hard to interpret, and the results cannot be immediately compared. Indeed, it is not clear whether it is possible to define a causal effect of chance at all. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Effect of Causal Chain Length on Counterfactual Conditional Reasoning

ERIC Educational Resources Information Center

Beck, Sarah R.; Riggs, Kevin J.; Gorniak, Sarah L.

2010-01-01

We investigated German and Nichols' finding that 3-year-olds could answer counterfactual conditional questions about short causal chains of events, but not long. In four experiments (N = 192), we compared 3- and 4-year-olds' performance on short and long causal chain questions, manipulating whether the child could draw on general knowledge to…

Deconstructing events: The neural bases for space, time, and causality

PubMed Central

Kranjec, Alexander; Cardillo, Eileen R.; Lehet, Matthew; Chatterjee, Anjan

2013-01-01

Space, time, and causality provide a natural structure for organizing our experience. These abstract categories allow us to think relationally in the most basic sense; understanding simple events require one to represent the spatial relations among objects, the relative durations of actions or movements, and links between causes and effects. The present fMRI study investigates the extent to which the brain distinguishes between these fundamental conceptual domains. Participants performed a one-back task with three conditions of interest (SPACE, TIME and CAUSALITY). Each condition required comparing relations between events in a simple verbal narrative. Depending on the condition, participants were instructed to either attend to the spatial, temporal, or causal characteristics of events, but between participants, each particular event relation appeared in all three conditions. Contrasts compared neural activity during each condition against the remaining two and revealed how thinking about events is deconstructed neurally. Space trials recruited neural areas traditionally associated with visuospatial processing, primarily bilateral frontal and occipitoparietal networks. Causality trials activated areas previously found to underlie causal thinking and thematic role assignment, such as left medial frontal, and left middle temporal gyri, respectively. Causality trials also produced activations in SMA, caudate, and cerebellum; cortical and subcortical regions associated with the perception of time at different timescales. The TIME contrast however, produced no significant effects. This pattern, indicating negative results for TIME trials, but positive effects for CAUSALITY trials in areas important for time perception, motivated additional overlap analyses to further probe relations between domains. The results of these analyses suggest a closer correspondence between time and causality than between time and space. PMID:21861674

Causal attributions in Brazilian children's reasoning about health and illness.

PubMed

Boruchovitch, E; Mednick, B R

2000-10-01

At a time when a great number of diseases can be prevented by changing one's habits and life style, investigations have focused on understanding what adults and children believe to be desirable health practices and uncovering the factors associated with successful adherence to such practices. For these, causal attributions for health and illness were investigated among 96 Brazilian elementary school students. Ninety six subjects, aged 6 to 14, were interviewed individually and their causal attributions were assessed through 14 true-false items (e.g. people stay well [healthy] because they are lucky). The relationship between the children's causal attributions and demographic characteristics were also examined. Overall, the results were consistent with previous researches. "Taking care of oneself" was considered the most important cause of good health. "Viruses and germs" and "lack of self-care" were the most selected causes of illness. Analyses revealed significant relationship between subjects' causal attribution and their age, school grade level, socioeconomic status and gender. The study findings suggest that there may be more cross-cultural similarities than differences in children's causal attributions for health and illness. Finding ways to help individuals engage in appropriate preventive-maintenance health practices without developing an exaggerated notion that the individuals can control their own health and illness is a challenge which remains to be addressed by further research.

Drawing causal inferences using propensity scores: a practical guide for community psychologists.

PubMed

Lanza, Stephanie T; Moore, Julia E; Butera, Nicole M

2013-12-01

Confounding present in observational data impede community psychologists' ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods-weighting, matching, and subclassification-is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.

Drawing Causal Inferences Using Propensity Scores: A Practical Guide for Community Psychologists

PubMed Central

Lanza, Stephanie T.; Moore, Julia E.; Butera, Nicole M.

2014-01-01

Confounding present in observational data impede community psychologists’ ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods – weighting, matching, and subclassification – is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research. PMID:24185755

Current perspectives on the biological study of play: signs of progress.

PubMed

Graham, Kerrie Lewis; Burghardt, Gordon M

2010-12-01

There has been a recent resurgence of interest in the study of play behavior, marked by much empirical research and theoretical review. These efforts suggest that play may be of greater biological significance than most scientists realize. Here we present a brief synopsis of current play research covering issues of adaptive function, phylogeny, causal mechanisms, and development. Our goal is to selectively highlight contemporary areas of research in which the underlying processes and consequences of play should not be ignored. We elucidate some of the new and burgeoning areas of play research and interpret them from an integrative biological theoretical perspective that highlights areas in need of further experimental, comparative, and field research.

Motivation and burnout among top amateur rugby players.

PubMed

Cresswell, Scott L; Eklund, Robert C

2005-03-01

Self-determination theory has proven to be a useful theoretical explanation of the occurrence of ill-being on a variety of accounts. Self-determination theory may also provide a useful explanation of the occurrence of athlete burnout. To date, limited evidence exists to support links between motivation and burnout. To examine relationships and potential causal directions among burnout and types of motivation differing in degree of self-determination. Data were collected on burnout using the Athlete Burnout Questionnaire and Sport Motivation Scale from 392 top amateur male rugby players. Structural equation modeling procedures were employed to evaluate a measurement model and three conceptually grounded structural models. One conceptual model specified concomitant (noncausal) relationships between burnout and motivations varying in self-determination. The other conceptual models specified causal pathways between burnout and the three motivation variables considered in the investigation (i.e., intrinsic motivation, external regulation, and amotivation). Within the models, amotivation, the least self-determined type of motivation, had a large positive association with burnout. Externally regulated motivation had trivial and nonsignificant relationships with burnout. Self-determined forms of motivation (i.e., intrinsic motivation) exhibited significant negative associations with burnout. Overall the results support the potential utility of a self-determination theory explanation of burnout. As all models displayed reasonable and comparable fits, further research is required to establish the nature (concomitant vs directional causal vs reciprocal causal) of the relationship between burnout and motivation.

Preliminary Validation of the Perceived Locus of Causality Scale for Academic Motivation in the Context of University Studies (PLOC-U)

ERIC Educational Resources Information Center

Sánchez de Miguel, Manuel; Lizaso, Izarne; Hermosilla, Daniel; Alcover, Carlos-Maria; Goudas, Marios; Arranz-Freijó, Enrique

2017-01-01

Background: Research has shown that self-determination theory can be useful in the study of motivation in sport and other forms of physical activity. The Perceived Locus of Causality (PLOC) scale was originally designed to study both. Aim: The current research presents and validates the new PLOC-U scale to measure academic motivation in the…

The role of counterfactual theory in causal reasoning.

PubMed

Maldonado, George

2016-10-01

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not. I hypothesize that the interventionist approach to causal inference in epidemiology stems from elevating the randomized trial design to the gold standard for thinking about causal inference. I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)). I also remind us that no method should be termed "causal" unless it addresses the effect of other biases in addition to the problem of confounding. Copyright © 2016 Elsevier Inc. All rights reserved.

Causal Analysis After Haavelmo

PubMed Central

Heckman, James; Pinto, Rodrigo

2014-01-01

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

The Impact of Troops to Teachers Participants on Student Achievement: A Causal-Comparative Study

ERIC Educational Resources Information Center

Osuch, Kurt Stanley

2014-01-01

The purpose of this causal-comparative study is to examine the impact of Troops to Teachers (TTT) participants on student achievement by comparing the mean scores of Texas students in the eighth grade during the 2011-2012 academic year taught by TTT participants with the mean scores of all other Texas eighth grade students on each of four…

Illusions of causality: how they bias our everyday thinking and how they could be reduced.

PubMed

Matute, Helena; Blanco, Fernando; Yarritu, Ion; Díaz-Lago, Marcos; Vadillo, Miguel A; Barberia, Itxaso

2015-01-01

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion.

Illusions of causality: how they bias our everyday thinking and how they could be reduced

PubMed Central

Matute, Helena; Blanco, Fernando; Yarritu, Ion; Díaz-Lago, Marcos; Vadillo, Miguel A.; Barberia, Itxaso

2015-01-01

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion. PMID:26191014

Case Studies Nested in Fuzzy-Set QCA on Sufficiency: Formalizing Case Selection and Causal Inference

ERIC Educational Resources Information Center

Schneider, Carsten Q.; Rohlfing, Ingo

2016-01-01

Qualitative Comparative Analysis (QCA) is a method for cross-case analyses that works best when complemented with follow-up case studies focusing on the causal quality of the solution and its constitutive terms, the underlying causal mechanisms, and potentially omitted conditions. The anchorage of QCA in set theory demands criteria for follow-up…

Ultra-Wideband Electromagnetic Pulse Propagation through Causal Media

DTIC Science & Technology

2016-03-04

AFRL-AFOSR-VA-TR-2016-0112 Ultra-Wideband Electromagnetic Pulse Propagation through Causal Media Natalie Cartwright RESEARCH FOUNDATION OF STATE... Electromagnetic Pulse Propagation through Causal Media 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0013 5c.  PROGRAM ELEMENT NUMBER 61102F 6...SUPPLEMENTARY NOTES 14. ABSTRACT When an electromagnetic pulse travels through a dispersive material each frequency of the transmitted pulse changes in both

Compared to What? Estimating Causal Effects for Latent Subgroups to Understand Variation in the Impacts of Head Start by Alternate Child Care Setting

ERIC Educational Resources Information Center

Feller, Avi; Grindal, Todd; Miratrix, Luke; Page, Lindsay C.

2014-01-01

Head Start programs currently provide early childhood education and family support services to more than 900,000 low-income children and their families across the United States with an annual budget of around $8 billion in state and federal funds. Researchers and policy makers have debated the program's effectiveness since its inception in 1964.…

Reducing Bias and Increasing Precision by Adding Either a Pretest Measure of the Study Outcome or a Nonequivalent Comparison Group to the Basic Regression Discontinuity Design: An Example from Education

ERIC Educational Resources Information Center

Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin

2015-01-01

Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…

The Mental Health Outcomes of Drought: A Systematic Review and Causal Process Diagram

PubMed Central

Vins, Holly; Bell, Jesse; Saha, Shubhayu; Hess, Jeremy J.

2015-01-01

Little is understood about the long term, indirect health consequences of drought (a period of abnormally dry weather). In particular, the implications of drought for mental health via pathways such as loss of livelihood, diminished social support, and rupture of place bonds have not been extensively studied, leaving a knowledge gap for practitioners and researchers alike. A systematic review of literature was performed to examine the mental health effects of drought. The systematic review results were synthesized to create a causal process diagram that illustrates the pathways linking drought effects to mental health outcomes. Eighty-two articles using a variety of methods in different contexts were gathered from the systematic review. The pathways in the causal process diagram with greatest support in the literature are those focusing on the economic and migratory effects of drought. The diagram highlights the complexity of the relationships between drought and mental health, including the multiple ways that factors can interact and lead to various outcomes. The systematic review and resulting causal process diagram can be used in both practice and theory, including prevention planning, public health programming, vulnerability and risk assessment, and research question guidance. The use of a causal process diagram provides a much needed avenue for integrating the findings of diverse research to further the understanding of the mental health implications of drought. PMID:26506367

What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts.

PubMed

Brion, Marie-Jo A; Lawlor, Debbie A; Matijasevich, Alicia; Horta, Bernardo; Anselmi, Luciana; Araújo, Cora L; Menezes, Ana Maria B; Victora, Cesar G; Smith, George Davey

2011-06-01

A novel approach is explored for improving causal inference in observational studies by comparing cohorts from high-income with low- or middle-income countries (LMIC), where confounding structures differ. This is applied to assessing causal effects of breastfeeding on child blood pressure (BP), body mass index (BMI) and intelligence quotient (IQ). Standardized approaches for assessing the confounding structure of breastfeeding by socio-economic position were applied to the British Avon Longitudinal Study of Parents and Children (ALSPAC) (N ≃ 5000) and Brazilian Pelotas 1993 cohorts (N ≃ 1000). This was used to improve causal inference regarding associations of breastfeeding with child BP, BMI and IQ. Analyses were extended to include results from a meta-analysis of five LMICs (N ≃ 10 000) and compared with a randomized trial of breastfeeding promotion. Findings Although higher socio-economic position was strongly associated with breastfeeding in ALSPAC, there was little such patterning in Pelotas. In ALSPAC, breastfeeding was associated with lower BP, lower BMI and higher IQ, adjusted for confounders, but in the directions expected if due to socioeconomic patterning. In contrast, in Pelotas, breastfeeding was not strongly associated with BP or BMI but was associated with higher IQ. Differences in associations observed between ALSPAC and the LMIC meta-analysis were in line with those observed between ALSPAC and Pelotas, but with robust evidence of heterogeneity detected between ALSPAC and the LMIC meta-analysis associations. Trial data supported the conclusions inferred by the cross-cohort comparisons, which provided evidence for causal effects on IQ but not for BP or BMI. While reported associations of breastfeeding with child BP and BMI are likely to reflect residual confounding, breastfeeding may have causal effects on IQ. Comparing associations between populations with differing confounding structures can be used to improve causal inference in observational studies.

The impact of pretend play on children's development: a review of the evidence.

PubMed

Lillard, Angeline S; Lerner, Matthew D; Hopkins, Emily J; Dore, Rebecca A; Smith, Eric D; Palmquist, Carolyn M

2013-01-01

Pretend play has been claimed to be crucial to children's healthy development. Here we examine evidence for this position versus 2 alternatives: Pretend play is 1 of many routes to positive developments (equifinality), and pretend play is an epiphenomenon of other factors that drive development. Evidence from several domains is considered. For language, narrative, and emotion regulation, the research conducted to date is consistent with all 3 positions but insufficient to draw conclusions. For executive function and social skills, existing research leans against the crucial causal position but is insufficient to differentiate the other 2. For reasoning, equifinality is definitely supported, ruling out a crucially causal position but still leaving open the possibility that pretend play is epiphenomenal. For problem solving, there is no compelling evidence that pretend play helps or is even a correlate. For creativity, intelligence, conservation, and theory of mind, inconsistent correlational results from sound studies and nonreplication with masked experimenters are problematic for a causal position, and some good studies favor an epiphenomenon position in which child, adult, and environment characteristics that go along with play are the true causal agents. We end by considering epiphenomenalism more deeply and discussing implications for preschool settings and further research in this domain. Our take-away message is that existing evidence does not support strong causal claims about the unique importance of pretend play for development and that much more and better research is essential for clarifying its possible role. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Combining Causal Model and Focus Group Discussions Experiences Learned from a Socio-Anthropological Research on the Differing Perceptions of Caretakers and Health Professionals on Children's Health (Bolivia/Peru)

ERIC Educational Resources Information Center

Lefevre, Pierre; de Suremain, Charles-Edouard; Rubin de Celis, Emma; Sejas, Edgar

2004-01-01

The paper discusses the utility of constructing causal models in focus groups. This was experienced as a complement to an in-depth ethnographic research on the differing perceptions of caretakers and health professionals on child's growth and development in Peru and Bolivia. The rational, advantages, difficulties and necessary adaptations of…

Turning health research into health promotion: a study of causality and 'critical insights' in a United Kingdom health campaign.

PubMed

Piggin, Joe

2012-10-01

This article examines how important decisions about health can alter between public health policy formulation and eventual marketing implementation. Specifically, the article traces the development and production of a major United Kingdom social marketing campaign named Change4Life, and examines how ideas about the causes of and solutions to the obesity epidemic are produced in differing ways throughout the health promotion process. This study examines a variety of United Kingdom health research, policy, marketing strategy and marketing messages between 2008 and 2011. This research demonstrates that claims about causality oscillate and alter throughout the research, policy and Change4Life marketing process. These oscillations are problematic, since the Department of Health described the original consumer research as 'critical'. Given both the importance of the health issues being addressed and the amount of funding dedicated to Change4Life, that 'critical' research was directly contradicted in the campaign requires urgent review. To conclude, the article discusses the utility of social marketing when considering causal claims in health promotion. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Narrative review of yoga intervention clinical trials including weight-related outcomes.

PubMed

Rioux, Jennifer Grace; Ritenbaugh, Cheryl

2013-01-01

Medical authorities have identified obesity as a causal factor in the development of diabetes, hypertension, and cardiovascular disease (CVD), and more broadly, of metabolic syndrome/insulin resistance syndrome. To provide solutions that can modify this risk factor, researchers need to identify methods of effective risk reduction and primary prevention of obesity. Research on the effectiveness of yoga as a treatment for obesity is limited, and studies vary in overall quality and methodological rigor. This narrative review assessed the quantity and quality of clinical trials of yoga as an intervention for weight loss or as a means of risk reduction or treatment for obesity and diseases in which obesity is a causal factor. This review summarized the studies' research designs and evaluated the efficacy of yoga for weight loss via the current evidence base. The research team evaluated published studies to determine the appropriateness of research designs, comparability of programs' intervention elements, and standardization of outcome measures. The research team's literature search used the key terms yoga and obesity or yoga and weight loss in three primary medical-literature databases (PubMed, PsychInfo, and Web of Science). The study excluded clinical trials with no quantitative obesity related measure. Extracted data included each study's (1) design; (2) setting and population; (3) nature, duration, and frequency of interventions; (4) comparison groups; (5) recruitment strategies; (6) outcome measures; (7) data analysis and presentation; and (8) results and conclusions. The research team developed an overall evaluation parameter to compare disparate trials. The research team reviewed each study to determine its key features, each worth a specified number of points, with a maximum total of 20 points. The features included a study's (1) duration, (2) frequency of yoga practice, (3) intensity of (length of) each practice, (4) number of yogic elements, (5) inclusion of dietary modification, (6) inclusion of a residential component, (7) the number of weight-related outcome measures, and (8) a discussion of the details of the yogic elements. Overall, therapeutic yoga programs are frequently effective in promoting weight loss and/or improvements in body composition. The effectiveness of yoga for weight loss is related to the following key features: (1) an increased frequency of practice; (2) a longer intervention duration (3) a yogic dietary component; (4) a residential component; (5) the comprehensive inclusion of yogic components; (5) and a home-practice component. Yoga appears to be an appropriate and potentially successful intervention for weight maintenance, prevention of obesity, and risk reduction for diseases in which obesity plays a significant causal role.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa.

PubMed

Rehfuess, Eva A; Best, Nicky; Briggs, David J; Joffe, Mike

2013-12-06

Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa

PubMed Central

2013-01-01

Background Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. Results Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings. Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. Conclusions Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce. PMID:24314302

Factors Relating to Staff Attributions of Control over Challenging Behaviour

ERIC Educational Resources Information Center

Dilworth, Jennifer A.; Phillips, Neil; Rose, John

2011-01-01

Background: Previous research has suggested that severity of intellectual disability (ID) and topography of behaviour may influence staff causal attributions regarding challenging behaviour. Subsequently, these causal attributions may influence helping behaviours. This study investigated the relationship between attributions of control over…

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

PubMed Central

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

Moral asymmetries in judgments of agency withstand ludicrous causal deviance

PubMed Central

Sousa, Paulo; Holbrook, Colin; Swiney, Lauren

2015-01-01

Americans have been shown to attribute greater intentionality to immoral than to amoral actions in cases of causal deviance, that is, cases where a goal is satisfied in a way that deviates from initially planned means (e.g., a gunman wants to hit a target and his hand slips, but the bullet ricochets off a rock into the target). However, past research has yet to assess whether this asymmetry persists in cases of extreme causal deviance. Here, we manipulated the level of mild to extreme causal deviance of an immoral versus amoral act. The asymmetry in attributions of intentionality was observed at all but the most extreme level of causal deviance, and, as we hypothesized, was mediated by attributions of blame/credit and judgments of action performance. These findings are discussed as they support a multiple-concepts interpretation of the asymmetry, wherein blame renders a naïve concept of intentional action (the outcome matches the intention) more salient than a composite concept (the outcome matches the intention and was brought about by planned means), and in terms of their implications for cross-cultural research on judgments of agency. PMID:26441755

[Social determinants of odontalgia in epidemiological studies: theoretical review and proposed conceptual model].

PubMed

Bastos, João Luiz Dornelles; Gigante, Denise Petrucci; Peres, Karen Glazer; Nedel, Fúlvio Borges

2007-01-01

The epidemiological literature has been limited by the absence of a theoretical framework reflecting the complexity of causal mechanisms for the occurrence of health phenomena / disease conditions. In the field of oral epidemiology, such lack of theory also prevails, since dental caries the leading topic in oral research has been often studied through a biological and reductionist viewpoint. One of the most important consequences of dental caries is dental pain (odontalgia), which has received little attention in studies with sophisticated theoretical models and powerful designs to establish causal relationships. The purpose of this study is to review the scientific literature on the determinants of odontalgia and to discuss theories proposed for the explanation of the phenomenon. Conceptual models and emerging theories on the social determinants of oral health are revised, in an attempt to build up links with the bio-psychosocial pain model, proposing a more elaborate causal model for odontalgia. The framework suggests causal pathways between social structure and oral health through material, psychosocial and behavioral pathways. Aspects of the social structure are highlighted in order to relate them to odontalgia, stressing their importance in discussions of causal relationships in oral health research.

Dual Enrollment Programs: A Comparative Study of High School Students' College Academic Achievement at Different Settings

ERIC Educational Resources Information Center

Flores, Agnes L. Acker

2012-01-01

The "ex post facto" causal-comparative study examined the academic achievement of high school students who took their dual credit English or mathematics college credit-bearing course in two different environments, namely, the college setting and the high school setting. Due to non-experimental nature of the study, no causal inferences…

Quantitative Causal-Comparative Relationship between Interactive Whiteboard Instruction and Student Science Proficiency

ERIC Educational Resources Information Center

Danelczyk, Ewa Krystyna

2013-01-01

The purpose of this quantitative causal-comparative study was to investigate the relationship between the instructional effects of the interactive whiteboard and students' proficiency levels in eighth-grade science as evidenced by the state FCAT scores. A total of 46 eighth-grade science teachers in a South Florida public school district completed…

A Causal-Comparative Study of the Affects of Benchmark Assessments on Middle Grades Science Achievement Scores

ERIC Educational Resources Information Center

Galloway, Melissa Ritchie

2016-01-01

The purpose of this causal comparative study was to test the theory of assessment that relates benchmark assessments to the Georgia middle grades science Criterion Referenced Competency Test (CRCT) percentages, controlling for schools who do not administer benchmark assessments versus schools who do administer benchmark assessments for all middle…

Computational Everyday Life Human Behavior Model as Servicable Knowledge

NASA Astrophysics Data System (ADS)

Motomura, Yoichi; Nishida, Yoshifumi

A project called `Open life matrix' is not only a research activity but also real problem solving as an action research. This concept is realized by large-scale data collection, probabilistic causal structure model construction and information service providing using the model. One concrete outcome of this project is childhood injury prevention activity in new team consist of hospital, government, and many varieties of researchers. The main result from the project is a general methodology to apply probabilistic causal structure models as servicable knowledge for action research. In this paper, the summary of this project and future direction to emphasize action research driven by artificial intelligence technology are discussed.

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

2018-02-01

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

Learning to make things happen: Infants' observational learning of social and physical causal events.

PubMed

Waismeyer, Anna; Meltzoff, Andrew N

2017-10-01

Infants learn about cause and effect through hands-on experience; however, they also can learn about causality simply from observation. Such observational causal learning is a central mechanism by which infants learn from and about other people. Across three experiments, we tested infants' observational causal learning of both social and physical causal events. Experiment 1 assessed infants' learning of a physical event in the absence of visible spatial contact between the causes and effects. Experiment 2 developed a novel paradigm to assess whether infants could learn about a social causal event from third-party observation of a social interaction between two people. Experiment 3 compared learning of physical and social events when the outcomes occurred probabilistically (happening some, but not all, of the time). Infants demonstrated significant learning in all three experiments, although learning about probabilistic cause-effect relations was most difficult. These findings about infant observational causal learning have implications for children's rapid nonverbal learning about people, things, and their causal relations. Copyright © 2017 Elsevier Inc. All rights reserved.

Causality

NASA Astrophysics Data System (ADS)

Pearl, Judea

2000-03-01

Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

Learning about causes from people and about people as causes: probabilistic models and social causal reasoning.

PubMed

Buchsbaum, Daphna; Seiver, Elizabeth; Bridgers, Sophie; Gopnik, Alison

2012-01-01

A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between physical causes and their effects to learn physical causal relationships, they also use covaration between people's actions and the environment to make inferences about the causes of human behavior.

Identifying Causal Variants at Loci with Multiple Signals of Association

PubMed Central

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-01-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20–50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. PMID:25104515

Identifying causal variants at loci with multiple signals of association.

PubMed

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-10-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. Copyright © 2014 by the Genetics Society of America.

Causal factors of corporate crime in Taiwan: qualitative and quantitative findings.

PubMed

Mon, Wei-Teh

2002-04-01

Street crimes are a primary concern of most criminologists in Taiwan. In recent years, however, crimes committed by corporations have increased greatly in this country. Employing the empirical approach to collect data about causal factors of corporate crime, the research presented in this article is the first systematic empirical study concerning corporate crime in Taiwan. The research sample was selected from a corporation with a criminal record of pollution caused by the release of toxic chemicals into the environment and a corporation with no criminal record. Questionnaire survey and interviews of corporate employees and managers were conducted, and secondary data were collected from official agencies. This research indicated the causal factors of corporate crime as follows: the failure of government regulation, lack of corporate self-regulation, lack of public concern about corporate crime, corporate mechanistic structure, and the low self-control tendency of corporate managers.

The social determinants of oral health: new approaches to conceptualizing and researching complex causal networks.

PubMed

Newton, J Timothy; Bower, Elizabeth J

2005-02-01

Oral epidemiological research into the social determinants of oral health has been limited by the absence of a theoretical framework which reflects the complexity of real life social processes and the network of causal pathways between social structure and oral health and disease. In the absence of such a framework, social determinants are treated as isolated risk factors, attributable to the individual, having a direct impact on oral health. There is little sense of how such factors interrelate over time and place and the pathways between the factors and oral health. Features of social life which impact on individuals' oral health but are not reducible to the individual remain under-researched. A conceptual framework informing mainstream epidemiological research into the social determinants of health is applied to oral epidemiology. The framework suggests complex causal pathways between social structure and health via interlinking material, psychosocial and behavioural pathways. Methodological implications for oral epidemiological research informed by the framework, such as the use of multilevel modelling, path analysis and structural equation modelling, combining qualitative and quantitative research methods, and collaborative research, are discussed. Copyright Blackwell Munksgaard, 2005.

Understanding bicycling in cities using system dynamics modelling.

PubMed

Macmillan, Alexandra; Woodcock, James

2017-12-01

Increasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes. We used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contrasting contexts: Auckland, London and Nijmegen. We combined qualitative interviews and workshops to develop the CLDs. We used the three CLDs to compare and contrast influences on cycling at different points on a "cycling trajectory" and drew out policy insights. The three CLDs consisted of feedback loops dynamically influencing cycling, with significant overlap between the three diagrams. Common reinforcing patterns emerged: growing numbers of people cycling lifts political will to improve the environment; cycling safety in numbers drives further growth; and more cycling can lead to normalisation across the population. By contrast, limits to growth varied as cycling increases. In Auckland and London, real and perceived danger was considered the main limit, with added barriers to normalisation in London. Cycling congestion and "market saturation" were important in the Netherlands. A generalisable, dynamic causal theory for urban cycling enables a more ordered set of policy recommendations for different cities on a cycling trajectory. Participation meant the collective knowledge of cycling stakeholders was represented and triangulated with research evidence. Extending this research to further cities, especially in low-middle income countries, would enhance generalizability of the CLDs.

Political Attitudes Develop Independently of Personality Traits

PubMed Central

Hatemi, Peter K.; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area. PMID:25734580

Political attitudes develop independently of personality traits.

PubMed

Hatemi, Peter K; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.

Contrasting cue-density effects in causal and prediction judgments.

PubMed

Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

2011-02-01

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

A Causal-Comparative Study of Third Grade Reading Achievement and the Use of Commercial Reading Programs to Promote Literacy

ERIC Educational Resources Information Center

McDonald, Wendy E.

2013-01-01

This quantitative, causal-comparative study examined the reading achievement of third grade students to ascertain the reading health of elementary students as measured through South Carolina's standardized assessment, the Palmetto Assessment of State Standards (PASS). The purpose of this study was to determine if there was a significant difference…

An assessment of predominant causal factors of pilot deviations that contribute to runway incursions

NASA Astrophysics Data System (ADS)

Campbell, Denado M.

The aim of this study was to identify predominant causal factors of pilot deviations in runway incursions over a two-year period. Runway incursion reports were obtained from NASA's Aviation Safety Reporting System (ASRS), and a qualitative method was used by classifying and coding each report to a specific causal factor(s). The causal factors that were used were substantiated by research from the Aircraft Owner's and Pilot's Association that found that these causal factors were the most common in runway incursion incidents and accidents. An additional causal factor was also utilized to determine the significance of pilot training in relation to runway incursions. From the reports examined, it was found that miscommunication and situational awareness have the greatest impact on pilots and are most often the major causes of runway incursions. This data can be used to assist airports, airlines, and the FAA to understand trends in pilot deviations, and to find solutions for specific problem areas in runway incursion incidents.

Academic Success Factors: An IT Student Perspective

ERIC Educational Resources Information Center

Zhang, Aimao; Aasheim, Cheryl L.

2011-01-01

Numerous studies have identified causal factors for academic success. Factors vary from personal factors, such as cognitive style (McKenzie & Schweitzer, 2001), to social factors, such as culture differences (Aysan, Tanriogen, & Tanriogen, 1996). However, in these studies it is re-searchers who theorized the causal dimensions and…

Pride and Prejudice and Causal Indicators

ERIC Educational Resources Information Center

Lee, Nick; Chamberlain, Laura

2016-01-01

Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…

Does Causality Matter More Now? Increase in the Proportion of Causal Language in English Texts.

PubMed

Iliev, Rumen; Axelrod, Robert

2016-05-01

The vast majority of the work on culture and cognition has focused on cross-cultural comparisons, largely ignoring the dynamic aspects of culture. In this article, we provide a diachronic analysis of causal cognition over time. We hypothesized that the increased role of education, science, and technology in Western societies should be accompanied by greater attention to causal connections. To test this hypothesis, we compared word frequencies in English texts from different time periods and found an increase in the use of causal language of about 40% over the past two centuries. The observed increase was not attributable to general language effects or to changing semantics of causal words. We also found that there was a consistent difference between the 19th and the 20th centuries, and that the increase happened mainly in the 20th century. © The Author(s) 2016.

Children's science learning: A core skills approach.

PubMed

Tolmie, Andrew K; Ghazali, Zayba; Morris, Suzanne

2016-09-01

Research has identified the core skills that predict success during primary school in reading and arithmetic, and this knowledge increasingly informs teaching. However, there has been no comparable work that pinpoints the core skills that underlie success in science. The present paper attempts to redress this by examining candidate skills and considering what is known about the way in which they emerge, how they relate to each other and to other abilities, how they change with age, and how their growth may vary between topic areas. There is growing evidence that early-emerging tacit awareness of causal associations is initially separated from language-based causal knowledge, which is acquired in part from everyday conversation and shows inaccuracies not evident in tacit knowledge. Mapping of descriptive and explanatory language onto causal awareness appears therefore to be a key development, which promotes unified conceptual and procedural understanding. This account suggests that the core components of initial science learning are (1) accurate observation, (2) the ability to extract and reason explicitly about causal connections, and (3) knowledge of mechanisms that explain these connections. Observational ability is educationally inaccessible until integrated with verbal description and explanation, for instance, via collaborative group work tasks that require explicit reasoning with respect to joint observations. Descriptive ability and explanatory ability are further promoted by managed exposure to scientific vocabulary and use of scientific language. Scientific reasoning and hypothesis testing are later acquisitions that depend on this integration of systems and improved executive control. © 2016 The British Psychological Society.

Causality analysis in business performance measurement system using system dynamics methodology

NASA Astrophysics Data System (ADS)

Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

2014-07-01

One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

Unexpected findings and promoting monocausal claims, a cautionary tale.

PubMed

Copeland, Samantha Marie

2017-10-01

Stories of serendipitous discoveries in medicine incorrectly imply that the path from an unexpected observation to major discovery is straightforward or guaranteed. In this paper, I examine a case from the field of research about chronic fatigue syndrome (CFS). In Norway, an unexpected positive result during clinical care has led to the development of a research programme into the potential for the immunosuppressant drug rituximab to relieve the symptoms of CFS. The media and public have taken up researchers' speculations that their research results indicate a causal mechanism for CFS - consequently, patients now have great hope that 'the cause' of CFS has been found, and thus, a cure is sure to follow. I argue that a monocausal claim cannot be correctly asserted, either on the basis of the single case of an unexpected, although positive, result or on the basis of the empirical research that has followed up on that result. Further, assertion and promotion of this claim will have specific harmful effects: it threatens to inappropriately narrow the scope of research on CFS, might misdirect research altogether, and could directly and indirectly harm patients. Therefore, the CFS case presents a cautionary tale, illustrating the risks involved in drawing a theoretical hypothesis from an unexpected observation. Further, I draw attention to the tendency in contemporary clinical research with CFS to promote new research directions on the basis of reductive causal models of that syndrome. Particularly, in the case of CFS research, underdetermination and causal complexity undermine the potential value of a monocausal claim. In sum, when an unexpected finding occurs in clinical practice or medical research, the value of following up on that finding is to be found not in the projected value of a singular causal relationship inferred from the finding but rather in the process of research that follows. © 2016 John Wiley & Sons, Ltd.

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

Advancing understanding of affect labeling with dynamic causal modeling

PubMed Central

Torrisi, Salvatore J.; Lieberman, Matthew D.; Bookheimer, Susan Y.; Altshuler, Lori L.

2013-01-01

Mechanistic understandings of forms of incidental emotion regulation have implications for basic and translational research in the affective sciences. In this study we applied Dynamic Causal Modeling (DCM) for fMRI to a common paradigm of labeling facial affect to elucidate prefrontal to subcortical influences. Four brain regions were used to model affect labeling, including right ventrolateral prefrontal cortex (vlPFC), amygdala and Broca’s area. 64 models were compared, for each of 45 healthy subjects. Family level inference split the model space to a likely driving input and Bayesian Model Selection within the winning family of 32 models revealed a strong pattern of endogenous network connectivity. Modulatory effects of labeling were most prominently observed following Bayesian Model Averaging, with the dampening influence on amygdala originating from Broca’s area but much more strongly from right vlPFC. These results solidify and extend previous correlation and regression-based estimations of negative corticolimbic coupling. PMID:23774393

Do chimpanzees seek explanations? Preliminary comparative investigations.

PubMed

Povinelli, D J; Dunphy-Lelii, S

2001-06-01

During the past decade, considerable effort has been devoted to understanding whether chimpanzees reason about unobservable variables as explanations for observable events. With respect to physical causality, these investigations have explored chimpanzees' understanding of gravity, force, mass, shape, and so on. With respect to social causality, this research has focused on the question of whether they reason about mental states such as emotions, desires, and beliefs. In the studies reported here, we explored whether the chimpanzee's natural motivation for object exploration is modulated by a cognitive system that seeks explanations for unexpected events. We confronted both chimpanzees and young children with simple tasks which occasionally could not be made to work. We coded their reactions to determine if they appeared to be searching for an apparent cause (or explanation) of the task failure. The results of these preliminary studies point to both similarities and differences in how young children and chimpanzees react to such circumstances.

Disgust and the politics of sex: exposure to a disgusting odorant increases politically conservative views on sex and decreases support for gay marriage.

PubMed

Adams, Thomas G; Stewart, Patrick A; Blanchar, John C

2014-01-01

Disgust has been implicated as a potential causal agent underlying socio-political attitudes and behaviors. Several recent studies have suggested that pathogen disgust may be a causal mechanism underlying social conservatism. However, the specificity of this effect is still in question. The present study tested the effects of disgust on a range of policy preferences to clarify whether disgust is generally implicated in political conservatism across public policy attitudes or is uniquely related to specific content domains. Self-reported socio-political attitudes were compared between participants in two experimental conditions: 1) an odorless control condition, and 2) a disgusting odor condition. In keeping with previous research, the present study showed that exposure to a disgusting odor increased endorsement of socially conservative attitudes related to sexuality. In particular, there was a strong and consistent link between induced disgust and less support for gay marriage.

Siblings within Families: Levels of Analysis and Patterns of Influence

ERIC Educational Resources Information Center

Jenkins, Jennifer; Dunn, Judy

2009-01-01

The study of siblings has become increasingly central to developmental science. Sibling relationships have unique effects on development, and sibling designs allow researchers to isolate causal mechanisms in development. This volume emphasizes causal mechanisms in the social domain. We review the preceding chapters in relation to six topics: a…

Empirically Driven Variable Selection for the Estimation of Causal Effects with Observational Data

ERIC Educational Resources Information Center

Keller, Bryan; Chen, Jianshen

2016-01-01

Observational studies are common in educational research, where subjects self-select or are otherwise non-randomly assigned to different interventions (e.g., educational programs, grade retention, special education). Unbiased estimation of a causal effect with observational data depends crucially on the assumption of ignorability, which specifies…

Fostering Deeper Critical Inquiry with Causal Layered Analysis

ERIC Educational Resources Information Center

Haigh, Martin

2016-01-01

Causal layered analysis (CLA) is a technique that enables deeper critical inquiry through a structured exploration of four layers of causation. CLA's layers reach down from the surface litany of media understanding, through the layer of systemic causes identified by conventional research, to underpinning worldviews, ideologies and philosophies,…

Extending Attribution Theory: Considering Students' Perceived Control of the Attribution Process

ERIC Educational Resources Information Center

Fishman, Evan J.; Husman, Jenefer

2017-01-01

Research in attribution theory has shown that students' causal thinking profoundly affects their learning and motivational outcomes. Very few studies, however, have explored how students' attribution-related beliefs influence the causal thought process. The present study used the perceived control of the attribution process (PCAP) model to examine…

Commentary: Using Potential Outcomes to Understand Causal Mediation Analysis

ERIC Educational Resources Information Center

Imai, Kosuke; Jo, Booil; Stuart, Elizabeth A.

2011-01-01

In this commentary, we demonstrate how the potential outcomes framework can help understand the key identification assumptions underlying causal mediation analysis. We show that this framework can lead to the development of alternative research design and statistical analysis strategies applicable to the longitudinal data settings considered by…

The representation of inherent properties.

PubMed

Prasada, Sandeep

2014-10-01

Research on the representation of generic knowledge suggests that inherent properties can have either a principled or a causal connection to a kind. The type of connection determines whether the outcome of the storytelling process will include intuitions of inevitability and a normative dimension and whether it will ground causal explanations.

Music and Spatial Task Performance: A Causal Relationship.

ERIC Educational Resources Information Center

Rauscher, Frances H.; And Others

This research paper reports on testing the hypothesis that music and spatial task performance are causally related. Two complementary studies are presented that replicate and explore previous findings. One study of college students showed that listening to a Mozart sonata induces subsequent short-term spatial reasoning facilitation and tested the…

Distinguishing Valid from Invalid Causal Indicator Models

ERIC Educational Resources Information Center

Cadogan, John W.; Lee, Nick

2016-01-01

In this commentary from Issue 14, n3, authors John Cadogan and Nick Lee applaud the paper by Aguirre-Urreta, Rönkkö, and Marakas "Measurement: Interdisciplinary Research and Perspectives", 14(3), 75-97 (2016), since their explanations and simulations work toward demystifying causal indicator models, which are often used by scholars…

A general, multivariate definition of causal effects in epidemiology.

PubMed

Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

Unified framework for information integration based on information geometry

PubMed Central

Oizumi, Masafumi; Amari, Shun-ichi

2016-01-01

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner. PMID:27930289

Spot the difference: Causal contrasts in scientific diagrams.

PubMed

Scholl, Raphael

2016-12-01

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown that causal contrasts can be expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they often serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific - that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers. Copyright © 2016. Published by Elsevier Ltd.

Packet Randomized Experiments for Eliminating Classes of Confounders

PubMed Central

Pavela, Greg; Wiener, Howard; Fontaine, Kevin R.; Fields, David A.; Voss, Jameson D.; Allison, David B.

2014-01-01

Background Although randomization is considered essential for causal inference, it is often not possible to randomize in nutrition and obesity research. To address this, we develop a framework for an experimental design—packet randomized experiments (PREs), which improves causal inferences when randomization on a single treatment variable is not possible. This situation arises when subjects are randomly assigned to a condition (such as a new roommate) which varies in one characteristic of interest (such as weight), but also varies across many others. There has been no general discussion of this experimental design, including its strengths, limitations, and statistical properties. As such, researchers are left to develop and apply PREs on an ad hoc basis, limiting its potential to improve causal inferences among nutrition and obesity researchers. Methods We introduce PREs as an intermediary design between randomized controlled trials and observational studies. We review previous research that used the PRE design and describe its application in obesity-related research, including random roommate assignments, heterochronic parabiosis, and the quasi-random assignment of subjects to geographic areas. We then provide a statistical framework to control for potential packet-level confounders not accounted for by randomization. Results PREs have successfully been used to improve causal estimates of the effect of roommates, altitude, and breastfeeding on weight outcomes. When certain assumptions are met, PREs can asymptotically control for packet-level characteristics. This has the potential to statistically estimate the effect of a single treatment even when randomization to a single treatment did not occur. Conclusions Applying PREs to obesity-related research will improve decisions about clinical, public health, and policy actions insofar as it offers researchers new insight into cause and effect relationships among variables. PMID:25444088

Design of fuzzy cognitive maps using neural networks for predicting chaotic time series.

PubMed

Song, H J; Miao, C Y; Shen, Z Q; Roel, W; Maja, D H; Francky, C

2010-12-01

As a powerful paradigm for knowledge representation and a simulation mechanism applicable to numerous research and application fields, Fuzzy Cognitive Maps (FCMs) have attracted a great deal of attention from various research communities. However, the traditional FCMs do not provide efficient methods to determine the states of the investigated system and to quantify causalities which are the very foundation of the FCM theory. Therefore in many cases, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to model subjectivity and difficulties with accessing its reliability. In this paper, we propose a fuzzy neural network to enhance the learning ability of FCMs so that the automatic determination of membership functions and quantification of causalities can be incorporated with the inference mechanism of conventional FCMs. In this manner, FCM models of the investigated systems can be automatically constructed from data, and therefore are independent of the experts. Furthermore, we employ mutual subsethood to define and describe the causalities in FCMs. It provides more explicit interpretation for causalities in FCMs and makes the inference process easier to understand. To validate the performance, the proposed approach is tested in predicting chaotic time series. The simulation studies show the effectiveness of the proposed approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

Teacher Perception of Principals' Leadership Traits and Middle School Math and Science Teachers' Job Satisfaction: A Causal-Comparative and Correlational Study

ERIC Educational Resources Information Center

Cousar, Theresa Ann

2017-01-01

The purpose of this quantitative study was to examine middle school teachers' job satisfaction (low vs. high) and how teachers perceive principals' leadership traits. The study used a causal-comparative and correlational design. The teachers were divided into two job satisfaction level groups. Teacher perception of principal leadership traits for…

Clinical Research Methodology 2: Observational Clinical Research.

PubMed

Sessler, Daniel I; Imrey, Peter B

2015-10-01

Case-control and cohort studies are invaluable research tools and provide the strongest feasible research designs for addressing some questions. Case-control studies usually involve retrospective data collection. Cohort studies can involve retrospective, ambidirectional, or prospective data collection. Observational studies are subject to errors attributable to selection bias, confounding, measurement bias, and reverse causation-in addition to errors of chance. Confounding can be statistically controlled to the extent that potential factors are known and accurately measured, but, in practice, bias and unknown confounders usually remain additional potential sources of error, often of unknown magnitude and clinical impact. Causality-the most clinically useful relation between exposure and outcome-can rarely be definitively determined from observational studies because intentional, controlled manipulations of exposures are not involved. In this article, we review several types of observational clinical research: case series, comparative case-control and cohort studies, and hybrid designs in which case-control analyses are performed on selected members of cohorts. We also discuss the analytic issues that arise when groups to be compared in an observational study, such as patients receiving different therapies, are not comparable in other respects.

The Singaporean public beliefs about the causes of mental illness: results from a multi-ethnic population-based study.

PubMed

Pang, S; Subramaniam, M; Lee, S P; Lau, Y W; Abdin, E; Chua, B Y; Picco, L; Vaingankar, J A; Chong, S A

2017-04-03

To identify the common causal beliefs of mental illness in a multi-ethnic Southeast Asian community and describe the sociodemographic associations to said beliefs. The factor structure to the causal beliefs scale is explored. The causal beliefs relating to five different mental illnesses (alcohol abuse, depression, obsessive-compulsive disorder (OCD), dementia and schizophrenia) and desire for social distance are also investigated. Data from 3006 participants from a nationwide vignette-based study on mental health literacy were analysed using factor analysis and multiple logistic regression to address the aims. Participants answered questions related to sociodemographic information, causal beliefs of mental illness and their desire for social distance towards those with mental illness. Physical causes, psychosocial causes and personality causes were endorsed by the sample. Sociodemographic differences including ethnic, gender and age differences in causal beliefs were found in the sample. Differences in causal beliefs were shown across different mental illness vignettes though psychosocial causes was the most highly attributed cause across vignettes (endorsed by 97.9% of respondents), followed by personality causes (83.5%) and last, physical causes (37%). Physical causes were more likely to be endorsed for OCD, depression and schizophrenia. Psychosocial causes were less often endorsed for OCD. Personality causes were less endorsed for dementia but more associated with depression. The factor structure of the causal beliefs scale is not entirely the same as that found in previous research. Further research on the causal beliefs endorsed by Southeast Asian communities should be conducted to investigate other potential causes such as biogenetic factors and spiritual/supernatural causes. Mental health awareness campaigns should address causes of mental illness as a topic. Lay beliefs in the different causes must be acknowledged and it would be beneficial for the public to be informed of the causes of some of the most common mental illnesses in order to encourage help-seeking and treatment compliance.

From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology

USGS Publications Warehouse

Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.

2015-01-01

In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

Quasi-experimental study designs series-paper 4: uses and value.

PubMed

Bärnighausen, Till; Tugwell, Peter; Røttingen, John-Arne; Shemilt, Ian; Rockers, Peter; Geldsetzer, Pascal; Lavis, John; Grimshaw, Jeremy; Daniels, Karen; Brown, Annette; Bor, Jacob; Tanner, Jeffery; Rashidian, Arash; Barreto, Mauricio; Vollmer, Sebastian; Atun, Rifat

2017-09-01

Quasi-experimental studies are increasingly used to establish causal relationships in epidemiology and health systems research. Quasi-experimental studies offer important opportunities to increase and improve evidence on causal effects: (1) they can generate causal evidence when randomized controlled trials are impossible; (2) they typically generate causal evidence with a high degree of external validity; (3) they avoid the threats to internal validity that arise when participants in nonblinded experiments change their behavior in response to the experimental assignment to either intervention or control arm (such as compensatory rivalry or resentful demoralization); (4) they are often well suited to generate causal evidence on long-term health outcomes of an intervention, as well as nonhealth outcomes such as economic and social consequences; and (5) they can often generate evidence faster and at lower cost than experiments and other intervention studies. Copyright © 2017 Elsevier Inc. All rights reserved.

[Causal inference in medicine--a historical view in epidemiology].

PubMed

Tsuda, T; Babazono, A; Mino, Y; Matsuoka, H; Yamamoto, E

1996-07-01

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.

Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Outcome expectancy and self-efficacy: theoretical implications of an unresolved contradiction.

PubMed

Williams, David M

2010-11-01

According to self-efficacy theory, self-efficacy--defined as perceived capability to perform a behavior--causally influences expected outcomes of behavior, but not vice versa. However, research has shown that expected outcomes causally influence self-efficacy judgments, and some authors have argued that this relationship invalidates self-efficacy theory. Bandura has rebutted those arguments saying that self-efficacy judgments are not invalidated when influenced by expected outcomes. This article focuses on a contradiction in Bandura's rebuttal. Specifically, Bandura has argued (a) expected outcomes cannot causally influence self-efficacy, but (b) self-efficacy judgments remain valid when causally influenced by expected outcomes. While the debate regarding outcome expectancies and self-efficacy has subsided in recent years, the inattention to this contradiction has led to a disproportionate focus on self-efficacy as a causal determinant of behavior at the expense of expected outcomes.

Causality and Causal Inference in Social Work: Quantitative and Qualitative Perspectives

PubMed Central

Palinkas, Lawrence A.

2015-01-01

Achieving the goals of social work requires matching a specific solution to a specific problem. Understanding why the problem exists and why the solution should work requires a consideration of cause and effect. However, it is unclear whether it is desirable for social workers to identify cause and effect, whether it is possible for social workers to identify cause and effect, and, if so, what is the best means for doing so. These questions are central to determining the possibility of developing a science of social work and how we go about doing it. This article has four aims: (1) provide an overview of the nature of causality; (2) examine how causality is treated in social work research and practice; (3) highlight the role of quantitative and qualitative methods in the search for causality; and (4) demonstrate how both methods can be employed to support a “science” of social work. PMID:25821393

Causal Superlearning Arising from Interactions Among Cues

PubMed Central

Urushihara, Kouji; Miller, Ralph R.

2017-01-01

Superconditioning refers to supernormal responding to a conditioned stimulus (CS) that sometimes occurs in classical conditioning when the CS is paired with an unconditioned stimulus (US) in the presence of a conditioned inhibitor for that US. In the present research, we conducted four experiments to investigate causal superlearning, a phenomenon in human causal learning analogous to superconditioning. Experiment 1 demonstrated superlearning relative to appropriate control conditions. Experiment 2 showed that superlearning wanes when the number of cues used in an experiment is relatively large. Experiment 3 determined that even when relatively many cues are used, superlearning can be observed provided testing is conducted immediately after training, which is problematic for explanations by most contemporary learning theories. Experiment 4 found that ratings of a superlearning cue are weaker than those to the training excitor which gives basis to the conditioned inhibitor-like causal preventor used during causal superlearning training. This is inconsistent with the prediction by propositional reasoning accounts of causal cue competition, but is readily explained by associative learning models. In sum, the current experiments revealed some weaknesses of both the associative and propositional reasoning models with respect to causal superlearning. PMID:28383940

Accounting for occurrences: an explanation for some novel tendencies in causal judgment from contingency information.

PubMed

White, Peter A

2009-06-01

Contingency information is information about empirical associations between possible causes and outcomes. In the present research, it is shown that, under some circumstances, there is a tendency for negative contingencies to lead to positive causal judgments and for positive contingencies to lead to negative causal judgments. If there is a high proportion of instances in which a candidate cause (CC) being judged is present, these tendencies are predicted by weighted averaging models of causal judgment. If the proportion of such instances is low, the predictions of weighted averaging models break down. It is argued that one of the main aims of causal judgment is to account for occurrences of the outcome. Thus, a CC is not given a high causal judgment if there are few or no occurrences of it, regardless of the objective contingency. This argument predicts that, if there is a low proportion of instances in which a CC is present, causal judgments are determined mainly by the number of Cell A instances (i.e., CC present, outcome occurs), and that this explains why weighted averaging models fail to predict judgmental tendencies under these circumstances. Experimental results support this argument.

Kant on causal laws and powers.

PubMed

Henschen, Tobias

2014-12-01

The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life

PubMed Central

Reininghaus, Ulrich; Depp, Colin A.; Myin-Germeys, Inez

2016-01-01

Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals’ daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864

Assessing natural direct and indirect effects through multiple pathways.

PubMed

Lange, Theis; Rasmussen, Mette; Thygesen, Lau Caspar

2014-02-15

Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190-195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas).

Investigating causal associations between use of nicotine, alcohol, caffeine and cannabis: a two-sample bidirectional Mendelian randomization study.

PubMed

Verweij, Karin J H; Treur, Jorien L; Vink, Jacqueline M

2018-07-01

Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine and cannabis use. Two-sample MR was employed to estimate bidirectional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week) and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy. Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these were not supported by the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing. Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine and cannabis use. © 2018 Society for the Study of Addiction.

The influence of the number of relevant causes on the processing of covariation information in causal reasoning.

PubMed

Kim, Kyungil; Markman, Arthur B; Kim, Tae Hoon

2016-11-01

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.

Post-traumatic stress disorder and cardiometabolic disease: improving causal inference to inform practice.

PubMed

Koenen, K C; Sumner, J A; Gilsanz, P; Glymour, M M; Ratanatharathorn, A; Rimm, E B; Roberts, A L; Winning, A; Kubzansky, L D

2017-01-01

Post-traumatic stress disorder (PTSD) has been declared 'a life sentence' based on evidence that the disorder leads to a host of physical health problems. Some of the strongest empirical research - in terms of methodology and findings - has shown that PTSD predicts higher risk of cardiometabolic diseases, specifically cardiovascular disease (CVD) and type 2 diabetes (T2D). Despite mounting evidence, PTSD is not currently acknowledged as a risk factor by cardiovascular or endocrinological medicine. This view is unlikely to change absent compelling evidence that PTSD causally contributes to cardiometabolic disease. This review suggests that with developments in methods for epidemiological research and the rapidly expanding knowledge of the behavioral and biological effects of PTSD the field is poised to provide more definitive answers to questions of causality. First, we discuss methods to improve causal inference using the observational data most often used in studies of PTSD and health, with particular reference to issues of temporality and confounding. Second, we consider recent work linking PTSD with specific behaviors and biological processes, and evaluate whether these may plausibly serve as mechanisms by which PTSD leads to cardiometabolic disease. Third, we evaluate how looking more comprehensively into the PTSD phenotype provides insight into whether specific aspects of PTSD phenomenology are particularly relevant to cardiometabolic disease. Finally, we discuss new areas of research that are feasible and could enhance understanding of the PTSD-cardiometabolic relationship, such as testing whether treatment of PTSD can halt or even reverse the cardiometabolic risk factors causally related to CVD and T2D.

Causal inference with missing exposure information: Methods and applications to an obstetric study.

PubMed

Zhang, Zhiwei; Liu, Wei; Zhang, Bo; Tang, Li; Zhang, Jun

2016-10-01

Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and for causal inference with complete data (i.e. no unmeasured confounding and another positivity assumption). These methods involve three models: one for treatment assignment, one for the dependence of outcome on treatment and covariates, and one for the missing data mechanism. In general, consistent estimation of causal quantities requires correct specification of at least two of the three models, although there may be some flexibility as to which two models need to be correct. Such flexibility is afforded by doubly robust estimators adapted from the missing covariates literature and the literature on causal inference with complete data, and by a newly developed triply robust estimator that is consistent if any two of the three models are correct. The methods are applied to the Consortium on Safe Labor data and compared in a simulation study mimicking the Consortium on Safe Labor. © The Author(s) 2013.

Comparison of six methods for the detection of causality in a bivariate time series

NASA Astrophysics Data System (ADS)

Krakovská, Anna; Jakubík, Jozef; Chvosteková, Martina; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-04-01

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

Undertaking Experiments in Social Sciences: Sequential, Multiple Time Series Designs for Consideration

ERIC Educational Resources Information Center

Phan, Huy P.; Ngu, Bing H.

2017-01-01

In social sciences, the use of stringent methodological approaches is gaining increasing emphasis. Researchers have recognized the limitations of cross-sectional, non-manipulative data in the study of causality. True experimental designs, in contrast, are preferred as they represent rigorous standards for achieving causal flows between variables.…

Causality: School Libraries and Student Success (CLASS). White Paper

ERIC Educational Resources Information Center

American Association of School Librarians, 2014

2014-01-01

On April 11 and 12, 2014, the American Association of School Librarians (AASL) held "Causality: School Libraries and Student Success" (CLASS), an IMLS-funded national forum. Dr. Thomas Cook, one of the most influential methodologists in education research, and a five member panel of expert scholars and practitioners led 50 established…

School Connectedness and Chinese Adolescents' Sleep Problems: A Cross-Lagged Panel Analysis

ERIC Educational Resources Information Center

Bao, Zhenzhou; Chen, Chuansheng; Zhang, Wei; Jiang, Yanping; Zhu, Jianjun; Lai, Xuefen

2018-01-01

Background: Although previous research indicates an association between school connectedness and adolescents' sleep quality, its causal direction has not been determined. This study used a 2-wave cross-lagged panel analysis to explore the likely causal direction between these 2 constructs. Methods: Participants were 888 Chinese adolescents (43.80%…

Causal Inferences with Group Based Trajectory Models

ERIC Educational Resources Information Center

Haviland, Amelia M.; Nagin, Daniel S.

2005-01-01

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the…

Comments: Causal Interpretations of Mediation Effects

ERIC Educational Resources Information Center

Jo, Booil; Stuart, Elizabeth A.

2012-01-01

The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…

Causal Indicator Models Have Nothing to Do with Measurement

ERIC Educational Resources Information Center

Howell, Roy D.; Breivik, Einar

2016-01-01

In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…

Predicting Teacher Value-Added Results in Non-Tested Subjects Based on Confounding Variables: A Multinomial Logistic Regression

ERIC Educational Resources Information Center

Street, Nathan Lee

2017-01-01

Teacher value-added measures (VAM) are designed to provide information regarding teachers' causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous…

Leveraging Fourth and Sixth Graders' Experiences to Reveal Understanding of the Forms and Features of Distributed Causality

ERIC Educational Resources Information Center

Grotzer, Tina A.; Derbiszewska, Katarzyna; Solis, S. Lynneth

2017-01-01

Research has focused on students' difficulties understanding phenomena in which agency is distributed across actors whose individual-level behaviors converge to result in collective outcomes. Building on Levy and Wilensky (2008), this study identified features of distributed causality students understand and that may offer affordances for…

Investigating the File Drawer Problem in Causal Effects Studies in Science Education

ERIC Educational Resources Information Center

Taylor, Joseph; Kowalski, Susan; Stuhlsatz, Molly; Wilson, Christopher; Spybrook, Jessaca

2013-01-01

The purpose of this paper is to use both conceptual and statistical approaches to explore publication bias in recent causal effects studies in science education, and to draw from this exploration implications for researchers, journal reviewers, and journal editors. This paper fills a void in the "science education" literature as no…

Within-Teacher Variation of Causal Attributions of Low Achieving Students

ERIC Educational Resources Information Center

Jager, Lieke; Denessen, Eddie

2015-01-01

In teacher research, causal attributions of low achievement have been proven to be predictive of teachers' efforts to provide optimal learning contexts for all students. In most studies, however, attributions have been studied as a between-teacher variable rather than a within-teacher variable assuming that teachers' responses to low achievement…

Combined Functional and Causal Connectivity Analyses of Language Networks in Children: A Feasibility Study

ERIC Educational Resources Information Center

Wilke, Marko; Lidzba, Karen; Krageloh-Mann, Ingeborg

2009-01-01

Instead of assessing activation in distinct brain regions, approaches to investigating the networks underlying distinct brain functions have come into the focus of neuroscience research. Here, we provide a completely data-driven framework for assessing functional and causal connectivity in functional magnetic resonance imaging (fMRI) data,…

The Effects of Affirmative Quality Feedback on Low Socio-Economic Students' Zone of Proximal Development Reading Gains (ZPDRL): A Causal-Comparative Study

ERIC Educational Resources Information Center

Prescott, Sharon H.

2010-01-01

The purpose of this study was to explore upper elementary reading classes in a low socio-economic area to determine the effects frequent praise, both academically and socially, have on the zone of proximal development in reading (ZPD[subscript RL], Renaissance Learning, 2006). A causal-comparative study was utilized by observing two groups of…

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis.

PubMed

Frewen, Paul A; Schmittmann, Verena D; Bringmann, Laura F; Borsboom, Denny

2013-01-01

Previous research demonstrates that posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame are frequently co-occurring problems that may be causally related. The present study utilized Perceived Causal Relations (PCR) scaling in order to assess participants' own attributions concerning whether and to what degree these co-occurring problems may be causally interrelated. 288 young adults rated the frequency and respective PCR scores associating their symptoms of posttraumatic reexperiencing, depression, anxiety, and guilt-shame. PCR scores were found to moderate associations between the frequency of posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame. Network analyses showed that the number of feedback loops between PCR scores was positively associated with symptom frequencies. Results tentatively support the interpretation of PCR scores as moderators of the association between different psychological problems, and lend support to the hypothesis that increased symptom frequencies are observed in the presence of an increased number of causal feedback loops between symptoms. Additionally, a perceived causal role for the reexperiencing of traumatic memories in exacerbating emotional disturbance was identified.

Does partial Granger causality really eliminate the influence of exogenous inputs and latent variables?

PubMed

Roelstraete, Bjorn; Rosseel, Yves

2012-04-30

Partial Granger causality was introduced by Guo et al. (2008) who showed that it could better eliminate the influence of latent variables and exogenous inputs than conditional G-causality. In the recent literature we can find some reviews and applications of this type of Granger causality (e.g. Smith et al., 2011; Bressler and Seth, 2010; Barrett et al., 2010). These articles apparently do not take into account a serious flaw in the original work on partial G-causality, being the negative F values that were reported and even proven to be plausible. In our opinion, this undermines the credibility of the obtained results and thus the validity of the approach. Our study is aimed to further validate partial G-causality and to find an answer why negative partial Granger causality estimates were reported. Time series were simulated from the same toy model as used in the original paper and partial and conditional causal measures were compared in the presence of confounding variables. Inference was done parametrically and using non-parametric block bootstrapping. We counter the proof that partial Granger F values can be negative, but the main conclusion of the original article remains. In the presence of unknown latent and exogenous influences, it appears that partial G-causality will better eliminate their influence than conditional G-causality, at least when non-parametric inference is used. Copyright © 2012 Elsevier B.V. All rights reserved.

Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

PubMed

Wang, Zhe; Alahmadi, Ahmed; Zhu, David C; Li, Tongtong

2016-05-01

This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

Exploring the relationship between child physical abuse and adult dating violence using a causal inference approach in an emerging adult population in South Korea.

PubMed

Jennings, Wesley G; Park, MiRang; Richards, Tara N; Tomsich, Elizabeth; Gover, Angela; Powers, Ráchael A

2014-12-01

Child maltreatment is one of the most commonly examined risk factors for violence in dating relationships. Often referred to as the intergenerational transmission of violence or cycle of violence, a fair amount of research suggests that experiencing abuse during childhood significantly increases the likelihood of involvement in violent relationships later, but these conclusions are primarily based on correlational research designs. Furthermore, the majority of research linking childhood maltreatment and dating violence has focused on samples of young people from the United States. Considering these limitations, the current study uses a rigorous, propensity score matching approach to estimate the causal effect of experiencing child physical abuse on adult dating violence among a large sample of South Korean emerging adults. Results indicate that the link between child physical abuse and adult dating violence is spurious rather than causal. Study limitations and implications are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Understanding social forces involved in diabetes outcomes: a systems science approach to quality-of-life research.

PubMed

Lounsbury, David W; Hirsch, Gary B; Vega, Chawntel; Schwartz, Carolyn E

2014-04-01

The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example. We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage. These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation. The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.

Social isolation, loneliness and health in old age: a scoping review.

PubMed

Courtin, Emilie; Knapp, Martin

2017-05-01

The health and well-being consequences of social isolation and loneliness in old age are increasingly being recognised. The purpose of this scoping review was to take stock of the available evidence and to highlight gaps and areas for future research. We searched nine databases for empirical papers investigating the impact of social isolation and/or loneliness on a range of health outcomes in old age. Our search, conducted between July and September 2013 yielded 11,736 articles, of which 128 items from 15 countries were included in the scoping review. Papers were reviewed, with a focus on the definitions and measurements of the two concepts, associations and causal mechanisms, differences across population groups and interventions. The evidence is largely US-focused, and loneliness is more researched than social isolation. A recent trend is the investigation of the comparative effects of social isolation and loneliness. Depression and cardiovascular health are the most often researched outcomes, followed by well-being. Almost all (but two) studies found a detrimental effect of isolation or loneliness on health. However, causal links and mechanisms are difficult to demonstrate, and further investigation is warranted. We found a paucity of research focusing on at-risk sub-groups and in the area of interventions. Future research should aim to better link the evidence on the risk factors for loneliness and social isolation and the evidence on their impact on health. © 2015 John Wiley & Sons Ltd.

Examination of Icing Induced Loss of Control and Its Mitigations

NASA Technical Reports Server (NTRS)

Reehorst, Andrew L.; Addy, Harold E., Jr.; Colantonio, Renato O.

2010-01-01

Factors external to the aircraft are often a significant causal factor in loss of control (LOC) accidents. In today s aviation world, very few accidents stem from a single cause and typically have a number of causal factors that culminate in a LOC accident. Very often the "trigger" that initiates an accident sequence is an external environment factor. In a recent NASA statistical analysis of LOC accidents, aircraft icing was shown to be the most common external environmental LOC causal factor for scheduled operations. When investigating LOC accident or incidents aircraft icing causal factors can be categorized into groups of 1) in-flight encounter with super-cooled liquid water clouds, 2) take-off with ice contamination, or 3) in-flight encounter with high concentrations of ice crystals. As with other flight hazards, icing induced LOC accidents can be prevented through avoidance, detection, and recovery mitigations. For icing hazards, avoidance can take the form of avoiding flight into icing conditions or avoiding the hazard of icing by making the aircraft tolerant to icing conditions. Icing detection mitigations can take the form of detecting icing conditions or detecting early performance degradation caused by icing. Recovery from icing induced LOC requires flight crew or automated systems capable of accounting for reduced aircraft performance and degraded control authority during the recovery maneuvers. In this report we review the icing induced LOC accident mitigations defined in a recent LOC study and for each mitigation describe a research topic required to enable or strengthen the mitigation. Many of these research topics are already included in ongoing or planned NASA icing research activities or are being addressed by members of the icing research community. These research activities are described and the status of the ongoing or planned research to address the technology needs is discussed

Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours.

PubMed

Treur, Jorien L; Gibson, Mark; Taylor, Amy E; Rogers, Peter J; Munafò, Marcus R

2018-04-22

Observationally, higher caffeine consumption is associated with poorer sleep and insomnia. We investigated whether these associations are a result of shared genetic risk factors and/or (possibly bidirectional) causal effects. Summary-level data were available from genome-wide association studies on caffeine intake (n = 91 462), plasma caffeine and caffeine metabolic rate (n = 9876), sleep duration and chronotype (being a "morning" versus an "evening" person) (n = 128 266), and insomnia complaints (n = 113 006). First, genetic correlations were calculated, reflecting the extent to which genetic variants influencing caffeine consumption and those influencing sleep overlap. Next, causal effects were estimated with bidirectional, two-sample Mendelian randomization. This approach utilizes the genetic variants most robustly associated with an exposure variable as an "instrument" to test causal effects. Estimates from individual variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR-Egger regression. We found no clear evidence for a genetic correlation between caffeine intake and sleep duration (rg = 0.000, p = .998), chronotype (rg = 0.086, p = .192) or insomnia complaints (rg = -0.034, p = .700). For plasma caffeine and caffeine metabolic rate, genetic correlations could not be calculated because of the small sample size. Mendelian randomization did not support causal effects of caffeine intake on sleep, or vice versa. There was weak evidence that higher plasma caffeine levels causally decrease the odds of being a morning person. Although caffeine may acutely affect sleep when taken shortly before bedtime, our findings suggest that a sustained pattern of high caffeine consumption is more likely to be associated with poorer sleep through shared environmental factors. Future research should identify such environments, which could aid the development of interventions to improve sleep. © 2018 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

Causality and headache triggers

PubMed Central

Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

2013-01-01

Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

From correlates to causes: can quasi-experimental studies and statistical innovations bring us closer to identifying the causes of antisocial behavior?

PubMed

Jaffee, Sara R; Strait, Luciana B; Odgers, Candice L

2012-03-01

Longitudinal, epidemiological studies have identified robust risk factors for youth antisocial behavior, including harsh and coercive discipline, maltreatment, smoking during pregnancy, divorce, teen parenthood, peer deviance, parental psychopathology, and social disadvantage. Nevertheless, because this literature is largely based on observational studies, it remains unclear whether these risk factors have truly causal effects. Identifying causal risk factors for antisocial behavior would be informative for intervention efforts and for studies that test whether individuals are differentially susceptible to risk exposures. In this article, we identify the challenges to causal inference posed by observational studies and describe quasi-experimental methods and statistical innovations that may move researchers beyond discussions of risk factors to allow for stronger causal inference. We then review studies that used these methods, and we evaluate whether robust risk factors identified from observational studies are likely to play a causal role in the emergence and development of youth antisocial behavior. There is evidence of causal effects for most of the risk factors we review. However, these effects are typically smaller than those reported in observational studies, suggesting that familial confounding, social selection, and misidentification might also explain some of the association between risk exposures and antisocial behavior. For some risk factors (e.g., smoking during pregnancy, parent alcohol problems), the evidence is weak that they have environmentally mediated effects on youth antisocial behavior. We discuss the implications of these findings for intervention efforts to reduce antisocial behavior and for basic research on the etiology and course of antisocial behavior.

Interpretation of correlations in clinical research.

PubMed

Hung, Man; Bounsanga, Jerry; Voss, Maren Wright

2017-11-01

Critically analyzing research is a key skill in evidence-based practice and requires knowledge of research methods, results interpretation, and applications, all of which rely on a foundation based in statistics. Evidence-based practice makes high demands on trained medical professionals to interpret an ever-expanding array of research evidence. As clinical training emphasizes medical care rather than statistics, it is useful to review the basics of statistical methods and what they mean for interpreting clinical studies. We reviewed the basic concepts of correlational associations, violations of normality, unobserved variable bias, sample size, and alpha inflation. The foundations of causal inference were discussed and sound statistical analyses were examined. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias. Recent published studies in the medical field provide evidence of causal assertion overreach drawn from correlational findings. The findings present a primer on the assumptions and nature of correlational methods of analysis and urge clinicians to exercise appropriate caution as they critically analyze the evidence before them and evaluate evidence that supports practice. Critically analyzing new evidence requires statistical knowledge in addition to clinical knowledge. Studies can overstate relationships, expressing causal assertions when only correlational evidence is available. Failure to account for the effect of sample size in the analyses tends to overstate the importance of predictive variables. It is important not to overemphasize the statistical significance without consideration of effect size and whether differences could be considered clinically meaningful.

Associations between causal attributions and personal stigmatizing attitudes in untreated persons with current mental health problems.

PubMed

Stolzenburg, Susanne; Freitag, Simone; Schmidt, Silke; Schomerus, Georg

2018-02-01

Past research has shown that among the general public, certain causal explanations like biomedical causes are associated with stronger desire for social distance from persons with mental illness. Aim of this study was to find out how different causal attributions of persons with untreated mental health problems regarding their own complaints are associated with stigmatizing attitudes, anticipated self-stigma when seeking help and perceived stigma-stress. Altogether, 207 untreated persons with a current depressive syndrome were interviewed. Biomedical causes, but also belief in childhood trauma or unhealthy behavior as a cause of the problem, were associated with stronger personal stigma and with more stigma-stress. Similarities and differences to findings among the general population and implications for future research are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

A Causal Analysis of Observed Declines in Managed Honey Bees (Apis mellifera)

PubMed Central

Staveley, Jane P.; Law, Sheryl A.; Fairbrother, Anne; Menzie, Charles A.

2013-01-01

The European honey bee (Apis mellifera) is a highly valuable, semi-free-ranging managed agricultural species. While the number of managed hives has been increasing, declines in overwinter survival, and the onset of colony collapse disorder in 2006, precipitated a large amount of research on bees' health in an effort to isolate the causative factors. A workshop was convened during which bee experts were introduced to a formal causal analysis approach to compare 39 candidate causes against specified criteria to evaluate their relationship to the reduced overwinter survivability observed since 2006 of commercial bees used in the California almond industry. Candidate causes were categorized as probable, possible, or unlikely; several candidate causes were categorized as indeterminate due to lack of information. Due to time limitations, a full causal analysis was not completed at the workshop. In this article, examples are provided to illustrate the process and provide preliminary findings, using three candidate causes. Varroa mites plus viruses were judged to be a “probable cause” of the reduced survival, while nutrient deficiency was judged to be a “possible cause.” Neonicotinoid pesticides were judged to be “unlikely” as the sole cause of this reduced survival, although they could possibly be a contributing factor. PMID:24363549

Education and health knowledge: evidence from UK compulsory schooling reform.

PubMed

Johnston, David W; Lordan, Grace; Shields, Michael A; Suziedelyte, Agne

2015-02-01

We investigate if there is a causal link between education and health knowledge using data from the 1984/85 and 1991/92 waves of the UK Health and Lifestyle Survey (HALS). Uniquely, the survey asks respondents what they think are the main causes of ten common health conditions, and we compare these answers to those given by medical professionals to form an index of health knowledge. For causal identification we use increases in the UK minimum school leaving age in 1947 (from 14 to 15) and 1972 (from 15 to 16) to provide exogenous variation in education. These reforms predominantly induced adolescents who would have left school to stay for one additionally mandated year. OLS estimates suggest that education significantly increases health knowledge, with a one-year increase in schooling increasing the health knowledge index by 15% of a standard deviation. In contrast, estimates from instrumental-variable models show that increased schooling due to the education reforms did not significantly affect health knowledge. This main result is robust to numerous specification tests and alternative formulations of the health knowledge index. Further research is required to determine whether there is also no causal link between higher levels of education - such as post-school qualifications - and health knowledge. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Pervasive Problem With Placebos in Psychology: Why Active Control Groups Are Not Sufficient to Rule Out Placebo Effects.

PubMed

Boot, Walter R; Simons, Daniel J; Stothart, Cary; Stutts, Cassie

2013-07-01

To draw causal conclusions about the efficacy of a psychological intervention, researchers must compare the treatment condition with a control group that accounts for improvements caused by factors other than the treatment. Using an active control helps to control for the possibility that improvement by the experimental group resulted from a placebo effect. Although active control groups are superior to "no-contact" controls, only when the active control group has the same expectation of improvement as the experimental group can we attribute differential improvements to the potency of the treatment. Despite the need to match expectations between treatment and control groups, almost no psychological interventions do so. This failure to control for expectations is not a minor omission-it is a fundamental design flaw that potentially undermines any causal inference. We illustrate these principles with a detailed example from the video-game-training literature showing how the use of an active control group does not eliminate expectation differences. The problem permeates other interventions as well, including those targeting mental health, cognition, and educational achievement. Fortunately, measuring expectations and adopting alternative experimental designs makes it possible to control for placebo effects, thereby increasing confidence in the causal efficacy of psychological interventions. © The Author(s) 2013.

Models and mosaics: investigating cross-cultural differences in risk perception and risk preference.

PubMed

Weber, E U; Hsee, C K

1999-12-01

In this article, we describe a multistudy project designed to explain observed cross-national differences in risk taking between respondents from the People's Republic of China and the United States. Using this example, we develop the following recommendations for cross-cultural investigations. First, like all psychological research, cross-cultural studies should be model based. Investigators should commit themselves to a model of the behavior under study that explicitly specifies possible causal constructs or variables hypothesized to influence the behavior, as well as the relationship between those variables, and allows for individual, group, or cultural differences in the value of these variables or in the relationship between them. This moves the focus from a simple demonstration of cross-national differences toward a prediction of the behavior, including its cross-national variation. Ideally, the causal construct hypothesized and shown to differ between cultures should be demonstrated to serve as a moderator or a mediator between culture and observed behavioral differences. Second, investigators should look for converging evidence for hypothesized cultural effects on behavior by looking at multiple dependent variables and using multiple methodological approaches. Thus, the data collection that will allow for the establishment of conclusive causal connections between a cultural variable and some target behavior can be compared with the creation of a mosaic.

Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

PubMed

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

Causal implications of viscous damping in compressible fluid flows

PubMed

Jordan; Meyer; Puri

2000-12-01

Classically, a compressible, isothermal, viscous fluid is regarded as a mathematical continuum and its motion is governed by the linearized continuity, Navier-Stokes, and state equations. Unfortunately, solutions of this system are of a diffusive nature and hence do not satisfy causality. However, in the case of a half-space of fluid set to motion by a harmonically vibrating plate the classical equation of motion can, under suitable conditions, be approximated by the damped wave equation. Since this equation is hyperbolic, the resulting solutions satisfy causal requirements. In this work the Laplace transform and other analytical and numerical tools are used to investigate this apparent contradiction. To this end the exact solutions, as well as their special and limiting cases, are found and compared for the two models. The effects of the physical parameters on the solutions and associated quantities are also studied. It is shown that propagating wave fronts are only possible under the hyperbolic model and that the concept of phase speed has different meanings in the two formulations. In addition, discontinuities and shock waves are noted and a physical system is modeled under both formulations. Overall, it is shown that the hyperbolic form gives a more realistic description of the physical problem than does the classical theory. Lastly, a simple mechanical analog is given and connections to viscoelastic fluids are noted. In particular, the research presented here supports the notion that linear compressible, isothermal, viscous fluids can, at least in terms of causality, be better characterized as a type of viscoelastic fluid.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.

PubMed

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-04-01

Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. Copyright © 2016 White et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

PubMed Central

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-01-01

Summary Background Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. Methods We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. Findings In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04–1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08–1·29), 1·10 (1·00–1·22), and 1·05 (0·92–1·20), respectively, per 1 SD increment in plasma urate. Interpretation Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Funding UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. PMID:26781229

Wilhelm Troll (1897 - 1978): idealistic morphology, physics, and phylogenetics.

PubMed

Rieppel, Olivier

2011-01-01

Idealistic morphology as articulated by the botanist Wilhelm Troll, the main target of the critique voiced by the early phylogeneticists, was firmly embedded in its contemporary scientific, cultural, and political context. Troll appealed to theoretical developments in contemporary physics in support of his research program. He understood burgeoning quantum mechanics not only to threaten the unity of physics, but also the validity of the principle of causality. Troll used this insight in support of his claim of a dualism in biology, relegating the causal-analytical approach to physiology, while rejuvenating the Goethean paradigm in comparative morphology. This embedded idealistic morphology in the völkisch tradition that characterized German culture during the Weimar Republic and its aftermath. In contrast, the contemporary phylogeneticists anchored their research program in the rise of logical positivism and in Darwin's principle of natural selection. This, in turn, brought phylogenetic systematists of the late 1930s and early 1940s into the orbit of national-socialist racial theory and eugenics. In conclusion, the early debate between idealistic morphologists and phylogenetic systematists was not only ideologically tainted, but also implied a philosophical impasse that is best characterized as a conflict between the Goethean and Newtonian paradigm of natural science.

Design and analysis of post-marketing research.

PubMed

Zhou, Xiao-Hua Andrew; Yang, Wei

2013-07-01

A post-marketing study is an integral part of research that helps to ensure a favorable risk-benefit profile for approved drugs used in the market. Because most of post-marketing studies use observational designs, which are liable to confounding, estimation of the causal effect of a drug versus a comparative one is very challenging. This article focuses on methodological issues of importance in designing and analyzing studies to evaluate the safety of marketed drugs, especially marketed traditional Chinese medicine (TCM) products. Advantages and limitations of the current designs and analytic methods for postmarketing studies are discussed, and recommendations are given for improving the validity of postmarketing studies in TCM products.

Seeing through rose-colored glasses: How optimistic expectancies guide visual attention

PubMed Central

Bristle, Mirko; Aue, Tatjana

2018-01-01

Optimism bias and positive attention bias have important highly similar implications for mental health but have only been examined in isolation. Investigating the causal relationships between these biases can improve the understanding of their underlying cognitive mechanisms, leading to new directions in neurocognitive research and revealing important information about normal functioning as well as the development, maintenance, and treatment of psychological diseases. In the current project, we hypothesized that optimistic expectancies can exert causal influences on attention deployment. To test this causal relation, we conducted two experiments in which we manipulated optimistic and pessimistic expectancies regarding future rewards and punishments. In a subsequent visual search task, we examined participants’ attention to positive (i.e., rewarding) and negative (i.e., punishing) target stimuli, measuring their eye gaze behavior and reaction times. In both experiments, participants’ attention was guided toward reward compared with punishment when optimistic expectancies were induced. Additionally, in Experiment 2, participants’ attention was guided toward punishment compared with reward when pessimistic expectancies were induced. However, the effect of optimistic (rather than pessimistic) expectancies on attention deployment was stronger. A key characteristic of optimism bias is that people selectively update expectancies in an optimistic direction, not in a pessimistic direction, when receiving feedback. As revealed in our studies, selective attention to rewarding versus punishing evidence when people are optimistic might explain this updating asymmetry. Thus, the current data can help clarify why optimistic expectancies are difficult to overcome. Our findings elucidate the cognitive mechanisms underlying optimism and attention bias, which can yield a better understanding of their benefits for mental health. PMID:29466420

Seeing through rose-colored glasses: How optimistic expectancies guide visual attention.

PubMed

Kress, Laura; Bristle, Mirko; Aue, Tatjana

2018-01-01

Optimism bias and positive attention bias have important highly similar implications for mental health but have only been examined in isolation. Investigating the causal relationships between these biases can improve the understanding of their underlying cognitive mechanisms, leading to new directions in neurocognitive research and revealing important information about normal functioning as well as the development, maintenance, and treatment of psychological diseases. In the current project, we hypothesized that optimistic expectancies can exert causal influences on attention deployment. To test this causal relation, we conducted two experiments in which we manipulated optimistic and pessimistic expectancies regarding future rewards and punishments. In a subsequent visual search task, we examined participants' attention to positive (i.e., rewarding) and negative (i.e., punishing) target stimuli, measuring their eye gaze behavior and reaction times. In both experiments, participants' attention was guided toward reward compared with punishment when optimistic expectancies were induced. Additionally, in Experiment 2, participants' attention was guided toward punishment compared with reward when pessimistic expectancies were induced. However, the effect of optimistic (rather than pessimistic) expectancies on attention deployment was stronger. A key characteristic of optimism bias is that people selectively update expectancies in an optimistic direction, not in a pessimistic direction, when receiving feedback. As revealed in our studies, selective attention to rewarding versus punishing evidence when people are optimistic might explain this updating asymmetry. Thus, the current data can help clarify why optimistic expectancies are difficult to overcome. Our findings elucidate the cognitive mechanisms underlying optimism and attention bias, which can yield a better understanding of their benefits for mental health.

Abilities of 4-7 Years Old Children to Provide Independent Explanations and Generalisations of Causality

ERIC Educational Resources Information Center

Daugirdiene, Ausra; Petrulyte, Aiste; Brandisauskiene, Agne

2018-01-01

The understanding and generalisation of causality are important thinking abilities, as they form the basis for a person's activity. Researchers exploring these abilities do not have a unified opinion regarding the age of children when they develop causative understanding and its determinant factors (e.g. age, prior knowledge, the content of a…

Causal Attributions of Success and Failure Made by Undergraduate Students in an Introductory-Level Computer Programming Course

ERIC Educational Resources Information Center

Hawi, N.

2010-01-01

The purpose of this research is to identify the causal attributions of business computing students in an introductory computer programming course, in the computer science department at Notre Dame University, Louaize. Forty-five male and female undergraduates who completed the computer programming course that extended for a 13-week semester…

Causal Attribution: A New Scale Developed to Minimize Existing Methodological Problems.

ERIC Educational Resources Information Center

Bull, Kay Sather; Feuquay, Jeffrey P.

In order to facilitate research on the construct of causal attribution, this paper details developmental procedures used to minimize previous deficiencies and proposes a new scale. The first version of the scale was in ipsative form and provided two basic sets of indices: (1) ability, effort, luck, and task difficulty indices in success and…

Causal Factors Influencing Adversity Quotient of Twelfth Grade and Third-Year Vocational Students

ERIC Educational Resources Information Center

Pangma, Rachapoom; Tayraukham, Sombat; Nuangchalerm, Prasart

2009-01-01

Problem statement: The aim of this research was to study the causal factors influencing students' adversity between twelfth grade and third-year vocational students in Sisaket province, Thailand. Six hundred and seventy two of twelfth grade and 376 third-year vocational students were selected by multi-stage random sampling techniques. Approach:…

Cognitive Demand Differences in Causal Inferences: Characters' Plans Are More Difficult to Comprehend than Physical Causation

ERIC Educational Resources Information Center

Shears, Connie; Miller, Vanessa; Ball, Megan; Hawkins, Amanda; Griggs, Janna; Varner, Andria

2007-01-01

Readers may draw knowledge-based inferences to connect sentences in text differently depending on the knowledge domain being accessed. Most prior research has focused on the direction of the causal explanation (predictive vs. backward) without regard to the knowledge domain drawn on to support comprehension. We suggest that less cognitive effort…

Designing and Validating a Language Teacher Attribution Scale: A Structural Equation Modeling Approach

ERIC Educational Resources Information Center

Ghanizadeh, Afsaneh; Ghonsooly, Behzad

2015-01-01

Causal attributions constitute one of the most universal forms of analyzing reality, since they fulfill basic functions in motivation for action. As a theory of causal explanations for success and failure, attribution research has found a natural context in the academic domain. Despite this, it appears that teacher attribution, in particular…

Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…

The Causal Effect of Education on Health: Evidence from the United Kingdom

ERIC Educational Resources Information Center

Silles, Mary A.

2009-01-01

Numerous economic studies have shown a strong positive correlation between health and years of schooling. The question at the centre of this research is whether the correlation between health and education represents a causal relation. This paper uses changes in compulsory schooling laws in the United Kingdom to test this hypothesis. Multiple…

Testing the causal theory of reference.

PubMed

Domaneschi, Filippo; Vignolo, Massimiliano; Di Paola, Simona

2017-04-01

Theories of reference are a crucial research topic in analytic philosophy. Since the publication of Kripke's Naming and Necessity, most philosophers have endorsed the causal/historical theory of reference. The goal of this paper is twofold: (i) to discuss a method for testing experimentally the causal theory of reference for proper names by investigating linguistic usage and (ii) to present the results from two experiments conducted with that method. Data collected in our experiments confirm the causal theory of reference for people proper names and for geographical proper names. A secondary but interesting result is that the semantic domain affects reference assignment: while with people proper names speakers tend to assign the semantic reference, with geographical proper names they are prompted to assign the speaker's reference. Copyright © 2016 Elsevier B.V. All rights reserved.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

The perceived causal structures of smoking: Smoker and non-smoker comparisons

PubMed Central

Lydon, David M; Howard, Matthew C; Wilson, Stephen J; Geier, Charles F

2015-01-01

Despite the detrimental impact of smoking on health, its prevalence remains high. Empirical research has provided insight into the many causes and effects of smoking, yet lay perceptions of smoking remain relatively understudied. The current study used a form of network analysis to gain insight into the causal attributions for smoking of both smoking and non-smoking college students. The analyses resulted in highly endorsed, complex network diagrams that conveyed the perceived causal structures of smoking. Differences in smoker and non-smoker networks emerged with smokers attributing less negative consequences to smoking behaviors. Implications for intervention are discussed. PMID:25690755

Expert explanations of honeybee losses in areas of extensive agriculture in France: Gaucho® compared with other supposed causal factors

NASA Astrophysics Data System (ADS)

Maxim, L.; van der Sluijs, J. P.

2010-01-01

Debates on causality are at the core of controversies as regards environmental changes. The present paper presents a new method for analyzing controversies on causality in a context of social debate and the results of its empirical testing. The case study used is the controversy as regards the role played by the insecticide Gaucho®, compared with other supposed causal factors, in the substantial honeybee (Apis mellifera L.) losses reported to have occurred in France between 1994 and 2004. The method makes use of expert elicitation of the perceived strength of evidence regarding each of Bradford Hill's causality criteria, as regards the link between each of eight possible causal factors identified in attempts to explain each of five signs observed in honeybee colonies. These judgments are elicited from stakeholders and experts involved in the debate, i.e., representatives of Bayer Cropscience, of the Ministry of Agriculture, of the French Food Safety Authority, of beekeepers and of public scientists. We show that the intense controversy observed in confused and passionate public discourses is much less salient when the various arguments are structured using causation criteria. The contradictions between the different expert views have a triple origin: (1) the lack of shared definition and quantification of the signs observed in colonies; (2) the lack of specialist knowledge on honeybees; and (3) the strategic discursive practices associated with the lack of trust between experts representing stakeholders having diverging stakes in the case.

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”☆

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2015-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. PMID:25681017

"Head take you": causal attributions of mental illness in Jamaica.

PubMed

Arthur, Carlotta M; Whitley, Rob

2015-02-01

Causal attributions are a key factor in explanatory models of illness; however, little research on causal attributions of mental illness has been conducted in developing nations in the Caribbean, including Jamaica. Explanatory models of mental illness may be important in understanding illness experience and be a crucial factor in mental health service seeking and utilization. We explored causal attributions of mental illness in Jamaica by conducting 20 focus groups, including 16 community samples, 2 patient samples, and 2 samples of caregivers of patients, with a total of 159 participants. The 5 most commonly endorsed causal attributions of mental illness are discussed: (a) drug-related causes, including ganja (marijuana); (b) biological causes, such as chemical imbalance, familial transmission, and "blood"; (c) psychological causes, including stress and thinking too much; (d) social causes, such as relationship problems and job loss; and (e) spiritual or religious causes, including Obeah. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Causal Relationships Among Time Series of the Lange Bramke Catchment (Harz Mountains, Germany)

NASA Astrophysics Data System (ADS)

Aufgebauer, Britta; Hauhs, Michael; Bogner, Christina; Meesenburg, Henning; Lange, Holger

2016-04-01

Convergent Cross Mapping (CCM) has recently been introduced by Sugihara et al. for the identification and quantification of causal relationships among ecosystem variables. In particular, the method allows to decide on the direction of causality; in some cases, the causality might be bidirectional, indicating a network structure. We extend this approach by introducing a method of surrogate data to obtain confidence intervals for CCM results. We then apply this method to time series from stream water chemistry. Specifically, we analyze a set of eight dissolved major ions from three different catchments belonging to the hydrological monitoring system at the Bramke valley in the Harz Mountains, Germany. Our results demonstrate the potentials and limits of CCM as a monitoring instrument in forestry and hydrology or as a tool to identify processes in ecosystem research. While some networks of causally linked ions can be associated with simple physical and chemical processes, other results illustrate peculiarities of the three studied catchments, which are explained in the context of their special history.

Verification of causal influences of reasoning skills and epistemology on physics conceptual learning

NASA Astrophysics Data System (ADS)

Ding, Lin

2014-12-01

This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills measured by the Classroom Test of Scientific Reasoning, pre- and postepistemological views measured by the Colorado Learning Attitudes about Science Survey, and pre- and postperformance on Newtonian concepts measured by the Force Concept Inventory. Students from a traditionally taught calculus-based introductory mechanics course at a research university participated in the study. Results largely support the postulated causal model and reveal strong influences of reasoning skills and preinstructional epistemology on student conceptual learning gains. Interestingly enough, postinstructional epistemology does not appear to have a significant influence on student learning gains. Moreover, pre- and postinstructional epistemology, although barely different from each other on average, have little causal connection between them.

Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence1234

PubMed Central

Brown, Andrew W; Bohan Brown, Michelle M

2013-01-01

Background: Various intentional and unintentional factors influence beliefs beyond what scientific evidence justifies. Two such factors are research lacking probative value (RLPV) and biased research reporting (BRR). Objective: We investigated the prevalence of RLPV and BRR in research about the proposition that skipping breakfast causes weight gain, which is called the proposed effect of breakfast on obesity (PEBO) in this article. Design: Studies related to the PEBO were synthesized by using a cumulative meta-analysis. Abstracts from these studies were also rated for the improper use of causal language and biased interpretations. In separate analyses, articles that cited an observational study about the PEBO were rated for the inappropriate use of causal language, and articles that cited a randomized controlled trial (RCT) about the PEBO were rated for misleadingly citing the RCT. Results: The current body of scientific knowledge indicates that the PEBO is only presumed true. The observational literature on the PEBO has gratuitously established the association, but not the causal relation, between skipping breakfast and obesity (final cumulative meta-analysis P value <10−42), which is evidence of RLPV. Four examples of BRR are evident in the PEBO literature as follows: 1) biased interpretation of one's own results, 2) improper use of causal language in describing one's own results, 3) misleadingly citing others’ results, and 4) improper use of causal language in citing others’ work. Conclusions: The belief in the PEBO exceeds the strength of scientific evidence. The scientific record is distorted by RLPV and BRR. RLPV is a suboptimal use of collective scientific resources. PMID:24004890

Do biogenetic causal beliefs reduce mental illness stigma in people with mental illness and in mental health professionals? A systematic review.

PubMed

Larkings, Josephine S; Brown, Patricia M

2018-06-01

Viewing mental illness as an 'illness like any other' and promoting biogenetic causes have been explored as a stigma-reduction strategy. The relationship between causal beliefs and mental illness stigma has been researched extensively in the general public, but has gained less attention in more clinically-relevant populations (i.e. people with mental illness and mental health professionals). A systematic review examining whether endorsing biogenetic causes decreases mental illness stigma in people with mental illness and mental health professionals was undertaken using the preferred reporting items for systematic reviews and meta-analyses guidelines. Multiple databases were searched, and studies that explored the relationship between biogenetic causal beliefs and mental illness stigma in people with mental illness or mental health professionals were considered. Studies were included if they focussed on depression, schizophrenia, or mental illness in general, were in English, and had adult participants. The search identified 11 journal articles reporting on 15 studies, which were included in this review. Of these, only two provided evidence that endorsing biogenetic causes was associated with less mental illness stigma in people with mental illness or mental health professionals. The majority of studies in the present review (n = 10) found that biogenetic causal beliefs were associated with increased stigma or negative attitudes towards mental illness. The present review highlights the lack of research exploring the impacts of endorsing biogenetic causes in people with mental illness and mental health professionals. Clinical implications associated with these results are discussed, and suggestions are made for further research that examines the relationship between causal beliefs and treatment variables. © 2017 Australian College of Mental Health Nurses Inc.

Imputation of adverse drug reactions: Causality assessment in hospitals

PubMed Central

Mastroianni, Patricia de Carvalho

2017-01-01

Background & objectives Different algorithms have been developed to standardize the causality assessment of adverse drug reactions (ADR). Although most share common characteristics, the results of the causality assessment are variable depending on the algorithm used. Therefore, using 10 different algorithms, the study aimed to compare inter-rater and multi-rater agreement for ADR causality assessment and identify the most consistent to hospitals. Methods Using ten causality algorithms, four judges independently assessed the first 44 cases of ADRs reported during the first year of implementation of a risk management service in a medium complexity hospital in the state of Sao Paulo (Brazil). Owing to variations in the terminology used for causality, the equivalent imputation terms were grouped into four categories: definite, probable, possible and unlikely. Inter-rater and multi-rater agreement analysis was performed by calculating the Cohen´s and Light´s kappa coefficients, respectively. Results None of the algorithms showed 100% reproducibility in the causal imputation. Fair inter-rater and multi-rater agreement was found. Emanuele (1984) and WHO-UMC (2010) algorithms showed a fair rate of agreement between the judges (k = 0.36). Interpretation & conclusions Although the ADR causality assessment algorithms were poorly reproducible, our data suggest that WHO-UMC algorithm is the most consistent for imputation in hospitals, since it allows evaluating the quality of the report. However, to improve the ability of assessing the causality using algorithms, it is necessary to include criteria for the evaluation of drug-related problems, which may be related to confounding variables that underestimate the causal association. PMID:28166274

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

PubMed Central

Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.

2015-01-01

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318

Robotics: Assessing Its Role in Improving Mathematics Skills for Grades 4 to 5

ERIC Educational Resources Information Center

Laughlin, Sara Rose

2013-01-01

Inspiring and motivating students to pursue science, technology, engineering, and mathematics education continues to be an important educational focus in the United States. Robotics programs are one strategy developed to accomplish this goal. This causal comparative study focused on investigating whether a causal relationship exists between…

Time and Order Effects on Causal Learning

ERIC Educational Resources Information Center

Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M.

2006-01-01

Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…

Participation in Athletics and Female Sexual Risk Behavior: The Evaluation of Four Causal Structures.

ERIC Educational Resources Information Center

Dodge, Tonya; Jaccard, James

2002-01-01

Compared sexual risk behavior of female athletes and nonathletes. Examined mediation, reverse mediation, spurious effects, and moderated causal models, using as potential mediators physical development, educational aspirations, self-esteem, attitudes toward pregnancy, involvement in a romantic relationship, age, ethnicity, and social class. Found…

Core questions in domestication research

PubMed Central

Zeder, Melinda A.

2015-01-01

The domestication of plants and animals is a key transition in human history, and its profound and continuing impacts are the focus of a broad range of transdisciplinary research spanning the physical, biological, and social sciences. Three central aspects of domestication that cut across and unify this diverse array of research perspectives are addressed here. Domestication is defined as a distinctive coevolutionary, mutualistic relationship between domesticator and domesticate and distinguished from related but ultimately different processes of resource management and agriculture. The relative utility of genetic, phenotypic, plastic, and contextual markers of evolving domesticatory relationships is discussed. Causal factors are considered, and two leading explanatory frameworks for initial domestication of plants and animals, one grounded in optimal foraging theory and the other in niche-construction theory, are compared. PMID:25713127

Paradigm shift in consciousness research: the child's self-awareness and abnormalities in autism, ADHD and schizophrenia.

PubMed

Lou, Hans C

2012-02-01

Self-awareness is a pivotal component of any conscious experience and conscious self-regulation of behaviour. A paralimbic network is active, specific and causal in self-awareness. Its regions interact by gamma synchrony. Gamma synchrony develops throughout infancy, childhood and adolescence into adulthood and is regulated by dopamine and other neurotransmitters via GABA interneurons. Major derailments of this network and self-awareness occur in developmental disorders of conscious self-regulation like autism, attention deficit hyperactivity disorder (ADHD) and schizophrenia. Recent research on conscious experience is no longer limited to the study of neural 'correlations' but is increasingly lending itself to the study of causality. This paradigm shift opens new perspectives for understanding the neural mechanisms of the developing self and the causal effects of their disturbance in developmental disorders. © 2011 The Author(s)/Acta Paediatrica © 2011 Foundation Acta Paediatrica.

A critical assessment of the association between postnatal toxoplasmosis and epilepsy in immune-competent patients.

PubMed

Uzorka, J W; Arend, S M

2017-07-01

While postnatal toxoplasmosis in immune-competent patients is generally considered a self-limiting and mild illness, it has been associated with a variety of more severe clinical manifestations. The causal relation with some manifestations, e.g. myocarditis, has been microbiologically proven, but this is not unequivocally so for other reported associations, such as with epilepsy. We aimed to systematically assess causality between postnatal toxoplasmosis and epilepsy in immune-competent patients. A literature search was performed. The Bradford Hill criteria for causality were used to score selected articles for each component of causality. Using an arbitrary but defined scoring system, the maximal score was 15 points (13 for case reports). Of 704 articles, five case reports or series and five case-control studies were selected. The strongest evidence for a causal relation was provided by two case reports and one case-control study, with a maximal causality score of, respectively, 9/13, 10/13 and 10/15. The remaining studies had a median causality score of 7 (range 5-9). No selection bias was identified, but 6/10 studies contained potential confounders (it was unsure whether the infection was pre- or postnatal acquired, or immunodeficiency was not specifically excluded). Based on the evaluation of the available literature, although scanty and of limited quality, a causal relationship between postnatal toxoplasmosis and epilepsy seems possible. More definite proof requires further research, e.g. by performing Toxoplasma serology in all de novo epilepsy cases.

Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

PubMed

Liu, Shao-Hsien; Ulbricht, Christine M; Chrysanthopoulou, Stavroula A; Lapane, Kate L

2016-07-20

Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.

ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

PubMed

Zhang, Kunlin; Chang, Suhua; Cui, Sijia; Guo, Liyuan; Zhang, Liuyan; Wang, Jing

2011-07-01

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.

Illusions of causality at the heart of pseudoscience.

PubMed

Matute, Helena; Yarritu, Ion; Vadillo, Miguel A

2011-08-01

Pseudoscience, superstitions, and quackery are serious problems that threaten public health and in which many variables are involved. Psychology, however, has much to say about them, as it is the illusory perceptions of causality of so many people that needs to be understood. The proposal we put forward is that these illusions arise from the normal functioning of the cognitive system when trying to associate causes and effects. Thus, we propose to apply basic research and theories on causal learning to reduce the impact of pseudoscience. We review the literature on the illusion of control and the causal learning traditions, and then present an experiment as an illustration of how this approach can provide fruitful ideas to reduce pseudoscientific thinking. The experiment first illustrates the development of a quackery illusion through the testimony of fictitious patients who report feeling better. Two different predictions arising from the integration of the causal learning and illusion of control domains are then proven effective in reducing this illusion. One is showing the testimony of people who feel better without having followed the treatment. The other is asking participants to think in causal terms rather than in terms of effectiveness. ©2010 The British Psychological Society.

Relationships between Adolescents' Preferred Sources of Help and Emotional Distress, Ambivalence over Emotional Expression, and Causal Attribution of Symptoms: A Singapore Study

ERIC Educational Resources Information Center

Lee, Boon-Ooi

2009-01-01

Past research has shown that many adolescents with depression and anxiety disorders do not consult mental health professionals. This study examines how emotional distress, ambivalence over emotional expression, and causal attribution of depressive and anxious symptoms are related to adolescents' preferred sources of help for these symptoms. 300…

Knowing When Help Is Needed: A Developing Sense of Causal Complexity

ERIC Educational Resources Information Center

Kominsky, Jonathan F.; Zamm, Anna P.; Keil, Frank C.

2018-01-01

Research on the division of cognitive labor has found that adults and children as young as age 5 are able to find appropriate experts for different causal systems. However, little work has explored how children and adults decide when to seek out expert knowledge in the first place. We propose that children and adults rely (in part) on…

Teachers' Interpersonal Style and Its Relationship to Emotions, Causal Attributions, and Type of Challenging Behaviors Displayed by Students with Intellectual Disabilities

ERIC Educational Resources Information Center

Alevriadou, Anastasia; Pavlidou, Kyriaki

2016-01-01

Teachers' interpersonal style is a new field of research in the study of students with intellectual disabilities and challenging behaviors in school context. In the present study, we investigate emotions and causal attributions of three basic types of challenging behaviors: aggression, stereotypy, and self-injury, in relation to teachers'…

Combining Group-Based Trajectory Modeling and Propensity Score Matching for Causal Inferences in Nonexperimental Longitudinal Data

ERIC Educational Resources Information Center

Haviland, Amelia; Nagin, Daniel S.; Rosenbaum, Paul R.; Tremblay, Richard E.

2008-01-01

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This article describes and applies a method for using observational longitudinal data to make more transparent causal inferences about the…

Word, Thought, and Deed: The Role of Object Categories in Children's Inductive Inferences and Exploratory Play

ERIC Educational Resources Information Center

Schulz, Laura E.; Standing, Holly R.; Bonawitz, Elizabeth B.

2008-01-01

Previous research (e.g., S. A. Gelman & E. M. Markman, 1986; A. Gopnik & D. M. Sobel, 2000) suggests that children can use category labels to make inductive inferences about nonobvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when (a) the property being projected varies…

Contending Claims to Causality: A Critical Review of Mediation Research in HRD

ERIC Educational Resources Information Center

Ghosh, Rajashi; Jacobson, Seth

2016-01-01

Purpose: The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies. Design/methodology/approach: This paper conducts…

Prior knowledge driven Granger causality analysis on gene regulatory network discovery

DOE PAGES

Yao, Shun; Yoo, Shinjae; Yu, Dantong

2015-08-28

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, themore » propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.« less

Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality.

PubMed

Biddle, Stuart J H; García Bengoechea, Enrique; Wiesner, Glen

2017-03-28

Sedentary behaviour (sitting time) has becoming a very popular topic for research and translation since early studies on TV viewing in children in the 1980s. The most studied area for sedentary behaviour health outcomes has been adiposity in young people. However, the literature is replete with inconsistencies. We conducted a systematic review of systematic reviews and meta-analyses to provide a comprehensive analysis of evidence and state-of-the-art synthesis on whether sedentary behaviours are associated with adiposity in young people, and to what extent any association can be considered 'causal'. Searches yielded 29 systematic reviews of over 450 separate papers. We analysed results by observational (cross-sectional and longitudinal) and intervention designs. Small associations were reported for screen time and adiposity from cross-sectional evidence, but associations were less consistent from longitudinal studies. Studies using objective accelerometer measures of sedentary behaviour yielded null associations. Most studies assessed BMI/BMI-z. Interventions to reduce sedentary behaviour produced modest effects for weight status and adiposity. Accounting for effects from sedentary behaviour reduction alone is difficult as many interventions included additional changes in behaviour, such as physical activity and dietary intake. Analysis of causality guided by the classic Bradford Hill criteria concluded that there is no evidence for a causal association between sedentary behaviour and adiposity in youth, although a small dose-response association exists. Associations between sedentary behaviour and adiposity in children and adolescents are small to very small and there is little to no evidence that this association is causal. This remains a complex field with different exposure and outcome measures and research designs. But claims for 'clear' associations between sedentary behaviour and adiposity in youth, and certainly for causality, are premature or misguided.

Time, frequency, and time-varying Granger-causality measures in neuroscience.

PubMed

Cekic, Sezen; Grandjean, Didier; Renaud, Olivier

2018-05-20

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned. Copyright © 2018 John Wiley & Sons, Ltd.

Causal conditionals and counterfactuals

PubMed Central

Frosch, Caren A.; Byrne, Ruth M.J.

2012-01-01

Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874

Comparison of depression level and identity styles between students in Allameh University and Islamic Seminary.

PubMed

Mahdavi, A; Aghaei, M; Besharat, M A; Khaki Seddigh, F; Akbari, S H; Hamidifar, Z

2015-01-01

This research was conducted to compare the depression level and the identity styles between students in Allameh University and Islamic Seminary in Tehran city. The research method was the ex post facto or causal-comparative kind. In this research, all the students of Allameh University and Islamic Seminary were chosen as the research population. Among the statistical population, by using the convenience sampling method, a sample consisting of 100 male students was chosen (50-50 from both universities). Afterwards, the Identity Styles Inventory (ISI-6G) and the Beck Depression Inventory (21 questions) were employed in order to collect the data. By using ANOVA and systematic regression, the collected data were analyzed. The findings of the research indicated that the average values of the normative component (p-value = 0.03) and the depression level (p-value = 0.000) of seminary's students were higher compared to the ones specific for the Allameh's students. Among the various identity styles, commitment style could totally predict 16% of depression variable changes of Allameh's students. Moreover, information and normative styles could totally predict 19% of the depression variable changes of the seminary's students.

Comparison of depression level and identity styles between students in Allameh University and Islamic Seminary

PubMed Central

Mahdavi, A; Aghaei, M; Besharat, MA; Khaki Seddigh, F; Akbari, SH; Hamidifar, Z

2015-01-01

This research was conducted to compare the depression level and the identity styles between students in Allameh University and Islamic Seminary in Tehran city. The research method was the ex post facto or causal-comparative kind. In this research, all the students of Allameh University and Islamic Seminary were chosen as the research population. Among the statistical population, by using the convenience sampling method, a sample consisting of 100 male students was chosen (50-50 from both universities). Afterwards, the Identity Styles Inventory (ISI-6G) and the Beck Depression Inventory (21 questions) were employed in order to collect the data. By using ANOVA and systematic regression, the collected data were analyzed. The findings of the research indicated that the average values of the normative component (p-value = 0.03) and the depression level (p-value = 0.000) of seminary’s students were higher compared to the ones specific for the Allameh’s students. Among the various identity styles, commitment style could totally predict 16% of depression variable changes of Allameh’s students. Moreover, information and normative styles could totally predict 19% of the depression variable changes of the seminary’s students. PMID:28316715

Anticipated motivation for genetic testing among smokers, nonsmokers, and former smokers: an exploratory qualitative study of decision making.

PubMed

Giordimaina, Alicia M; Sheldon, Jane P; Petty, Elizabeth M

2014-01-01

This qualitative study explores the public's interest in genetic testing related to cigarette smoking, comparing the public's motivations with researchers' intentions for this technology. Adult nonsmokers (n=463), former smokers (n=163), and current smokers (n=129) completed an online survey. Within a hypothetical scenario, respondents decided whether they desired genetic testing related to smoking and explained their decision making. A non-parametric Kruskal-Wallis test was used to compare the interest in genetic testing by smoking history group. Inductive content analysis was used to investigate respondents' explanations for their testing decisions. Most nonsmokers (64%) and former smokers (58%) did not want genetic testing. While most current daily smokers were interested in testing (56%), most current occasional smokers were not (52%). Respondents' decision-making explanations were categorized into 3 major themes: Causality, Relevancy and Utility (e.g. personal benefits or harms). The use of causality, relevancy and utility explanations varied by smoking history. Notable perceived benefits of testing included recreation and altruism. Notable perceived harms included fear of fatalistic thoughts and concern about genetic discrimination. Interest in genetic testing was highest among current daily smokers, despite potential utility in other groups. Although respondents' motivations for testing paralleled researchers' intentions of tailoring smoking cessation therapies and increasing motivation to quit or abstain, respondents also raised alternative motivations and fears that healthcare providers would need to address. © 2014 S. Karger AG, Basel.

Disgust and the Politics of Sex: Exposure to a Disgusting Odorant Increases Politically Conservative Views on Sex and Decreases Support for Gay Marriage

PubMed Central

Adams, Thomas G.; Stewart, Patrick A.; Blanchar, John C.

2014-01-01

Disgust has been implicated as a potential causal agent underlying socio-political attitudes and behaviors. Several recent studies have suggested that pathogen disgust may be a causal mechanism underlying social conservatism. However, the specificity of this effect is still in question. The present study tested the effects of disgust on a range of policy preferences to clarify whether disgust is generally implicated in political conservatism across public policy attitudes or is uniquely related to specific content domains. Self-reported socio-political attitudes were compared between participants in two experimental conditions: 1) an odorless control condition, and 2) a disgusting odor condition. In keeping with previous research, the present study showed that exposure to a disgusting odor increased endorsement of socially conservative attitudes related to sexuality. In particular, there was a strong and consistent link between induced disgust and less support for gay marriage. PMID:24798457

Investigation of injury/illness data at a nuclear facility. Part II

DOE PAGES

Cournoyer, Michael E.; Garcia, Vincent E.; Sandoval, Arnold N.; ...

2015-07-01

At Los Alamos National Laboratory (LANL), there are several nuclear facilities, accelerator facilities, radiological facilities, explosives sites, moderate- and high-hazard non-nuclear facilities, biosciences laboratory, etc. The Plutonium Science and Manufacturing Directorate (ADPSM) provides special nuclear material research, process development, technology demonstration, and manufacturing capabilities. ADPSM manages the LANL Plutonium Facility. Within the Radiological Control Area at TA-55 (PF-4), chemical and metallurgical operations with plutonium and other hazardous materials are performed. LANL Health and Safety Programs investigate injury and illness data. In this study, statistically significant trends have been identified and compared for LANL, ADPSM, and PF-4 injury/illness cases. A previouslymore » described output metric is used to measures LANL management progress towards meeting its operational safety objectives and goals. Timelines are used to determine trends in Injury/Illness types. Pareto Charts are used to prioritize causal factors. The data generated from analysis of Injury/Illness data have helped identify and reduce the number of corresponding causal factors.« less

[Selection or Better Service - Why are those with Private Health Insurance Healthier than those Covered by the Public Insurance System?

PubMed

Stauder, J; Kossow, T

2017-03-01

From previous research we know that privately insured people in Germany are healthier than those covered by the compulsory public health insurance system. Whether this difference is due to a selection of healthier people into the private health insurance or a causal effect in the sense that private health insurance better helps their clients to stay in good health than public insurances do is not clear. Using panel regression based on the German Socioeconomic Panel (GSOEP), we show that health status is better for individuals who have bought a private health insurance certificate since 2002 compared to those who remained within the public insurance system. Depending on age at joining the insurance system, the health gap between privately and publicly insured people is widening with time since joining the private insurance system. We argue that these findings point to a causal effect. © Georg Thieme Verlag KG Stuttgart · New York.

Causal Structure Learning over Time: Observations and Interventions

ERIC Educational Resources Information Center

Rottman, Benjamin M.; Keil, Frank C.

2012-01-01

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent--the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal…

Episodic memory and the witness trump card.

PubMed

Henry, Jeremy; Craver, Carl

2018-01-01

We accept Mahr & Csibra's (M&C's) causal claim that episodic memory provides humans with the means for evaluating the veracity of reports about non-occurrent events. We reject their evolutionary argument that this is the proper function of episodic memory. We explore three intriguing implications of the causal claim, for cognitive neuropsychology, comparative psychology, and philosophy.

Attributions for Pride, Anger, and Guilt among Incarcerated Minority Adolescents.

ERIC Educational Resources Information Center

Hudley-Paul, Cynthia A.

Two studies investigate causal attributions among minority adolescents. The first investigates attributions for the emotions of anger, pride, and guilt among 26 incarcerated male adolescents. Relatively few causes are found for anger and guilt, and a larger variety of causes are cited for pride. A follow-up study then compares causal attributions…

Causal Premise Semantics

ERIC Educational Resources Information Center

Kaufmann, Stefan

2013-01-01

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal…

Transitive closure of subsumption and causal relations in a large ontology of radiological diagnosis.

PubMed

Kahn, Charles E

2016-06-01

The Radiology Gamuts Ontology (RGO)-an ontology of diseases, interventions, and imaging findings-was developed to aid in decision support, education, and translational research in diagnostic radiology. The ontology defines a subsumption (is_a) relation between more general and more specific terms, and a causal relation (may_cause) to express the relationship between disorders and their possible imaging manifestations. RGO incorporated 19,745 terms with their synonyms and abbreviations, 1768 subsumption relations, and 55,558 causal relations. Transitive closure was computed iteratively; it yielded 2154 relations over subsumption and 1,594,896 relations over causality. Five causal cycles were discovered, all with path length of no more than 5. The graph-theoretic metrics of in-degree and out-degree were explored; the most useful metric to prioritize modification of the ontology was found to be the product of the in-degree of transitive closure over subsumption and the out-degree of transitive closure over causality. Two general types of error were identified: (1) causal assertions that used overly general terms because they implicitly assumed an organ-specific context and (2) subsumption relations where a site-specific disorder was asserted to be a subclass of the general disorder. Transitive closure helped identify incorrect assertions, prioritized and guided ontology revision, and aided resources that applied the ontology's knowledge. Copyright © 2016 Elsevier Inc. All rights reserved.

What Women Think: Cancer Causal Attributions in a Diverse Sample of Women

PubMed Central

Rodríguez, Vivian M.; Gyure, Maria E.; Corona, Rosalie; Bodurtha, Joann N.; Bowen, Deborah J.; Quillin, John M.

2014-01-01

Women hold diverse beliefs about cancer etiology, potentially affecting their use of cancer preventive behaviors. To date, research has greatly focused on the causal attributions cancer patients and survivors hold about cancer, and studies have been conducted primarily with White participants. Less is known about causal attributions held by women with and without a family history of cancer from a diverse community sample. This study sought to identify cancer causal attributions of women with and without a family history of cancer, and explore its relation to socio-cultural factors. Diverse women (60% African-American) recruited at an urban, safety-net women's health clinic (N=471) reported factors they believed cause cancer. Responses were coded into nine attributions and analyzed using chi-squares and logistic regressions. Lifestyle-choices (63%), genetics/heredity (34%), and environmental-exposures (19%) were the top causal attributions identified. Women without a family history of cancer were more likely to identify genetics/heredity as an attribution for cancer than women with a history of cancer in their families. Women who identified as White, who had a higher educational attainment, and had commercial insurance were more likely to report genetics/heredity as a causal attribution for cancer. These findings suggest that socio-cultural factors may play a role in the causal attributions individuals make about cancer, which can, in turn, inform cancer awareness and prevention messages. PMID:25398057

Depression and Genetic Causal Attribution of Epilepsy in Multiplex Epilepsy Families

PubMed Central

Sorge, Shawn T.; Hesdorffer, Dale C.; Phelan, Jo C.; Winawer, Melodie R.; Shostak, Sara; Goldsmith, Jeff; Chung, Wendy K.; Ottman, Ruth

2016-01-01

Summary Objectives Rapid advances in genetic research and increased use of genetic testing have increased the emphasis on genetic causes of epilepsy in patient encounters. Research in other disorders suggests that genetic causal attributions can influence patients’ psychological responses and coping strategies, but little is currently known about how epilepsy patients and their relatives will respond to genetic attributions of epilepsy. We investigated the possibility that depression, the most frequent psychiatric comorbidity in the epilepsies, might be related to the perception that epilepsy has a genetic cause among members of families containing multiple individuals with epilepsy. Methods A self-administered survey was completed by 417 individuals in 104 families averaging four individuals with epilepsy per family. Current depression was measured with the PHQ-9. Genetic causal attribution was assessed by three questions addressing: perceived likelihood of having an epilepsy-related mutation, perceived role of genetics in causing epilepsy in the family, and (in individuals with epilepsy) perceived influence of genetics in causing the individual’s epilepsy. Relatives without epilepsy were asked about their perceived chance of developing epilepsy in the future, compared with the average person. Results Prevalence of current depression was 14.8% in 182 individuals with epilepsy, 6.5% in 184 biological relatives without epilepsy, and 3.9% in 51 married-in individuals. Among individuals with epilepsy, depression was unrelated to genetic attribution. Among biological relatives without epilepsy, however, prevalence of depression increased with increasing perceived chance of having an epilepsy-related mutation (p=0.02). This association was not mediated by perceived future epilepsy risk among relatives without epilepsy. Significance Depression is associated with perceived likelihood of carrying an epilepsy-related mutation among individuals without epilepsy in families containing multiple affected individuals. This association should be considered when addressing mental health issues in such families. PMID:27558297

Causes, coping, and culture: a comparative survey study on representation of back pain in three Swiss language regions.

PubMed

Schulz, Peter J; Hartung, Uwe; Riva, Silvia

2013-01-01

This study intends to contribute to a research tradition that asks how causal attributions of illnesses affect coping behavior. Causal attributions are understood as the most important element of illness representations and coping as a means to preserve quality of life. The issue is applied to a condition so far often neglected in research on illness representations-back pain-and a third concept is added to the picture: culture. The aim of this study is (a) to explore the causal factors to which persons with back pain attribute the further course of their illness, (b) to find out whether the attributed causes are predictors of coping maxims, and (c) to find out whether cultural factors affect attributions and coping and moderate the relationship between the two. A total of 1259 gainfully employed or self-employed persons with recent episodes of back pain were recruited in the three language regions of Switzerland. They were asked to complete a structured online interview, measuring among many other variables attributed causes, coping maxims, and affiliation to one of the Swiss micro-cultures (German-, French- or Italian-speaking). Attributed causes of the illness that can be influenced by a patient go along with more active coping styles. Cultural affiliation impacts on coping maxims independently, but culture moderates the relationship of attributed causes and coping maxims only in two of twenty possible cases. The results show that cultural differences can be analytically incorporated in the models of illness representations. Results may help to improve healthcare providers' communication with patients and plan public health campaigns. The approach to micro-cultural differences and the substantive relationships between alterability of causes and activity in coping may help the further development of models of illness representations.

Causes, Coping, and Culture: A Comparative Survey Study on Representation of Back Pain in Three Swiss Language Regions

PubMed Central

Schulz, Peter J.; Hartung, Uwe; Riva, Silvia

2013-01-01

Introduction This study intends to contribute to a research tradition that asks how causal attributions of illnesses affect coping behavior. Causal attributions are understood as the most important element of illness representations and coping as a means to preserve quality of life. The issue is applied to a condition so far often neglected in research on illness representations–back pain–and a third concept is added to the picture: culture. Aim The aim of this study is (a) to explore the causal factors to which persons with back pain attribute the further course of their illness, (b) to find out whether the attributed causes are predictors of coping maxims, and (c) to find out whether cultural factors affect attributions and coping and moderate the relationship between the two. Methods A total of 1259 gainfully employed or self-employed persons with recent episodes of back pain were recruited in the three language regions of Switzerland. They were asked to complete a structured online interview, measuring among many other variables attributed causes, coping maxims, and affiliation to one of the Swiss micro-cultures (German-, French- or Italian-speaking). Results Attributed causes of the illness that can be influenced by a patient go along with more active coping styles. Cultural affiliation impacts on coping maxims independently, but culture moderates the relationship of attributed causes and coping maxims only in two of twenty possible cases. Implications The results show that cultural differences can be analytically incorporated in the models of illness representations. Results may help to improve healthcare providers’ communication with patients and plan public health campaigns. The approach to micro-cultural differences and the substantive relationships between alterability of causes and activity in coping may help the further development of models of illness representations. PMID:24223756

Guidelines for investigating causality of sequence variants in human disease

PubMed Central

MacArthur, D. G.; Manolio, T. A.; Dimmock, D. P.; Rehm, H. L.; Shendure, J.; Abecasis, G. R.; Adams, D. R.; Altman, R. B.; Antonarakis, S. E.; Ashley, E. A.; Barrett, J. C.; Biesecker, L. G.; Conrad, D. F.; Cooper, G. M.; Cox, N. J.; Daly, M. J.; Gerstein, M. B.; Goldstein, D. B.; Hirschhorn, J. N.; Leal, S. M.; Pennacchio, L. A.; Stamatoyannopoulos, J. A.; Sunyaev, S. R.; Valle, D.; Voight, B. F.; Winckler, W.; Gunter, C.

2014-01-01

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development. PMID:24759409

Guidelines for investigating causality of sequence variants in human disease.

PubMed

MacArthur, D G; Manolio, T A; Dimmock, D P; Rehm, H L; Shendure, J; Abecasis, G R; Adams, D R; Altman, R B; Antonarakis, S E; Ashley, E A; Barrett, J C; Biesecker, L G; Conrad, D F; Cooper, G M; Cox, N J; Daly, M J; Gerstein, M B; Goldstein, D B; Hirschhorn, J N; Leal, S M; Pennacchio, L A; Stamatoyannopoulos, J A; Sunyaev, S R; Valle, D; Voight, B F; Winckler, W; Gunter, C

2014-04-24

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Causal beliefs of the public and social acceptance of persons with mental illness: a comparative analysis of schizophrenia, depression and alcohol dependence.

PubMed

Schomerus, G; Matschinger, H; Angermeyer, M C

2014-01-01

There is an ongoing debate whether biological illness explanations improve tolerance towards persons with mental illness or not. Several theoretical models have been proposed to predict the relationship between causal beliefs and social acceptance. This study uses path models to compare different theoretical predictions regarding attitudes towards persons with schizophrenia, depression and alcohol dependence. In a representative population survey in Germany (n = 3642), we elicited agreement with belief in biogenetic causes, current stress and childhood adversities as causes of either disorder as described in an unlabelled case vignette. We further elicited potentially mediating attitudes related to different theories about the consequences of biogenetic causal beliefs (attribution theory: onset responsibility, offset responsibility; genetic essentialism: differentness, dangerousness; genetic optimism: treatability) and social acceptance. For each vignette condition, we calculated a multiple mediator path model containing all variables. Biogenetic beliefs were associated with lower social acceptance in schizophrenia and depression, and with higher acceptance in alcohol dependence. In schizophrenia and depression, perceived differentness and dangerousness mediated the largest indirect effects, the consequences of biogenetic causal explanations thus being in accordance with the predictions of genetic essentialism. Psychosocial causal beliefs had differential effects: belief in current stress as a cause was associated with higher acceptance in schizophrenia, while belief in childhood adversities resulted in lower acceptance of a person with depression. Biological causal explanations seem beneficial in alcohol dependence, but harmful in schizophrenia and depression. The negative correlates of believing in childhood adversities as a cause of depression merit further exploration.

Human Papilloma Viruses and Breast Cancer - Assessment of Causality.

PubMed

Lawson, James Sutherland; Glenn, Wendy K; Whitaker, Noel James

2016-01-01

High risk human papilloma viruses (HPVs) may have a causal role in some breast cancers. Case-control studies, conducted in many different countries, consistently indicate that HPVs are more frequently present in breast cancers as compared to benign breast and normal breast controls (odds ratio 4.02). The assessment of causality of HPVs in breast cancer is difficult because (i) the HPV viral load is extremely low, (ii) HPV infections are common but HPV associated breast cancers are uncommon, and (iii) HPV infections may precede the development of breast and other cancers by years or even decades. Further, HPV oncogenesis can be indirect. Despite these difficulties, the emergence of new evidence has made the assessment of HPV causality, in breast cancer, a practical proposition. With one exception, the evidence meets all the conventional criteria for a causal role of HPVs in breast cancer. The exception is "specificity." HPVs are ubiquitous, which is the exact opposite of specificity. An additional reservation is that the prevalence of breast cancer is not increased in immunocompromised patients as is the case with respect to HPV-associated cervical cancer. This indicates that HPVs may have an indirect causal influence in breast cancer. Based on the overall evidence, high-risk HPVs may have a causal role in some breast cancers.

Human Papilloma Viruses and Breast Cancer – Assessment of Causality

PubMed Central

Lawson, James Sutherland; Glenn, Wendy K.; Whitaker, Noel James

2016-01-01

High risk human papilloma viruses (HPVs) may have a causal role in some breast cancers. Case–control studies, conducted in many different countries, consistently indicate that HPVs are more frequently present in breast cancers as compared to benign breast and normal breast controls (odds ratio 4.02). The assessment of causality of HPVs in breast cancer is difficult because (i) the HPV viral load is extremely low, (ii) HPV infections are common but HPV associated breast cancers are uncommon, and (iii) HPV infections may precede the development of breast and other cancers by years or even decades. Further, HPV oncogenesis can be indirect. Despite these difficulties, the emergence of new evidence has made the assessment of HPV causality, in breast cancer, a practical proposition. With one exception, the evidence meets all the conventional criteria for a causal role of HPVs in breast cancer. The exception is “specificity.” HPVs are ubiquitous, which is the exact opposite of specificity. An additional reservation is that the prevalence of breast cancer is not increased in immunocompromised patients as is the case with respect to HPV-associated cervical cancer. This indicates that HPVs may have an indirect causal influence in breast cancer. Based on the overall evidence, high-risk HPVs may have a causal role in some breast cancers. PMID:27747193

Atypicalities in Perceptual Adaptation in Autism Do Not Extend to Perceptual Causality

PubMed Central

Karaminis, Themelis; Turi, Marco; Neil, Louise; Badcock, Nicholas A.; Burr, David; Pellicano, Elizabeth

2015-01-01

A recent study showed that adaptation to causal events (collisions) in adults caused subsequent events to be less likely perceived as causal. In this study, we examined if a similar negative adaptation effect for perceptual causality occurs in children, both typically developing and with autism. Previous studies have reported diminished adaptation for face identity, facial configuration and gaze direction in children with autism. To test whether diminished adaptive coding extends beyond high-level social stimuli (such as faces) and could be a general property of autistic perception, we developed a child-friendly paradigm for adaptation of perceptual causality. We compared the performance of 22 children with autism with 22 typically developing children, individually matched on age and ability (IQ scores). We found significant and equally robust adaptation aftereffects for perceptual causality in both groups. There were also no differences between the two groups in their attention, as revealed by reaction times and accuracy in a change-detection task. These findings suggest that adaptation to perceptual causality in autism is largely similar to typical development and, further, that diminished adaptive coding might not be a general characteristic of autism at low levels of the perceptual hierarchy, constraining existing theories of adaptation in autism. PMID:25774507

Improved methods for multi-trait fine mapping of pleiotropic risk loci.

PubMed

Kichaev, Gleb; Roytman, Megan; Johnson, Ruth; Eskin, Eleazar; Lindström, Sara; Kraft, Peter; Pasaniuc, Bogdan

2017-01-15

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation. In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data. The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Stratified exact tests for the weak causal null hypothesis in randomized trials with a binary outcome.

PubMed

Chiba, Yasutaka

2017-09-01

Fisher's exact test is commonly used to compare two groups when the outcome is binary in randomized trials. In the context of causal inference, this test explores the sharp causal null hypothesis (i.e. the causal effect of treatment is the same for all subjects), but not the weak causal null hypothesis (i.e. the causal risks are the same in the two groups). Therefore, in general, rejection of the null hypothesis by Fisher's exact test does not mean that the causal risk difference is not zero. Recently, Chiba (Journal of Biometrics and Biostatistics 2015; 6: 244) developed a new exact test for the weak causal null hypothesis when the outcome is binary in randomized trials; the new test is not based on any large sample theory and does not require any assumption. In this paper, we extend the new test; we create a version of the test applicable to a stratified analysis. The stratified exact test that we propose is general in nature and can be used in several approaches toward the estimation of treatment effects after adjusting for stratification factors. The stratified Fisher's exact test of Jung (Biometrical Journal 2014; 56: 129-140) tests the sharp causal null hypothesis. This test applies a crude estimator of the treatment effect and can be regarded as a special case of our proposed exact test. Our proposed stratified exact test can be straightforwardly extended to analysis of noninferiority trials and to construct the associated confidence interval. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Conducting Causal Effects Studies in Science Education: Considering Methodological Trade-Offs in the Context of Policies Affecting Research in Schools

ERIC Educational Resources Information Center

Taylor, Joseph; Kowalski, Susan; Wilson, Christopher; Getty, Stephen; Carlson, Janet

2013-01-01

This paper focuses on the trade-offs that lie at the intersection of methodological requirements for causal effect studies and policies that affect how and to what extent schools engage in such studies. More specifically, current federal funding priorities encourage large-scale randomized studies of interventions in authentic settings. At the same…

Interpretational Confounding Is Due to Misspecification, Not to Type of Indicator: Comment on Howell, Breivik, and Wilcox (2007)

ERIC Educational Resources Information Center

Bollen, Kenneth A.

2007-01-01

R. D. Howell, E. Breivik, and J. B. Wilcox (2007) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal…

Mortality rates and causes of death in children with epilepsy prescribed antiepileptic drugs: a retrospective cohort study using the UK General Practice Research Database.

PubMed

Ackers, Ruth; Besag, Frank M C; Hughes, Elaine; Squier, Waney; Murray, Macey L; Wong, Ian C K

2011-05-01

Patients with epilepsy, including children, have an increased risk of mortality compared with the general population. Antiepileptic drugs (AEDs) were the most frequent class of drugs reported in a study looking at fatal suspected adverse drug reactions in children in the UK. The objective of the study was to identify cases and causes of death in a paediatric patient cohort prescribed AEDs with an associated epilepsy diagnosis. This was a retrospective cohort study supplemented with general practitioner-completed questionnaires, post-mortem reports and death certificates. The setting was UK primary care practices contributing to the General Practice Research Database. Participants were children and adolescents aged 0-18 years prescribed AEDs between 1993 and 2005. Causality assessment was undertaken by a consensus panel comprising paediatric specialists in neuropathology, neurology, neuropsychiatry, paediatric epilepsy, pharmacoepidemiology and pharmacy to determine crude mortality rate (CMR) and standardized mortality ratios (SMRs), and the likelihood of an association between AED(s) and the event of death. There were 6190 subjects in the cohort (contributing 26,890 person-years of data), of whom 151 died. Median age at death was 8.0 years. CMR was 56.2 per 10,000 person-years and the SMR was 22.4 (95% CI 18.9, 26.2). The majority of deceased subjects had severe underlying disorders. Death was attributable to epilepsy in 18 subjects; in 9 the cause of death was sudden unexpected death in epilepsy (SUDEP) [3.3 per 10 000 person-years (95% CI 1.5, 6.4)]. AEDs were probably (n = 2) or possibly (n = 3) associated causally with death in five subjects. Two status epilepticus deaths were associated causally with AED withdrawal. Children prescribed AEDs have an increased risk of mortality relative to the general population. Most of the deaths were in children with serious underlying disorders. A small number of SUDEP cases were identified. AEDs are not a major cause of death but in a small proportion of cases, a causal relationship between death and AEDs could not be excluded.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use

PubMed Central

Edwards, Alexis C.; Maesr, Hermine H.; Prescott, Carol A.; Kendler, Kenneth S.

2014-01-01

Background Alcohol consumption is typically correlated with the alcohol use behaviors of one’s peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. Methods The current study uses data from a sample of male twins (N=1790) who provided retrospective reports of their own alcohol consumption and their peers’ alcohol related behaviors, from adolescence into young adulthood (ages 12–25). Structural equation modeling was employed to compare three plausible models of genetic and environmental influences on the relationship between phenotypes over time. Results Model fitting indicated that one’s own alcohol consumption and the alcohol use of one’s peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Conclusions Peers’ alcohol use behaviors and one’s own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. PMID:25597346

Comparing Methods for Estimating Direct Costs of Adverse Drug Events.

PubMed

Gyllensten, Hanna; Jönsson, Anna K; Hakkarainen, Katja M; Svensson, Staffan; Hägg, Staffan; Rehnberg, Clas

2017-12-01

To estimate how direct health care costs resulting from adverse drug events (ADEs) and cost distribution are affected by methodological decisions regarding identification of ADEs, assigning relevant resource use to ADEs, and estimating costs for the assigned resources. ADEs were identified from medical records and diagnostic codes for a random sample of 4970 Swedish adults during a 3-month study period in 2008 and were assessed for causality. Results were compared for five cost evaluation methods, including different methods for identifying ADEs, assigning resource use to ADEs, and for estimating costs for the assigned resources (resource use method, proportion of registered cost method, unit cost method, diagnostic code method, and main diagnosis method). Different levels of causality for ADEs and ADEs' contribution to health care resource use were considered. Using the five methods, the maximum estimated overall direct health care costs resulting from ADEs ranged from Sk10,000 (Sk = Swedish krona; ~€1,500 in 2016 values) using the diagnostic code method to more than Sk3,000,000 (~€414,000) using the unit cost method in our study population. The most conservative definitions for ADEs' contribution to health care resource use and the causality of ADEs resulted in average costs per patient ranging from Sk0 using the diagnostic code method to Sk4066 (~€500) using the unit cost method. The estimated costs resulting from ADEs varied considerably depending on the methodological choices. The results indicate that costs for ADEs need to be identified through medical record review and by using detailed unit cost data. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Supporting shared hypothesis testing in the biomedical domain.

PubMed

Agibetov, Asan; Jiménez-Ruiz, Ernesto; Ondrésik, Marta; Solimando, Alessandro; Banerjee, Imon; Guerrini, Giovanna; Catalano, Chiara E; Oliveira, Joaquim M; Patanè, Giuseppe; Reis, Rui L; Spagnuolo, Michela

2018-02-08

Pathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses. In this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences. We evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

The causal structure of utility conditionals.

PubMed

Bonnefon, Jean-François; Sloman, Steven A

2013-01-01

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ''if p then q'' statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. Copyright © 2012 Cognitive Science Society, Inc.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Reducing Children’s Behavior Problems through Social Capital: A Causal Assessment

PubMed Central

López Turley, Ruth N.; Gamoran, Adam; McCarty, Alyn Turner; Fish, Rachel

2016-01-01

Behavior problems among young children have serious detrimental effects on short and long-term educational outcomes. An especially promising prevention strategy may be one that focuses on strengthening the relationships among families in schools, or social capital. However, empirical research on social capital has been constrained by conceptual and causal ambiguity. This study attempts to construct a more focused conceptualization of social capital and aims to determine the causal effects of social capital on children’s behavior. Using data from a cluster randomized trial of 52 elementary schools, we apply several multilevel models to assess the causal relationship, including intent to treat and treatment on the treated analyses. Taken together, these analyses provide stronger evidence than previous studies that social capital improves children’s behavioral outcomes and that these improvements are not simply a result of selection into social relations but result from the social relations themselves. PMID:27886729

Causal Impact of Employee Work Perceptions on the Bottom Line of Organizations.

PubMed

Harter, James K; Schmidt, Frank L; Asplund, James W; Killham, Emily A; Agrawal, Sangeeta

2010-07-01

Perceptions of work conditions have proven to be important to the well-being of workers. However, customer loyalty, employee retention, revenue, sales, and profit are essential to the success of any business. It is known that these outcomes are correlated with employee attitudes and perceptions of work conditions, but the research into direction of causality has been inconclusive. Using a massive longitudinal database that included 2,178 business units in 10 large organizations, we found evidence supporting the causal impact of employee perceptions on these bottom-line measures; reverse causality of bottom-line measures on employee perceptions existed but was weaker. Managerial actions and practices can impact employee work conditions and employee perceptions of these conditions, thereby improving key outcomes at the organizational level. Perceptions of specific work conditions that engage employees in their work provide practical guidance in how best to manage people to obtain desired results. © The Author(s) 2010.

Academic procrastination: the relationship between causal attribution styles and behavioral postponement.

PubMed

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

This research was conducted to study the relationship between attribution and academic procrastination in University Students. The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning.

Internal Validity: A Must in Research Designs

ERIC Educational Resources Information Center

Cahit, Kaya

2015-01-01

In experimental research, internal validity refers to what extent researchers can conclude that changes in dependent variable (i.e. outcome) are caused by manipulations in independent variable. The causal inference permits researchers to meaningfully interpret research results. This article discusses (a) internal validity threats in social and…

Combining Propensity Score Methods and Complex Survey Data to Estimate Population Treatment Effects

ERIC Educational Resources Information Center

Stuart, Elizabeth A.; Dong, Nianbo; Lenis, David

2016-01-01

Complex surveys are often used to estimate causal effects regarding the effects of interventions or exposures of interest. Propensity scores (Rosenbaum & Rubin, 1983) have emerged as one popular and effective tool for causal inference in non-experimental studies, as they can help ensure that groups being compared are similar with respect to a…

Causal illusions in children when the outcome is frequent

PubMed Central

2017-01-01

Causal illusions occur when people perceive a causal relation between two events that are actually unrelated. One factor that has been shown to promote these mistaken beliefs is the outcome probability. Thus, people tend to overestimate the strength of a causal relation when the potential consequence (i.e. the outcome) occurs with a high probability (outcome-density bias). Given that children and adults differ in several important features involved in causal judgment, including prior knowledge and basic cognitive skills, developmental studies can be considered an outstanding approach to detect and further explore the psychological processes and mechanisms underlying this bias. However, the outcome density bias has been mainly explored in adulthood, and no previous evidence for this bias has been reported in children. Thus, the purpose of this study was to extend outcome-density bias research to childhood. In two experiments, children between 6 and 8 years old were exposed to two similar setups, both showing a non-contingent relation between the potential cause and the outcome. These two scenarios differed only in the probability of the outcome, which could either be high or low. Children judged the relation between the two events to be stronger in the high probability of the outcome setting, revealing that, like adults, they develop causal illusions when the outcome is frequent. PMID:28898294

The influence of farmer demographic characteristics on environmental behaviour: a review.

PubMed

Burton, Rob J F

2014-03-15

Many agricultural studies have observed a relationship between farmer demographic characteristics and environmental behaviours. These relationships are frequently employed in the construction of models, the identification of farmer types, or as part of more descriptive analyses aimed at understanding farmers' environmental behaviour. However, they have also often been found to be inconsistent or contradictory. Although a considerable body of literature has built up around the subject area, research has a tendency to focus on factors such as the direction, strength and consistency of the relationship - leaving the issue of causality largely to speculation. This review addresses this gap by reviewing literature on 4 key demographic variables: age, experience, education, and gender for hypothesised causal links. Overall the review indicates that the issue of causality is a complex one. Inconsistent relationships can be attributed to the presence of multiple causal pathways, the role of scheme factors in determining which pathway is important, inadequately specified measurements of demographic characteristics, and the treatment of non-linear causalities as linear. In addition, all demographic characteristics were perceived to be influenced (to varying extents) by cultural-historical patterns leading to cohort effects or socialised differences in the relationship with environmental behaviour. The paper concludes that more work is required on the issue of causality. Copyright © 2013 Elsevier Ltd. All rights reserved.

Subfertility factors rather than assisted conception factors affect cognitive and behavioural development of 4-year-old singletons.

PubMed

Schendelaar, Pamela; La Bastide-Van Gemert, Sacha; Heineman, Maas Jan; Middelburg, Karin J; Seggers, Jorien; Van den Heuvel, Edwin R; Hadders-Algra, Mijna

2016-12-01

Research on cognitive and behavioural development of children born after assisted conception is inconsistent. This prospective study aimed to explore underlying causal relationships between ovarian stimulation, in-vitro procedures, subfertility components and child cognition and behaviour. Participants were singletons born to subfertile couples after ovarian stimulation IVF (n = 63), modified natural cycle IVF (n = 53), natural conception (n = 79) and singletons born to fertile couples (reference group) (n = 98). At 4 years, cognition (Kaufmann-ABC-II; total IQ) and behaviour (Child Behavior Checklist; total problem T-score) were assessed. Causal inference search algorithms and structural equation modelling was applied to unravel causal mechanisms. Most children had typical cognitive and behavioural scores. No underlying causal effect was found between ovarian stimulation and the in-vitro procedure and outcome. Direct negative causal effects were found between severity of subfertility (time to pregnancy) and cognition and presence of subfertility and behaviour. Maternal age and maternal education acted as confounders. The study concludes that no causal effects were found between ovarian stimulation or in-vitro procedures and cognition and behaviour in childrenaged 4 years born to subfertile couples. Subfertility, especially severe subfertility, however, was associated with worse cognition and behaviour. Copyright © 2016 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Marginal Structural Models with Counterfactual Effect Modifiers.

PubMed

Zheng, Wenjing; Luo, Zhehui; van der Laan, Mark J

2018-06-08

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. 2000;11:561-570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393-1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

DOE PAGES

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

2017-10-29

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X) of some underlying causal model producing the observables or responses (Y = g ( X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research inmore » which inverse predictions, along with an assessment of predictive capability, are desired.« less

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X) of some underlying causal model producing the observables or responses (Y = g ( X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research inmore » which inverse predictions, along with an assessment of predictive capability, are desired.« less

Population-based health promotion perspective for older driver safety: Conceptual framework to intervention plan

PubMed Central

Classen, Sherrilene; Lopez, Ellen DS; Winter, Sandra; Awadzi, Kezia D; Ferree, Nita; Garvan, Cynthia W

2007-01-01

The topic of motor vehicle crashes among the elderly is dynamic and multi-faceted requiring a comprehensive and synergistic approach to intervention planning. This approach must be based on the values of a given population as well as health statistics and asserted through community, organizational and policy strategies. An integrated summary of the predictors (quantitative research), and views (qualitative research) of the older drivers and their stakeholders, does not currently exist. This study provided an explicit socio-ecological view explaining the interrelation of possible causative factors, an integrated summary of these causative factors, and empirical guidelines for developing public health interventions to promote older driver safety. Using a mixed methods approach, we were able to compare and integrate main findings from a national crash dataset with perspectives of stakeholders. We identified: 11 multi-causal factors for safe elderly driving; the importance of the environmental factors - previously underrated in the literature- interacting with behavioral and health factors; and the interrelatedness among many socio-ecological factors. For the first time, to our knowledge, we conceptualized the fundamental elements of a multi-causal health promotion plan, with measurable intermediate and long-term outcomes. After completing the detailed plan we will test the effectiveness of this intervention on multiple levels. PMID:18225470

Alternatives to the Randomized Controlled Trial

PubMed Central

West, Stephen G.; Duan, Naihua; Pequegnat, Willo; Gaist, Paul; Des Jarlais, Don C.; Holtgrave, David; Szapocznik, José; Fishbein, Martin; Rapkin, Bruce; Clatts, Michael; Mullen, Patricia Dolan

2008-01-01

Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurricane Katrina) for which the randomized controlled trial (RCT) may not be a feasible option. Drawing on the potential outcomes framework (Rubin Causal Model) and Campbellian perspectives, we consider alternative research designs that permit relatively strong causal inferences. In randomized encouragement designs, participants are randomly invited to participate in one of the treatment conditions, but are allowed to decide whether to receive treatment. In quantitative assignment designs, treatment is assigned on the basis of a quantitative measure (e.g., need, merit, risk). In observational studies, treatment assignment is unknown and presumed to be nonrandom. Major threats to the validity of each design and statistical strategies for mitigating those threats are presented. PMID:18556609

A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?

PubMed Central

Lanza, Stephanie T.; Coffman, Donna L.

2013-01-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p=0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p=0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work. PMID:23839479

A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

PubMed

Butera, Nicole M; Lanza, Stephanie T; Coffman, Donna L

2014-06-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

Measurement Models for Reasoned Action Theory.

PubMed

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-03-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach.

Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.

PubMed

Faes, L; Porta, A; Cucino, R; Cerutti, S; Antolini, R; Nollo, G

2004-06-01

Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y was estimated after imposing causality by setting to zero the model coefficients representative of the reverse effects from y to x. The method was tested in simulations reproducing linear open and closed loop interactions, showing a better adherence of the causal transfer function to the theoretical curves with respect to the traditional approach in presence of non-negligible reverse effects. It was then applied in ten healthy young subjects to characterize the transfer functions from respiration to heart period (RR interval) and to systolic arterial pressure (SAP), and from SAP to RR interval. In the first two cases, the causal and non-causal transfer function estimates were comparable, indicating that respiration, acting as exogenous signal, sets an open loop relationship upon SAP and RR interval. On the contrary, causal and traditional transfer functions from SAP to RR were significantly different, suggesting the presence of a considerable influence on the opposite causal direction. Thus, the proposed causal approach seems to be appropriate for the estimation of parameters, like the gain and the phase lag from SAP to RR interval, which have a large clinical and physiological relevance.

Drug- and Herb-Induced Liver Injury in Clinical and Translational Hepatology: Causality Assessment Methods, Quo Vadis?

PubMed Central

Eickhoff, Axel; Schulze, Johannes

2013-01-01

Drug-induced liver injury (DILI) and herb-induced liver injury (HILI) are typical diseases of clinical and translational hepatology. Their diagnosis is complex and requires an experienced clinician to translate basic science into clinical judgment and identify a valid causality algorithm. To prospectively assess causality starting on the day DILI or HILI is suspected, the best approach for physicians is to use the Council for International Organizations of Medical Sciences (CIOMS) scale in its original or preferably its updated version. The CIOMS scale is validated, liver-specific, structured, and quantitative, providing final causality grades based on scores of specific items for individual patients. These items include latency period, decline in liver values after treatment cessation, risk factors, co-medication, alternative diagnoses, hepatotoxicity track record of the suspected product, and unintentional re-exposure. Provided causality is established as probable or highly probable, data of the CIOMS scale with all individual items, a short clinical report, and complete raw data should be transmitted to the regulatory agencies, manufacturers, expert panels, and possibly to the scientific community for further refinement of the causality evaluation in a setting of retrospective expert opinion. Good-quality case data combined with thorough CIOMS-based assessment as a standardized approach should avert subsequent necessity for other complex causality assessment methods that may have inter-rater problems because of poor-quality data. In the future, the CIOMS scale will continue to be the preferred tool to assess causality of DILI and HILI cases and should be used consistently, both prospectively by physicians, and retrospectively for subsequent expert opinion if needed. For comparability and international harmonization, all parties assessing causality in DILI and HILI cases should attempt this standardized approach using the updated CIOMS scale. PMID:26357608

Common sense: folk wisdom that ethnobiological and ethnomedical research cannot afford to ignore

PubMed Central

2013-01-01

Common sense [CS], especially that of the non-scientist, can have predictive power to identify promising research avenues, as humans anywhere on Earth have always looked for causal links to understand, shape and control the world around them. CS is based on the experience of many individuals and is thus believed to hold some truths. Outcomes predicted by CS are compatible with observations made by whole populations and have survived tests conducted by a plethora of non-scientists. To explore our claim, we provide 4 examples of empirical insights (relevant to probably all ethnic groups on Earth) into causal phenomena predicted by CS: (i) “humans must have a sense of time”, (ii) “at extreme latitudes, more people have the winter blues”, (iii) “sleep is a cure for many ills” and (iv) “social networks affect health and disease”. While CS is fallible, it should not be ignored by science – however improbable or self-evident the causal relationships predicted by CS may appear to be. PMID:24295068

Making stronger causal inferences: Accounting for selection bias in associations between high performance work systems, leadership, and employee and customer satisfaction.

PubMed

Schmidt, Joseph A; Pohler, Dionne M

2018-05-17

We develop competing hypotheses about the relationship between high performance work systems (HPWS) with employee and customer satisfaction. Drawing on 8 years of employee and customer survey data from a financial services firm, we used a recently developed empirical technique-covariate balanced propensity score (CBPS) weighting-to examine if the proposed relationships between HPWS and satisfaction outcomes can be explained by reverse causality, selection effects, or commonly omitted variables such as leadership behavior. The results provide support for leader behaviors as a primary driver of customer satisfaction, rather than HPWS, and also suggest that the problem of reverse causality requires additional attention in future human resource (HR) systems research. Model comparisons suggest that the estimates and conclusions vary across CBPS, meta-analytic, cross-sectional, and time-lagged models (with and without a lagged dependent variable as a control). We highlight the theoretical and methodological implications of the findings for HR systems research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Infant acetylcholine, dopamine, and melatonin dysregulation: Neonatal biomarkers and causal factors for ASD and ADHD phenotypes.

PubMed

Hellmer, Kahl; Nyström, Pär

2017-03-01

Autism spectrum disorders (ASD) and ADHD are common neurodevelopmental disorders that benefit from early intervention but currently suffer from late detection and diagnosis: neurochemical dysregulations are extant already at birth but clinical phenotypes are not distinguishable until preschool age or later. The vast heterogeneity between subjects' phenotypes relates to interaction between multiple unknown factors, making research on factor causality insurmountable. To unlock this situation we pose the hypothesis that atypical pupillary light responses from rods, cones, and the recently discovered ipRGC system reflect early acetylcholine, melatonin, and dopamine dysregulation that are sufficient but not necessary factors for developing ASD and/or ADHD disorders. Current technology allows non-invasive cost-efficient assessment already from the first postnatal month. The benefits of the current proposal are: identification of clinical subgroups based on cause rather than phenotypes; facilitation of research on other causal factors; neonatal prediction of later diagnoses; and guidance for targeted therapeutical intervention. Copyright © 2017 Elsevier Ltd. All rights reserved.

Skeletal Muscle Pump Drives Control of Cardiovascular and Postural Systems

NASA Astrophysics Data System (ADS)

Verma, Ajay K.; Garg, Amanmeet; Xu, Da; Bruner, Michelle; Fazel-Rezai, Reza; Blaber, Andrew P.; Tavakolian, Kouhyar

2017-03-01

The causal interaction between cardio-postural-musculoskeletal systems is critical in maintaining postural stability under orthostatic challenge. The absence or reduction of such interactions could lead to fainting and falls often experienced by elderly individuals. The causal relationship between systolic blood pressure (SBP), calf electromyography (EMG), and resultant center of pressure (COPr) can quantify the behavior of cardio-postural control loop. Convergent cross mapping (CCM) is a non-linear approach to establish causality, thus, expected to decipher nonlinear causal cardio-postural-musculoskeletal interactions. Data were acquired simultaneously from young participants (25 ± 2 years, n = 18) during a 10-minute sit-to-stand test. In the young population, skeletal muscle pump was found to drive blood pressure control (EMG → SBP) as well as control the postural sway (EMG → COPr) through the significantly higher causal drive in the direction towards SBP and COPr. Furthermore, the effect of aging on muscle pump activation associated with blood pressure regulation was explored. Simultaneous EMG and SBP were acquired from elderly group (69 ± 4 years, n = 14). A significant (p = 0.002) decline in EMG → SBP causality was observed in the elderly group, compared to the young group. The results highlight the potential of causality to detect alteration in blood pressure regulation with age, thus, a potential clinical utility towards detection of fall proneness.

Sequential causal inference: Application to randomized trials of adaptive treatment strategies

PubMed Central

Dawson, Ree; Lavori, Philip W.

2009-01-01

SUMMARY Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal ‘one-step’ estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies. PMID:17914714

Design approaches to experimental mediation☆

PubMed Central

Pirlott, Angela G.; MacKinnon, David P.

2016-01-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., “measurement-of-mediation” designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable. PMID:27570259

Design approaches to experimental mediation.

PubMed

Pirlott, Angela G; MacKinnon, David P

2016-09-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., "measurement-of-mediation" designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable.

Private schooling and admission to medicine: a case study using matched samples and causal mediation analysis.

PubMed

Houston, Muir; Osborne, Michael; Rimmer, Russell

2015-08-20

Are applicants from private schools advantaged in gaining entry to degrees in medicine? This is of international significance and there is continuing research in a range of nations including the USA, the UK, other English-speaking nations and EU countries. Our purpose is to seek causal explanations using a quantitative approach. We took as a case study admission to medicine in the UK and drew samples of those who attended private schools and those who did not, with sample members matched on background characteristics. Unlike other studies in the area, causal mediation analysis was applied to resolve private-school influence into direct and indirect effects. In so doing, we sought a benchmark, using data for 2004, against which the effectiveness of policies adopted over the past decade can be assessed. Private schooling improved admission likelihood. This did not occur indirectly via the effect of school type on academic performance; but arose directly from attending private schools. A sensitivity analysis suggests this finding is unlikely to be eliminated by the influence of an unobserved variable. Academic excellence is not a certain pathway into medicine at university; yet applying with good grades after attending private school is more certain. The results of our paper differ from those in an earlier observational study and find support in a later study. Consideration of sources of difference from the earlier observational study suggest the causal approach offers substantial benefits and the consequences in the causal study for gender, ethnicity, socio-economic classification and region of residence provide a benchmark for assessing policy in future research.

A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates.

PubMed

Barberia, Itxaso; Tubau, Elisabet; Matute, Helena; Rodríguez-Ferreiro, Javier

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students.

Backward Blocking and Interference between Cues Are Empirically Equivalent in Non-Causally Framed Learning Tasks

ERIC Educational Resources Information Center

Luque, David; Moris, Joaquin; Orgaz, Cristina; Cobos, Pedro L.; Matute, Helena

2011-01-01

Backward blocking (BB) and interference between cues (IbC) are cue competition effects produced by very similar manipulations. In a standard BB design, both effects might occur simultaneously, which implies a potential problem for studying BB. In the present study with humans, the magnitude of both effects was compared using a non-causal scenario…

The historical dynamics of social-ecological traps.

PubMed

Boonstra, Wiebren J; de Boer, Florianne W

2014-04-01

Environmental degradation is a typical unintended outcome of collective human behavior. Hardin's metaphor of the "tragedy of the commons" has become a conceived wisdom that captures the social dynamics leading to environmental degradation. Recently, "traps" has gained currency as an alternative concept to explain the rigidity of social and ecological processes that produce environmental degradation and livelihood impoverishment. The trap metaphor is, however, a great deal more complex compared to Hardin's insight. This paper takes stock of studies using the trap metaphor. It argues that the concept includes time and history in the analysis, but only as background conditions and not as a factor of causality. From a historical-sociological perspective this is remarkable since social-ecological traps are clearly path-dependent processes, which are causally produced through a conjunction of events. To prove this point the paper conceptualizes social-ecological traps as a process instead of a condition, and systematically compares history and timing in one classic and three recent studies of social-ecological traps. Based on this comparison it concludes that conjunction of social and environmental events contributes profoundly to the production of trap processes. The paper further discusses the implications of this conclusion for policy intervention and outlines how future research might generalize insights from historical-sociological studies of traps.

Over-imitation is not automatic: context sensitivity in children's overimitation and action interpretation of causally irrelevant actions.

PubMed

Keupp, Stefanie; Behne, Tanya; Zachow, Joanna; Kasbohm, Alina; Rakoczy, Hannes

2015-02-01

Recent research has documented the robust tendency of children to "over-imitate," that is, to copy causally irrelevant action elements in goal-directed action sequences. Different explanations for over-imitation have been proposed. Causal accounts claim that children mistakenly perceive such action elements as causally relevant and, therefore, imitate them. Affiliation accounts claim that children over-imitate to affiliate with the model. Normative accounts claim that children conceive of causally irrelevant actions as essential parts of an overarching conventional activity. These different accounts generally hold the same predictions regarding children's imitative response. However, it is possible to distinguish between them when one considers additional parameters. The normative account predicts wide-ranging flexibility with regard to action interpretation and the occurrence of over-imitation. First, it predicts spontaneous protest against norm violators who omit the causally irrelevant actions. Second, children should perform the causally irrelevant actions less frequently, and criticize others less frequently for omitting them, when the actions take place in a different context from the one of the initial demonstration. Such flexibility is not predicted by causal accounts and is predicted for only a limited range of contexts by affiliation accounts. Study 1 investigated children's own imitative response and found less over-imitation when children acted in a different context from when they acted in the same context as the initial demonstration. In Study 2, children criticized a puppet less frequently for omitting irrelevant actions when the puppet acted in a different context. The results support the notion that over-imitation is not an automatic and inflexible phenomenon. Copyright © 2014 Elsevier Inc. All rights reserved.

Causal attributions in parents of babies with a cleft lip and/or palate and their association with psychological well-being.

PubMed

Nelson, Jonathan; O'Leary, Catherine; Weinman, John

2009-07-01

This study aimed to assess causal attributions of parents of babies with a cleft lip and/or palate. Evidence from causal attribution theory and attribution studies in other medical conditions led to the hypothesis that parents who make internal attributions (self-blame) will have poorer psychological well-being. A cross-sectional survey. Postal questionnaires were sent to parents of children under the care of the South Thames Cleft Service at Guy's Hospital. PARTICIPANTS were recruited if they had a baby between 12 and 24 months old with a cleft lip and/or palate. Of 204 parents, 42 responded. A semistructured questionnaire about causal beliefs was completed alongside validated questionnaires measuring anxiety, depression (Hospital Anxiety and Depression Scale), and perceived stress (Perceived Stress Scale). Causal attributions were grouped according to type (environmental, chance, self-blame, and no belief) and loci (external or internal). The most common attribution made was to external factors (54.4%), followed by no causal attribution (38.1%). Parents making an internal (self-blaming) attribution (16.7%) had significantly (p < .05) higher scores on the Hospital Anxiety and Depression Scale anxiety measure (r = .32) and Perceived Stress Scale (r = .33), but not on the Hospital Anxiety and Depression Scale depression measure (p = .283). The high number of parents making an external attribution can be explained by causal attribution theory. However, the percentage of parents making no causal attribution was higher than seen in previous research. Surprisingly, no parents blamed others. The main hypothesis was tentatively accepted because there were significantly higher anxiety and stress scores in parents who self-blamed; although, depression scores were not significantly higher.

Quasi-experimental study designs series-paper 7: assessing the assumptions.

PubMed

Bärnighausen, Till; Oldenburg, Catherine; Tugwell, Peter; Bommer, Christian; Ebert, Cara; Barreto, Mauricio; Djimeu, Eric; Haber, Noah; Waddington, Hugh; Rockers, Peter; Sianesi, Barbara; Bor, Jacob; Fink, Günther; Valentine, Jeffrey; Tanner, Jeffrey; Stanley, Tom; Sierra, Eduardo; Tchetgen, Eric Tchetgen; Atun, Rifat; Vollmer, Sebastian

2017-09-01

Quasi-experimental designs are gaining popularity in epidemiology and health systems research-in particular for the evaluation of health care practice, programs, and policy-because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions. Copyright © 2017 Elsevier Inc. All rights reserved.

Globally conditioned Granger causality in brain–brain and brain–heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

PubMed Central

Passamonti, Luca; Wald, Lawrence L.; Barbieri, Riccardo

2016-01-01

The causal, directed interactions between brain regions at rest (brain–brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain–heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain–brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain–brain and brain–heart interactions reflecting central modulation of ANS outflow. PMID:27044985

Herbal hepatotoxicity: Challenges and pitfalls of causality assessment methods

PubMed Central

Teschke, Rolf; Frenzel, Christian; Schulze, Johannes; Eickhoff, Axel

2013-01-01

The diagnosis of herbal hepatotoxicity or herb induced liver injury (HILI) represents a particular clinical and regulatory challenge with major pitfalls for the causality evaluation. At the day HILI is suspected in a patient, physicians should start assessing the quality of the used herbal product, optimizing the clinical data for completeness, and applying the Council for International Organizations of Medical Sciences (CIOMS) scale for initial causality assessment. This scale is structured, quantitative, liver specific, and validated for hepatotoxicity cases. Its items provide individual scores, which together yield causality levels of highly probable, probable, possible, unlikely, and excluded. After completion by additional information including raw data, this scale with all items should be reported to regulatory agencies and manufacturers for further evaluation. The CIOMS scale is preferred as tool for assessing causality in hepatotoxicity cases, compared to numerous other causality assessment methods, which are inferior on various grounds. Among these disputed methods are the Maria and Victorino scale, an insufficiently qualified, shortened version of the CIOMS scale, as well as various liver unspecific methods such as the ad hoc causality approach, the Naranjo scale, the World Health Organization (WHO) method, and the Karch and Lasagna method. An expert panel is required for the Drug Induced Liver Injury Network method, the WHO method, and other approaches based on expert opinion, which provide retrospective analyses with a long delay and thereby prevent a timely assessment of the illness in question by the physician. In conclusion, HILI causality assessment is challenging and is best achieved by the liver specific CIOMS scale, avoiding pitfalls commonly observed with other approaches. PMID:23704820

Causality Assessment in Drug-Induced Liver Injury Using a Structured Expert Opinion Process: Comparison to the Roussel-Uclaf Causality Assessment Method

PubMed Central

Rockey, Don C.; Seeff, Leonard B.; Rochon, James; Freston, James; Chalasani, Naga; Bonacini, Maurizio; Fontana, Robert J.; Hayashi, Paul H.

2011-01-01

Drug-induced liver injury (DILI) is largely a diagnosis of exclusion and is therefore challenging. The US Drug-Induced Liver Injury Network (DILIN) prospective study used two methods to assess DILI causality: a structured expert opinion process and the Roussel-Uclaf Causality Assessment Method (RUCAM). Causality assessment focused on detailed clinical and laboratory data from patients with suspected DILI. The adjudication process used standardized numerical and descriptive definitions and scored cases as definite, highly likely, probable, possible, or unlikely. Results of the structured expert opinion procedure were compared with those derived by the RUCAM approach. Among 250 patients with suspected DILI, the expert opinion adjudication process scored 78 patients (31%) as definite, 102 (41%) as highly likely, 37 (15%) as probable, 25 (10%) as possible, and 8 (3%) as unlikely. Among 187 enrollees who had received a single implicated drug, initial complete agreement was reached for 50 (27%) with the expert opinion process and for 34 (19%) with a five-category RUCAM scale (P = 0.08), and the two methods demonstrated a modest correlation with each other (Spearman's r = 0.42, P = 0.0001). Importantly, the RUCAM approach substantially shifted the causality likelihood toward lower probabilities in comparison with the DILIN expert opinion process. Conclusion The structured DILIN expert opinion process produced higher agreement rates and likelihood scores than RUCAM in assessing causality, but there was still considerable interobserver variability in both. Accordingly, a more objective, reliable, and reproducible means of assessing DILI causality is still needed. PMID:20512999

Introspective Reasoning Models for Multistrategy Case-Based and Explanation

DTIC Science & Technology

1997-03-10

symptoms and diseases to causal 30 principles about diseases and first-principle analysis grounded in basic science. Based on research in process...the symptoms of the failure to conclusion that the process which posts learning goals a causal explanation of the failure. Secondl,,. the learner...the vernacular, a "jones" is a drug habit accompanied the faucet for water. Therefore, the story can end with by withdrawal symptoms . The verb "to jones

Comparative Logic Modeling for Policy Analysis: The Case of HIV Testing Policy Change at the Department of Veterans Affairs

PubMed Central

Langer, Erika M; Gifford, Allen L; Chan, Kee

2011-01-01

Objective Logic models have been used to evaluate policy programs, plan projects, and allocate resources. Logic Modeling for policy analysis has been used rarely in health services research but can be helpful in evaluating the content and rationale of health policies. Comparative Logic Modeling is used here on human immunodeficiency virus (HIV) policy statements from the Department of Veterans Affairs (VA) and Centers for Disease Control and Prevention (CDC). We created visual representations of proposed HIV screening policy components in order to evaluate their structural logic and research-based justifications. Data Sources and Study Design We performed content analysis of VA and CDC HIV testing policy documents in a retrospective case study. Data Collection Using comparative Logic Modeling, we examined the content and primary sources of policy statements by the VA and CDC. We then quantified evidence-based causal inferences within each statement. Principal Findings VA HIV testing policy structure largely replicated that of the CDC guidelines. Despite similar design choices, chosen research citations did not overlap. The agencies used evidence to emphasize different components of the policies. Conclusion Comparative Logic Modeling can be used by health services researchers and policy analysts more generally to evaluate structural differences in health policies and to analyze research-based rationales used by policy makers. PMID:21689094

Causal premise semantics.

PubMed

Kaufmann, Stefan

2013-08-01

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals. Copyright © 2013 Cognitive Science Society, Inc.

The association between exaggeration in health related science news and academic press releases: retrospective observational study

PubMed Central

Vivian-Griffiths, Solveiga; Boivin, Jacky; Williams, Andy; Venetis, Christos A; Davies, Aimée; Ogden, Jack; Whelan, Leanne; Hughes, Bethan; Dalton, Bethan; Boy, Fred

2014-01-01

Objective To identify the source (press releases or news) of distortions, exaggerations, or changes to the main conclusions drawn from research that could potentially influence a reader’s health related behaviour. Design Retrospective quantitative content analysis. Setting Journal articles, press releases, and related news, with accompanying simulations. Sample Press releases (n=462) on biomedical and health related science issued by 20 leading UK universities in 2011, alongside their associated peer reviewed research papers and news stories (n=668). Main outcome measures Advice to readers to change behaviour, causal statements drawn from correlational research, and inference to humans from animal research that went beyond those in the associated peer reviewed papers. Results 40% (95% confidence interval 33% to 46%) of the press releases contained exaggerated advice, 33% (26% to 40%) contained exaggerated causal claims, and 36% (28% to 46%) contained exaggerated inference to humans from animal research. When press releases contained such exaggeration, 58% (95% confidence interval 48% to 68%), 81% (70% to 93%), and 86% (77% to 95%) of news stories, respectively, contained similar exaggeration, compared with exaggeration rates of 17% (10% to 24%), 18% (9% to 27%), and 10% (0% to 19%) in news when the press releases were not exaggerated. Odds ratios for each category of analysis were 6.5 (95% confidence interval 3.5 to 12), 20 (7.6 to 51), and 56 (15 to 211). At the same time, there was little evidence that exaggeration in press releases increased the uptake of news. Conclusions Exaggeration in news is strongly associated with exaggeration in press releases. Improving the accuracy of academic press releases could represent a key opportunity for reducing misleading health related news. PMID:25498121

The association between exaggeration in health related science news and academic press releases: retrospective observational study.

PubMed

Sumner, Petroc; Vivian-Griffiths, Solveiga; Boivin, Jacky; Williams, Andy; Venetis, Christos A; Davies, Aimée; Ogden, Jack; Whelan, Leanne; Hughes, Bethan; Dalton, Bethan; Boy, Fred; Chambers, Christopher D

2014-12-09

To identify the source (press releases or news) of distortions, exaggerations, or changes to the main conclusions drawn from research that could potentially influence a reader's health related behaviour. Retrospective quantitative content analysis. Journal articles, press releases, and related news, with accompanying simulations. Press releases (n = 462) on biomedical and health related science issued by 20 leading UK universities in 2011, alongside their associated peer reviewed research papers and news stories (n = 668). Advice to readers to change behaviour, causal statements drawn from correlational research, and inference to humans from animal research that went beyond those in the associated peer reviewed papers. 40% (95% confidence interval 33% to 46%) of the press releases contained exaggerated advice, 33% (26% to 40%) contained exaggerated causal claims, and 36% (28% to 46%) contained exaggerated inference to humans from animal research. When press releases contained such exaggeration, 58% (95% confidence interval 48% to 68%), 81% (70% to 93%), and 86% (77% to 95%) of news stories, respectively, contained similar exaggeration, compared with exaggeration rates of 17% (10% to 24%), 18% (9% to 27%), and 10% (0% to 19%) in news when the press releases were not exaggerated. Odds ratios for each category of analysis were 6.5 (95% confidence interval 3.5 to 12), 20 (7.6 to 51), and 56 (15 to 211). At the same time, there was little evidence that exaggeration in press releases increased the uptake of news. Exaggeration in news is strongly associated with exaggeration in press releases. Improving the accuracy of academic press releases could represent a key opportunity for reducing misleading health related news. © Sumner et al 2014.

Design Issues and Inference in Experimental L2 Research

ERIC Educational Resources Information Center

Hudson, Thom; Llosa, Lorena

2015-01-01

Explicit attention to research design issues is essential in experimental second language (L2) research. Too often, however, such careful attention is not paid. This article examines some of the issues surrounding experimental L2 research and its relationships to causal inferences. It discusses the place of research questions and hypotheses,…

Evaluation of occupational health interventions using a randomized controlled trial: challenges and alternative research designs.

PubMed

Schelvis, Roosmarijn M C; Oude Hengel, Karen M; Burdorf, Alex; Blatter, Birgitte M; Strijk, Jorien E; van der Beek, Allard J

2015-09-01

Occupational health researchers regularly conduct evaluative intervention research for which a randomized controlled trial (RCT) may not be the most appropriate design (eg, effects of policy measures, organizational interventions on work schedules). This article demonstrates the appropriateness of alternative designs for the evaluation of occupational health interventions, which permit causal inferences, formulated along two study design approaches: experimental (stepped-wedge) and observational (propensity scores, instrumental variables, multiple baseline design, interrupted time series, difference-in-difference, and regression discontinuity). For each design, the unique characteristics are presented including the advantages and disadvantages compared to the RCT, illustrated by empirical examples in occupational health. This overview shows that several appropriate alternatives for the RCT design are feasible and available, which may provide sufficiently strong evidence to guide decisions on implementation of interventions in workplaces. Researchers are encouraged to continue exploring these designs and thus contribute to evidence-based occupational health.

Comparative quantification of health risks: Conceptual framework and methodological issues

PubMed Central

Murray, Christopher JL; Ezzati, Majid; Lopez, Alan D; Rodgers, Anthony; Vander Hoorn, Stephen

2003-01-01

Reliable and comparable analysis of risks to health is key for preventing disease and injury. Causal attribution of morbidity and mortality to risk factors has traditionally been conducted in the context of methodological traditions of individual risk factors, often in a limited number of settings, restricting comparability. In this paper, we discuss the conceptual and methodological issues for quantifying the population health effects of individual or groups of risk factors in various levels of causality using knowledge from different scientific disciplines. The issues include: comparing the burden of disease due to the observed exposure distribution in a population with the burden from a hypothetical distribution or series of distributions, rather than a single reference level such as non-exposed; considering the multiple stages in the causal network of interactions among risk factor(s) and disease outcome to allow making inferences about some combinations of risk factors for which epidemiological studies have not been conducted, including the joint effects of multiple risk factors; calculating the health loss due to risk factor(s) as a time-indexed "stream" of disease burden due to a time-indexed "stream" of exposure, including consideration of discounting; and the sources of uncertainty. PMID:12780936

Differential change in integrative psychotherapy: a re-analysis of a change-factor based RCT in a naturalistic setting.

PubMed

Holtforth, Martin Grosse; Wilm, Katharina; Beyermann, Stefanie; Rhode, Annemarie; Trost, Stephanie; Steyer, Rolf

2011-11-01

General Psychotherapy (GPT; Grawe, 1997) is a research-informed psychotherapy that combines cognitive-behavioral and process-experiential techniques and that assumes motivational clarification and problem mastery as central mechanisms of change. To isolate the effect of motivational clarification, GPT was compared to a treatment that proscribed motivational clarification (General Psychotherapy Minus Clarification, GPT-C) in a randomized-controlled trial with 67 diagnostically heterogeneous outpatients. Previous analyses demonstrated equal outcomes and some superiority for highly avoidant patients in GPT. Re-analyses using causal-analytic methods confirmed equal changes, but also showed superior effects for GPT in highly symptomatic patients. Results are discussed regarding theory, methodological limitations, and implications for research and practice.

Vitalistic thinking in adults.

PubMed

Wilson, Stuart

2013-11-01

Vitalistic thinking has traditionally been associated with reasoning about biological phenomena. The current research aimed to investigate a broader range of vitalistic thinking than previously studied. Esoteric notions of 'energy' are frequently used by individuals when making causal attributions for strange occurrences, and previous literature has linked such thinking with paranormal, magical, and superstitious beliefs. Two experiments are described that aim to investigate whether adults are vitalistic when asked to make causal judgments, and whether this can be predicted by thinking styles and prior paranormal belief. Experiment 1 asked participants to rate three causal options (one of which was vitalistic) for six vignettes. Scores on one dimension of paranormal belief (New Age Philosophy) and analytical thinking significantly predicted vitalism, but scores on intuitive thinking and Traditional Paranormal Beliefs did not. Experiment 2 extended the findings by asking participants to generate their own causal responses. Again, paranormal belief was found to be the best predictor of vitalism, but this time Traditional Paranormal Beliefs were associated with vitalistic responses whilst both intuitive and analytical thinking were unable to significantly predict classification. Results challenge previous findings, suggesting that vitalistic thinking may operate differently when applied to everyday causal reasoning. © 2012 The British Psychological Society.

Causal inference and longitudinal data: a case study of religion and mental health.

PubMed

VanderWeele, Tyler J; Jackson, John W; Li, Shanshan

2016-11-01

We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.

Causal interactions in resting-state networks predict perceived loneliness.

PubMed

Tian, Yin; Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

Causal interactions in resting-state networks predict perceived loneliness

PubMed Central

Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual. PMID:28545125

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

Causal mapping of emotion networks in the human brain: Framework and initial findings.

PubMed

Dubois, Julien; Oya, Hiroyuki; Tyszka, J Michael; Howard, Matthew; Eberhardt, Frederick; Adolphs, Ralph

2017-11-13

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI. Copyright © 2017 Elsevier Ltd. All rights reserved.

A review of current evidence for the causal impact of attentional bias on fear and anxiety.

PubMed

Van Bockstaele, Bram; Verschuere, Bruno; Tibboel, Helen; De Houwer, Jan; Crombez, Geert; Koster, Ernst H W

2014-05-01

Prominent cognitive theories postulate that an attentional bias toward threatening information contributes to the etiology, maintenance, or exacerbation of fear and anxiety. In this review, we investigate to what extent these causal claims are supported by sound empirical evidence. Although differences in attentional bias are associated with differences in fear and anxiety, this association does not emerge consistently. Moreover, there is only limited evidence that individual differences in attentional bias are related to individual differences in fear or anxiety. In line with a causal relation, some studies show that attentional bias precedes fear or anxiety in time. However, other studies show that fear and anxiety can precede the onset of attentional bias, suggesting circular or reciprocal causality. Importantly, a recent line of experimental research shows that changes in attentional bias can lead to changes in anxiety. Yet changes in fear and anxiety also lead to changes in attentional bias, which confirms that the relation between attentional bias and fear and anxiety is unlikely to be unidirectional. Finally, a similar causal relation between interpretation bias and anxiety has been documented. In sum, there is evidence in favor of causality, yet a strict unidirectional cause-effect model is unlikely to hold. The relation between attentional bias and fear and anxiety is best described as a bidirectional, maintaining, or mutually reinforcing relation.

Consequences of bullying victimization in childhood and adolescence: A systematic review and meta-analysis

PubMed Central

Moore, Sophie E; Norman, Rosana E; Suetani, Shuichi; Thomas, Hannah J; Sly, Peter D; Scott, James G

2017-01-01

AIM To identify health and psychosocial problems associated with bullying victimization and conduct a meta-analysis summarizing the causal evidence. METHODS A systematic review was conducted using PubMed, EMBASE, ERIC and PsycINFO electronic databases up to 28 February 2015. The study included published longitudinal and cross-sectional articles that examined health and psychosocial consequences of bullying victimization. All meta-analyses were based on quality-effects models. Evidence for causality was assessed using Bradford Hill criteria and the grading system developed by the World Cancer Research Fund. RESULTS Out of 317 articles assessed for eligibility, 165 satisfied the predetermined inclusion criteria for meta-analysis. Statistically significant associations were observed between bullying victimization and a wide range of adverse health and psychosocial problems. The evidence was strongest for causal associations between bullying victimization and mental health problems such as depression, anxiety, poor general health and suicidal ideation and behaviours. Probable causal associations existed between bullying victimization and tobacco and illicit drug use. CONCLUSION Strong evidence exists for a causal relationship between bullying victimization, mental health problems and substance use. Evidence also exists for associations between bullying victimization and other adverse health and psychosocial problems, however, there is insufficient evidence to conclude causality. The strong evidence that bullying victimization is causative of mental illness highlights the need for schools to implement effective interventions to address bullying behaviours. PMID:28401049

Causal impulse response for circular sources in viscous media

PubMed Central

Kelly, James F.; McGough, Robert J.

2008-01-01

The causal impulse response of the velocity potential for the Stokes wave equation is derived for calculations of transient velocity potential fields generated by circular pistons in viscous media. The causal Green’s function is numerically verified using the material impulse response function approach. The causal, lossy impulse response for a baffled circular piston is then calculated within the near field and the far field regions using expressions previously derived for the fast near field method. Transient velocity potential fields in viscous media are computed with the causal, lossy impulse response and compared to results obtained with the lossless impulse response. The numerical error in the computed velocity potential field is quantitatively analyzed for a range of viscous relaxation times and piston radii. Results show that the largest errors are generated in locations near the piston face and for large relaxation times, and errors are relatively small otherwise. Unlike previous frequency-domain methods that require numerical inverse Fourier transforms for the evaluation of the lossy impulse response, the present approach calculates the lossy impulse response directly in the time domain. The results indicate that this causal impulse response is ideal for time-domain calculations that simultaneously account for diffraction and quadratic frequency-dependent attenuation in viscous media. PMID:18397018

A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates

PubMed Central

Tubau, Elisabet; Matute, Helena

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students. PMID:29385184

Bulk viscous cosmology with causal transport theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Piattella, Oliver F.; Fabris, Júlio C.; Zimdahl, Winfried, E-mail: oliver.piattella@gmail.com, E-mail: fabris@pq.cnpq.br, E-mail: winfried.zimdahl@pq.cnpq.br

2011-05-01

We consider cosmological scenarios originating from a single imperfect fluid with bulk viscosity and apply Eckart's and both the full and the truncated Müller-Israel-Stewart's theories as descriptions of the non-equilibrium processes. Our principal objective is to investigate if the dynamical properties of Dark Matter and Dark Energy can be described by a single viscous fluid and how such description changes when a causal theory (Müller-Israel-Stewart's, both in its full and truncated forms) is taken into account instead of Eckart's non-causal one. To this purpose, we find numerical solutions for the gravitational potential and compare its behaviour with the corresponding ΛCDMmore » case. Eckart's and the full causal theory seem to be disfavoured, whereas the truncated theory leads to results similar to those of the ΛCDM model for a bulk viscous speed in the interval 10{sup −11} || cb{sup 2} ∼< 10{sup −8}.« less

Functional clustering of time series gene expression data by Granger causality

PubMed Central

2012-01-01

Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

Differences in Causal Estimates from Longitudinal Analyses of Residualized versus Simple Gain Scores: Contrasting Controls for Selection and Regression Artifacts

ERIC Educational Resources Information Center

Larzelere, Robert E.; Ferrer, Emilio; Kuhn, Brett R.; Danelia, Ketevan

2010-01-01

This study estimates the causal effects of six corrective actions for children's problem behaviors, comparing four types of longitudinal analyses that correct for pre-existing differences in a cohort of 1,464 4- and 5-year-olds from Canadian National Longitudinal Survey of Children and Youth (NLSCY) data. Analyses of residualized gain scores found…

Alpha Oscillations Are Causally Linked to Inhibitory Abilities in Ageing.

PubMed

Borghini, Giulia; Candini, Michela; Filannino, Cristina; Hussain, Masud; Walsh, Vincent; Romei, Vincenzo; Zokaei, Nahid; Cappelletti, Marinella

2018-05-02

Aging adults typically show reduced ability to ignore task-irrelevant information, an essential skill for optimal performance in many cognitive operations, including those requiring working memory (WM) resources. In a first experiment, young and elderly human participants of both genders performed an established WM paradigm probing inhibitory abilities by means of valid, invalid, and neutral retro-cues. Elderly participants showed an overall cost, especially in performing invalid trials, whereas younger participants' general performance was comparatively higher, as expected.Inhibitory abilities have been linked to alpha brain oscillations but it is yet unknown whether in aging these oscillations (also typically impoverished) and inhibitory abilities are causally linked. To probe this possible causal link in aging, we compared in a second experiment parietal alpha-transcranial alternating current stimulation (tACS) with either no stimulation (Sham) or with two control stimulation frequencies (theta- and gamma-tACS) in the elderly group while performing the same WM paradigm. Alpha- (but not theta- or gamma-) tACS selectively and significantly improved performance (now comparable to younger adults' performance in the first experiment), particularly for invalid cues where initially elderly showed the highest costs. Alpha oscillations are therefore causally linked to inhibitory abilities and frequency-tuned alpha-tACS interventions can selectively change these abilities in the elderly. SIGNIFICANCE STATEMENT Ignoring task-irrelevant information, an ability associated to rhythmic brain activity in the alpha frequency band, is fundamental for optimal performance. Indeed, impoverished inhibitory abilities contribute to age-related decline in cognitive functions like working memory (WM), the capacity to briefly hold information in mind. Whether in aging adults alpha oscillations and inhibitory abilities are causally linked is yet unknown. We experimentally manipulated frequency-tuned brain activity using transcranial alternating current stimulation (tACS), combined with a retro-cue paradigm assessing WM and inhibition. We found that alpha-tACS induced a significant improvement in target responses and misbinding errors, two indexes of inhibition. We concluded that in aging alpha oscillations are causally linked to inhibitory abilities, and that despite being impoverished, these abilities are still malleable. Copyright © 2018 the authors 0270-6474/18/384419-12$15.00/0.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

Proximal, Distal, and the Politics of Causation: What’s Level Got to Do With It?

PubMed Central

Krieger, Nancy

2008-01-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal–distal divide—surprisingly embraced by both biomedical and social determinist frameworks—and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power. PMID:18172144

Measurement Models for Reasoned Action Theory

PubMed Central

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-01-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach. PMID:23243315

Proximal, distal, and the politics of causation: what's level got to do with it?

PubMed

Krieger, Nancy

2008-02-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal-distal divide--surprisingly embraced by both biomedical and social determinist frameworks--and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power.

Climate variability, food production shocks, and violent conflict in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Buhaug, Halvard; Benjaminsen, Tor A.; Sjaastad, Espen; Magnus Theisen, Ole

2015-12-01

Earlier research that reports a correlational pattern between climate anomalies and violent conflict routinely refers to drought-induced agricultural shocks and adverse economic spillover effects as a key causal mechanism linking the two phenomena. Comparing half a century of statistics on climate variability, food production, and political violence across Sub-Saharan Africa, this study offers the most precise and theoretically consistent empirical assessment to date of the purported indirect relationship. The analysis reveals a robust link between weather patterns and food production where more rainfall generally is associated with higher yields. However, the second step in the causal model is not supported; agricultural output and violent conflict are only weakly and inconsistently connected, even in the specific contexts where production shocks are believed to have particularly devastating social consequences. Although this null result could, in theory, be fully compatible with recent reports of food price-related riots, it suggests that the wider socioeconomic and political context is much more important than drought and crop failures in explaining violent conflict in contemporary Africa.

Effects of urban sprawl and vehicle miles traveled on traffic fatalities.

PubMed

Yeo, Jiho; Park, Sungjin; Jang, Kitae

2015-01-01

Previous research suggests that urban sprawl increases auto-dependency and that excessive auto use increases the risk of traffic fatalities. This indirect effect of urban sprawl on traffic fatalities is compared to non-vehicle miles traveled (VMT)-related direct effect of sprawl on fatalities. We conducted a path analysis to examine the causal linkages among urban sprawl, VMT, traffic fatalities, income, and fuel cost. The path diagram includes 2 major linkages: the direct relationship between urban sprawl and traffic fatalities and the indirect effect on fatalities through increased VMT in sprawling urban areas. To measure the relative strength of these causal linkages, path coefficients are estimated using data collected nationally from 147 urbanized areas in the United States. Through both direct and indirect paths, urban sprawl is associated with greater numbers of traffic fatalities, but the direct effect of sprawl on fatalities is more influential than the indirect effect. Enhancing traffic safety can be achieved by impeding urban sprawl and encouraging compact development. On the other hand, policy tools reducing VMT may be less effective than anticipated for traffic safety.

Correlation of causal factors that influence construction safety performance: A model.

PubMed

Rodrigues, F; Coutinho, A; Cardoso, C

2015-01-01

The construction sector has presented positive development regarding the decrease in occupational accident rates in recent years. Regardless, the construction sector stands out systematically from other industries due to its high number of fatalities. The aim of this paper is to deeply understand the causality of construction accidents from the early design phase through a model. This study reviewed several research papers presenting various analytical models that correlate the contributing factors to occupational accidents in this sector. This study also analysed different construction projects and conducted a survey of design and site supervision teams. This paper proposes a model developed from the analysis of existing ones, which correlates the causal factors through all the construction phases. It was concluded that effective risk prevention can only be achieved by a global correlation of causal factors including not only production ones but also client requirements, financial climate, design team competence, project and risk management, financial capacity, health and safety policy and early planning. Accordingly, a model is proposed.

Academic Procrastination: The Relationship Between Causal Attribution Styles and Behavioral Postponement

PubMed Central

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

Objective: This research was conducted to study the relationship between attribution and academic procrastination in University Students. Methods: The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). Results: The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. Conclusion: We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning. PMID:24644450

Assessing causality in drug policy analyses: How useful are the Bradford Hill criteria in analysing take-home naloxone programs?

PubMed

Olsen, Anna; McDonald, David; Lenton, Simon; Dietze, Paul M

2018-05-01

The Bradford Hill criteria for assessing causality are useful in assembling evidence, including within complex policy analyses. In this paper, we argue that the implementation of take-home naloxone (THN) programs in Australia and elsewhere reflects sensible, evidence-based public health policy, despite the absence of randomised controlled trials. However, we also acknowledge that the debate around expanding access to THN would benefit from a careful consideration of causal inference and health policy impact of THN program implementation. Given the continued debate around expanding access to THN, and the relatively recent access to new data from implementation studies, two research groups independently conducted Bradford Hill analyses in order to carefully consider causal inference and health policy impact. Hill's criteria offer a useful analytical tool for interpreting current evidence on THN programs and making decisions about the (un)certainty of THN program safety and effectiveness. © 2017 Australasian Professional Society on Alcohol and other Drugs.

Did she jump because she was the big sister or because the trampoline was safe? Causal inference and the development of social attribution.

PubMed

Seiver, Elizabeth; Gopnik, Alison; Goodman, Noah D

2013-01-01

Children rely on both evidence and prior knowledge to make physical causal inferences; this study explores whether they make attributions about others' behavior in the same manner. A total of one hundred and fifty-nine 4- and 6-year-olds saw 2 dolls interacting with 2 activities, and explained the dolls' actions. In the person condition, each doll acted consistently across activities, but differently from each other. In the situation condition, the two dolls acted differently for each activity, but both performed the same actions. Both age groups provided more "person" explanations (citing features of the doll) in the person condition than in the situation condition. In addition, 6-year-olds showed an overall bias toward "person" explanations. As in physical causal inference, social causal inference combines covariational evidence and prior knowledge. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

Status Report on Speech Research. A Report on the Status and Progress of Studies on the Nature of Speech, Instrumentation for Its Investigation, and Practical Applications.

DTIC Science & Technology

1984-08-01

6, 391-395. Abbs, J. H., & Gracco, V. L. (in press). Control of complex motor gestures 0 and orofacial muscle responses to load perturbations of the...E2, and E, are on the same world line where %,! E. is causally constrained by E2 and E. is causally constrained by El. You take pains to note that the

The causal role of smoking on the risk of hip or knee replacement due to primary osteoarthritis: a Mendelian randomisation analysis of the HUNT study.

PubMed

Johnsen, M B; Vie, G Å; Winsvold, B S; Bjørngaard, J H; Åsvold, B O; Gabrielsen, M E; Pedersen, L M; Hellevik, A I; Langhammer, A; Furnes, O; Flugsrud, G B; Skorpen, F; Romundstad, P R; Storheim, K; Nordsletten, L; Zwart, J A

2017-06-01

Smoking has been associated with a reduced risk of hip and knee osteoarthritis (OA) and subsequent joint replacement. The aim of the present study was to assess whether the observed association is likely to be causal. 55,745 participants of a population-based cohort were genotyped for the rs1051730 C > T single-nucleotide polymorphism (SNP), a proxy for smoking quantity among smokers. A Mendelian randomization analysis was performed using rs1051730 as an instrument to evaluate the causal role of smoking on the risk of hip or knee replacement (combined as total joint replacement (TJR)). Association between rs1051730 T alleles and TJR was estimated by hazard ratios (HRs) and 95% confidence intervals (CIs). All analyses were adjusted for age and sex. Smoking quantity (no. of cigarettes) was inversely associated with TJR (HR 0.97, 95% CI 0.97-0.98). In the Mendelian randomization analysis, rs1051730 T alleles were associated with reduced risk of TJR among current smokers (HR 0.84, 95% CI 0.76-0.98, per T allele), however we found no evidence of association among former (HR 0.97, 95% CI 0.88-1.07) and never smokers (HR 0.97, 95% CI 0.89-1.06). Neither adjusting for body mass index (BMI), cardiovascular disease (CVD) nor accounting for the competing risk of mortality substantially changed the results. This study suggests that smoking may be causally associated with the reduced risk of TJR. Our findings add support to the inverse association found in previous observational studies. More research is needed to further elucidate the underlying mechanisms of this causal association. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Establishing causality in the decline and deformity of amphibians: The amphibian research and monitoring initiative model

USGS Publications Warehouse

Little, E.E.; Bridges, C.M.; Linder, G.; Boone, M.; ,

2003-01-01

Research to date has indicated that a range of environmental variables such as disease, parasitism, predation, competition, environmental contamination, solar ultraviolet radiation, climate change, or habitat alteration may be responsible for declining amphibian populations and the appearance of deformed organisms, yet in many cases no definitive environmental variable stands out as a causal factor. Multiple Stressors are often present in the habitat, and interactions among these can magnify injury to biota. This raises the possibility that the additive or synergistic impact of these Stressors may be the underlying cause of amphibian declines. Effective management for the restoration of amphibian populations requires the identification of causal factors contributing to their declines. A systematic approach to determine causality is especially important because initial impressions may be misleading or ambiguous. In addition, the evaluation of amphibian populations requires consideration of a broader spatial scale than commonly used in regulatory monitoring. We describe a systematic three-tiered approach to determine causality in amphibian declines and deformities. Tier 1 includes an evaluation of historic databases and extant data and would involve a desktop synopsis of the status of various stressors as well as site visits. Tier 2 studies are iterative, hypothesis driven studies beginning with general tests and continuing with analyses of increasing complexity as certain stressors are identified for further investigation. Tier 3 applies information developed in Tier 2 as predictive indicators of habitats and species at risk over broad landscape scales and provides decision support for the adaptive management of amphibian recovery. This comprehensive, tiered program could provide a mechanistic approach to identifying and addressing specific stressors responsible for amphibian declines across various landscapes.

Research requirements to improve safety of civil helicopters

NASA Technical Reports Server (NTRS)

Waters, K. T.

1977-01-01

Helicopter and fixed-wing accident data were reviewed and major accident causal factors were established. The impact of accidents on insurance rates was examined and the differences in fixed-wing and helicopter accident costs discussed. The state of the art in civil helicopter safety was compared to military helicopters. Goals were established based on incorporation of known technology and achievable improvements that require development, as well as administrative-type changes such as the impact of improved operational planning, training, and human factors effects. Specific R and D recommendations are provided with an estimation of the payoffs, timing, and development costs.

Fundamentals of clinical methodology: 2. Etiology.

PubMed

Sadegh-Zadeh, K

1998-03-01

The concept of etiology is analyzed and the possibilities and limitations of deterministic, probabilistic, and fuzzy etiology are explored. Different kinds of formal structures for the relation of causation are introduced which enable us to explicate the notion of cause on qualitative, comparative, and quantitative levels. The conceptual framework developed is an approach to a theory of causality that may be useful in etiologic research, in building nosological systems, and in differential diagnosis, therapeutic decision-making, and controlled clinical trials. The bearings of the theory are exemplified by examining the current Chlamydia pneumoniae hypothesis on the incidence of myocardial infarction.

Research Priorities in the Field of Posttraumatic Pain and Disability: Results of a Transdisciplinary Consensus-Generating Workshop

PubMed Central

Elliott, James M.; Lee, Joshua; Loh, Eldon; Schabrun, Siobhan; Siqueira, Walter L.; Corneil, Brian D.; Aal, Bill; Birmingham, Trevor; Brown, Amy; Dickey, James P.; Dixon, S. Jeffrey; Fraser, Douglas D.; Gati, Joseph S.; Gloor, Gregory B.; Good, Gordon; Holdsworth, David; McLean, Samuel A.; Millard, Wanda; Miller, Jordan; Sadi, Jackie; Seminowicz, David A.; Shoemaker, J. Kevin; Siegmund, Gunter P.; Vertseegh, Theodore; Wideman, Timothy H.

2016-01-01

Background. Chronic or persistent pain and disability following noncatastrophic “musculoskeletal” (MSK) trauma is a pervasive public health problem. Recent intervention trials have provided little evidence of benefit from several specific treatments for preventing chronic problems. Such findings may appear to argue against formal targeted intervention for MSK traumas. However, these negative findings may reflect a lack of understanding of the causal mechanisms underlying the transition from acute to chronic pain, rendering informed and objective treatment decisions difficult. The Canadian Institutes of Health Research (CIHR) Institute of Musculoskeletal Health and Arthritis (IMHA) has recently identified better understanding of causal mechanisms as one of three priority foci of their most recent strategic plan. Objectives. A 2-day invitation-only active participation workshop was held in March 2015 that included 30 academics, clinicians, and consumers with the purpose of identifying consensus research priorities in the field of trauma-related MSK pain and disability, prediction, and prevention. Methods. Conversations were recorded, explored thematically, and member-checked for accuracy. Results. From the discussions, 13 themes were generated that ranged from a focus on identifying causal mechanisms and models to challenges with funding and patient engagement. Discussion. Novel priorities included the inclusion of consumer groups in research from the early conceptualization and design stages and interdisciplinary longitudinal studies that include evaluation of integrated phenotypes and mechanisms. PMID:27445598

Using register data to estimate causal effects of interventions: An ex post synthetic control-group approach.

PubMed

Bygren, Magnus; Szulkin, Ryszard

2017-07-01

It is common in the context of evaluations that participants have not been selected on the basis of transparent participation criteria, and researchers and evaluators many times have to make do with observational data to estimate effects of job training programs and similar interventions. The techniques developed by researchers in such endeavours are useful not only to researchers narrowly focused on evaluations, but also to social and population science more generally, as observational data overwhelmingly are the norm, and the endogeneity challenges encountered in the estimation of causal effects with such data are not trivial. The aim of this article is to illustrate how register data can be used strategically to evaluate programs and interventions and to estimate causal effects of participation in these. We use propensity score matching on pretreatment-period variables to derive a synthetic control group, and we use this group as a comparison to estimate the employment-treatment effect of participation in a large job-training program. We find the effect of treatment to be small and positive but transient. Our method reveals a strong regression to the mean effect, extremely easy to interpret as a treatment effect had a less advanced design been used (e.g. a within-subjects panel data analysis), and illustrates one of the unique advantages of using population register data for research purposes.

Entanglement entropy in causal set theory

NASA Astrophysics Data System (ADS)

Sorkin, Rafael D.; Yazdi, Yasaman K.

2018-04-01

Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to render entanglement entropy finite. Formulating a notion of entanglement entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which its definition typically relies is not available. Instead, we appeal to the more global expression given in Sorkin (2012 (arXiv:1205.2953)) which, for a Gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region (its ‘Wightman function’ W(x, x') ). Carrying this formula over to the causal set, one obtains an entropy which is both finite and of a Lorentz invariant nature. We evaluate this global entropy-expression numerically for certain regions (primarily order-intervals or ‘causal diamonds’) within causal sets of 1  +  1 dimensions. For the causal-set counterpart of the entanglement entropy, we obtain, in the first instance, a result that follows a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one obtains an area law if one truncates the commutator function (‘Pauli–Jordan operator’) and the Wightman function by projecting out the eigenmodes of the Pauli–Jordan operator whose eigenvalues are too close to zero according to a geometrical criterion which we describe more fully below. In connection with these results and the questions they raise, we also study the ‘entropy of coarse-graining’ generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same, ‘universal’ behaviour in both cases.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

PubMed

Shim, Woo H; Baek, Kwangyeol; Kim, Jeong Kon; Chae, Yongwook; Suh, Ji-Yeon; Rosen, Bruce R; Jeong, Jaeseung; Kim, Young R

2013-01-01

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

PubMed

Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

2016-01-01

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

General dependencies and causality analysis of road traffic fatalities in OECD countries.

PubMed

Yaseen, Muhammad Rizwan; Ali, Qamar; Khan, Muhammad Tariq Iqbal

2018-05-07

The road traffic accidents were responsible for material and human loss which was equal to 2.8 to 5% of gross national product (GNP). However, literature does not explore the elasticity coefficients and nexus of road traffic fatalities with foreign direct investment, health expenditures, trade openness, mobile subscriptions, the number of researchers in R&D department, and environmental particulate matter. This study filled this research gap by exploring the nexus between road traffic fatalities, foreign direct investment, health expenditures, trade openness, mobile subscriptions, the number of researchers, and environmental particulate matter in Organization for Economic Cooperation and Development (OECD) countries by using panel data from 1995 to 2015. The panel Autoregressive Distributed Lag (ARDL) bound test was used for the detection of cointegration between the variables after checking the stationarity in selected variables with different panel unit root tests. Panel vector error correction model explored the causality of road traffic fatalities, foreign direct investment, PM2.5 in the environment, and trade openness in the long run. Road traffic fatalities showed short run bi-directional causality with foreign direct investment and health expenditures. The short run bi-directional causality was also observed between trade and foreign direct investment and cellular mobile subscriptions and foreign direct investment. The panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) showed the 0.947% reduction in road fatalities for 1% increase in the health expenditures in OECD countries. The significant reduction in road fatalities was also observed due to 1% increase in trade openness and researchers in R&D, which implies the importance of trade and research for road safety. It is required to invest in the health sector for the safety of precious human lives like the hospitals with latest medical equipment and improvement in the emergency services in the country. The research and development activities should be enhanced especially for the health and transportation sectors. The trade of environment-friendly technology should be promoted for the protection of environment.

Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

PubMed

Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

2016-02-29

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Improving Education through Research? From Effectiveness, Causality and Technology to Purpose, Complexity and Culture

ERIC Educational Resources Information Center

Biesta, Gert

2016-01-01

This paper focuses on the role of research in the improvement of educational practice. I use the "10 Principles for Effective Pedagogy," which were formulated on the basis of research conducted in the UK's Teacher and Learning Research Programme as an example to highlight some common problems in the discussion about research and…

Assessing the Impact of Drug Use and Drug Selling on Violent Offending in a Panel of Delinquent Youth

PubMed Central

Phillips, Matthew D.

2016-01-01

Despite a vast number of empirical studies arguing for or against a causal relationship between illegal drug use and selling and violent behavior, the debate continues. In part this is due to methodological weaknesses of previous research. Using data from the Rochester Youth Development Study, the current study seeks to improve on prior research designs to allow for a more precise examination of the mechanisms that lead from an individual’s drug use (chiefly, marijuana use in the current sample) and drug selling to violent action. Results will allow for greater confidence in making causal inference regarding a long-standing concern in the discipline. PMID:26889079

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

An Introduction to Causal Inference

DTIC Science & Technology

2009-11-02

Introduction The questions that motivate most studies in the health, social and behavioral sciences are not associational but causal in nature. For example...what is the efficacy of a given drug in a given population? Whether data can prove an employer guilty of hiring discrimination? What fraction of past...a unifying theory, called “structural,” within which most (if not all) aspects of causation can be formulated, analyzed and compared, thirdly

Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.

PubMed

Klasnja, Predrag; Hekler, Eric B; Shiffman, Saul; Boruvka, Audrey; Almirall, Daniel; Tewari, Ambuj; Murphy, Susan A

2015-12-01

This article presents an experimental design, the microrandomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals' health behaviors. Microrandomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. The article describes the microrandomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Microrandomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions

PubMed Central

Klasnja, Predrag; Hekler, Eric B.; Shiffman, Saul; Boruvka, Audrey; Almirall, Daniel; Tewari, Ambuj; Murphy, Susan A.

2015-01-01

Objective This paper presents an experimental design, the micro-randomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals’ health behaviors. Micro-randomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. Methods The paper describes the micro-randomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Results Micro-randomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Conclusions Micro-randomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions’ effects, enabling creation of more effective JITAIs. PMID:26651463