Publications by authors named "Felix Thoemmes"

26 Publications

  • Page 1 of 1

The Taboo Against Explicit Causal Inference in Nonexperimental Psychology.

Perspect Psychol Sci 2020 09 29;15(5):1243-1255. Epub 2020 Jul 29.

Department of Human Development, Cornell University.

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects-the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.
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http://dx.doi.org/10.1177/1745691620921521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472833PMC
September 2020

Positive affect and chronic pain: a preregistered systematic review and meta-analysis.

Pain 2020 06;161(6):1140-1149

Department of Medicine, Weill Cornell Medical College, New York City, NY, United States.

Chronic noncancer pain (CNCP) is a significant health burden among adults. Standard behavioral therapies typically focus on targeting negative affect (NA) and yield only modest treatment effects. The aims of this study were to systematically review and investigate the association between positive affect (PA) and pain severity among adults with CNCP. Databases that were searched included MEDLINE (PubMed), PsycINFO, CINAHL, ProQuest Dissertations and Theses, OLASTER, Open Grey, and PsyArXiv (inception to July 23, 2019). We analyzed studies that: (1) used observational, experimental, or intervention study designs; (2) enrolled individuals with CNCP (pain ≥ 12 weeks); and (3) reported full quantitative results on outcomes. Two researchers independently screened articles, extracted data, and assessed the risk of bias. The main meta-analysis was followed by subgroup analyses. All analyses were performed using random-effects models. Formal tests for heterogeneity (Q-statistic; I) and publication bias (p-curve and p-uniform*) were performed. We meta-analyzed 29 studies with 3521 participants. Results demonstrated that PA inversely impacts pain severity in people with CNCP (r = -0.23). Subgroup analyses showed a significant effect for gender and marginally significant effects for age in studies that adjusted for NA. On average, effect sizes for observational studies were larger in studies with a higher proportion of female respondents and in studies that did not adjust for NA. Finally, larger effect sizes were found in intervention studies with older compared with younger samples.
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http://dx.doi.org/10.1097/j.pain.0000000000001828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230030PMC
June 2020

The Assumptions of Direction Dependence Analysis.

Authors:
Felix Thoemmes

Multivariate Behav Res 2020 Jul-Aug;55(4):516-522. Epub 2019 Jun 19.

Department of Human Development, Cornell University.

Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions.
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http://dx.doi.org/10.1080/00273171.2019.1608800DOI Listing
June 2019

Analysis of Variance Models with Stochastic Group Weights.

Multivariate Behav Res 2019 Jul-Aug;54(4):542-554. Epub 2019 Jan 20.

b Cornell University.

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models - a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided.
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http://dx.doi.org/10.1080/00273171.2018.1548960DOI Listing
December 2019

Local fit evaluation of structural equation models using graphical criteria.

Psychol Methods 2018 Mar 20;23(1):27-41. Epub 2017 Jul 20.

Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences.

Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record
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http://dx.doi.org/10.1037/met0000147DOI Listing
March 2018

Does Gender of Administrator Matter? National Study Explores U.S. University Administrators' Attitudes About Retaining Women Professors in STEM.

Front Psychol 2017 22;8:700. Epub 2017 May 22.

Department of Human Development, Cornell UniversityIthaca, NY, United States.

Omnipresent calls for more women in university administration presume women will prioritize using resources and power to increase female representation, especially in STEM fields where women are most underrepresented. However, empirical evidence is lacking for systematic differences in female vs. male administratorsŠ attitudes. Do female administrators agree on which strategies are best, and do men see things differently? We explored United States college and university administratorsŠ opinions regarding strategies, policies, and structural changes in their organizations designed to increase women professorsŠ representation and retention in STEM fields. A comprehensive review of past research yielded a database of potentially-effective, recommended policies. A survey based on these policies was sent to provosts, deans, associate deans, and department chairs of STEM fields at 96 public and private research universities across the U.S. These administrators were asked to rate the quality and feasibility of each strategy; 474 provided data, of which 334 contained complete numerical data used in the analyses. Our data revealed that female (vs. male) administrators believed the 44 strategies were higher in overall-but not higher in -with 9 strategies perceived differently by women and men, after imposing conservative statistical controls. There was broad general agreement on the rankings of the 44 strategies. Women (vs. men) gave higher quality ratings to increasing the value of teaching, service, and administrative experience in tenure/promotion decisions, increasing flexibility of federal-grant funding to accommodate mothers, conducting gender-equity research, and supporting shared tenure lines enabling work-life balance. Women (vs. men) believed it was more feasible for men to stop the tenure clock for 1 year for childrearing and for universities to support requests for shared tenure lines, but less feasible for women to chair search committees. Our national survey thus supported the belief that placing women into administration creates greater endorsement of strategies to attract and retain women in STEM, although the effectiveness of these strategies was outside the scope of this research. Topics of disagreement between women and men are potentially important focuses of future policy, because female administrators may have insights into how to retain women that male administrators do not share.
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http://dx.doi.org/10.3389/fpsyg.2017.00700DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439084PMC
May 2017

The effects of exposure to objective coherence on perceived meaning in life: a preregistered direct replication of Heintzelman, Trent & King (2013).

R Soc Open Sci 2016 Nov 23;3(11):160431. Epub 2016 Nov 23.

Department of Human Development , Cornell University , G06 Martha Van Rensselaer Hall, Ithaca, NY 14853-4401 , USA.

Having a sense of meaning in life (MIL) has been acknowledged as a catalyst to psychological flourishing. As such, understanding ways to promote MIL represents a worthy goal for those interested in bolstering positive outcomes. This study sought to replicate the findings of Heintzelman, Trent & King (2013 , 991-998 (doi:10.1177/0956797612465878)), who found that MIL could be influenced by external stimulation. Their findings suggest that exposure to coherent stimuli produces significantly higher MIL scores than exposure to incoherent stimuli. Using materials and methodology provided by the corresponding author of the original paper, this study attempted to directly test this manipulation under conditions with increased statistical power. All tests, however, failed to replicate. Possible explanations for these discrepant findings are discussed, and potential future directions for this area of the literature are proposed.
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http://dx.doi.org/10.1098/rsos.160431DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5180121PMC
November 2016

What is the biological reality of gene-environment interaction estimates? An assessment of bias in developmental models.

J Child Psychol Psychiatry 2016 11 31;57(11):1258-1267. Epub 2016 May 31.

Department of Human Development, Cornell University, Ithaca, NY, USA.

Background: Standard models used to test gene-environment interaction (G × E) hypotheses make the causal assumption that there are no unobserved variables that could be biasing the interaction estimate. Whether this assumption can be met in nonexperimental studies is unclear because the interactive biological pathways from genetic polymorphisms and environments to behavior, and the confounders that can be introduced along these pathways, are often not delineated. This is problematic in the context of studies focused on caregiver-child dyads, in which common genes and environments induce gene-environment correlation. To address the impact of sources of bias in G × E models specifically assessing the interaction between child genotype and caregiver behavior, we provide a causal framework that integrates biological and statistical concepts of G × E, and assess the magnitude of bias introduced by various confounding pathways in different causal circumstances.

Methods: A simulation assessed the magnitude of bias introduced by four types of confounding pathways in different causal models. Unadjusted and adjusted statistical models were then applied to the simulated data to assess the efficacy of these procedures to capture unbiased G × E estimates. Finally, the simulation was run under null effects of the genotype to assess the impact of biasing sources on the false-positive rate.

Results: Common environmental pathways between caregiver and child inflated G × E estimates and raised the false-positive rate. Evocative effects of the child also inflated G × E estimates.

Conclusions: Gene-environment interaction studies should be approached with consideration to the causal pathways at play and the confounding opportunities along these pathways to facilitate the inclusion of adequate statistical controls and correct inferences from study findings. Bridging biological and statistical concepts of G × E can significantly improve research design and the communication of how a G × E process fits into a broader developmental framework.
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http://dx.doi.org/10.1111/jcpp.12579DOI Listing
November 2016

I wish I had (not) taken a gap-year? The psychological and attainment outcomes of different post-school pathways.

Dev Psychol 2015 Mar;51(3):323-33

Collegium for Advanced Studies, University of Helsinki.

Existing gap-year research indicates a number of benefits of a gap-year at the end of school and before university enrollment. Life span theory of control, however, suggests that direct goal investment, rather than delay, at developmental transitions is associated with more adaptive outcomes. Comparing these perspectives, the authors undertook 2 studies: 1 in Finland (N = 384, waves = 3) and 1 in Australia (N = 2,259, waves = 5) both with an initial time wave in the last year of high school. The authors explored the effects of a gap-year on both psychological and attainment outcomes using an extensive propensity score matching technique. The Finnish study found no difference in growth in goal commitment, effort, expectations of attainment and strain, or in actual university enrollment in those planning to enter university directly versus those who plan to take a gap-year. The Australian study found no difference in growth in outlooks for the future and career prospects, and life satisfaction between gap-year youth and direct university entrants. However, the study did find that gap-year students were more likely to drop out of a university degree. Implication for theory and practice are discussed.
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http://dx.doi.org/10.1037/a0038667DOI Listing
March 2015

A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.

Multivariate Behav Res 2014 Sep-Oct;49(5):443-59

b University of Tübingen.

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion; that is, taking all available auxiliary variables. In this article, we point out that there are some instances in which auxiliary variables exhibit the surprising property of increasing bias in missing data problems. In a series of focused simulation studies, we highlight some situations in which this type of biasing behavior can occur. We briefly discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.
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http://dx.doi.org/10.1080/00273171.2014.931799DOI Listing
March 2016

Theory and Analysis of Total, Direct, and Indirect Causal Effects.

Multivariate Behav Res 2014 Sep-Oct;49(5):425-42

e Arizona State University.

Mediation analysis, or more generally models with direct and indirect effects, are commonly used in the behavioral sciences. As we show in our illustrative example, traditional methods of mediation analysis that omit confounding variables can lead to systematically biased direct and indirect effects, even in the context of a randomized experiment. Therefore, several definitions of causal effects in mediation models have been presented in the literature (Baron & Kenny, 1986 ; Imai, Keele, & Tingley, 2010 ; Pearl, 2012 ). We illustrate the stochastic theory of causal effects as an alternative foundation of causal mediation analysis based on probability theory. In this theory we define total, direct, and indirect effects and show how they can be identified in the context of our illustrative example. A particular strength of the stochastic theory of causal effects are the causality conditions that imply causal unbiasedness of effect estimates. The causality conditions have empirically testable implications and can be used for covariate selection. In the discussion, we highlight some similarities and differences of the stochastic theory of causal effects with other theories of causal effects.
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http://dx.doi.org/10.1080/00273171.2014.931797DOI Listing
March 2016

Continuously Cumulating Meta-Analysis and Replicability.

Perspect Psychol Sci 2014 May;9(3):333-42

University of California, Riverside.

The current crisis in scientific psychology about whether our findings are irreproducible was presaged years ago by Tversky and Kahneman (1971), who noted that even sophisticated researchers believe in the fallacious Law of Small Numbers-erroneous intuitions about how imprecisely sample data reflect population phenomena. Combined with the low power of most current work, this often leads to the use of misleading criteria about whether an effect has replicated. Rosenthal (1990) suggested more appropriate criteria, here labeled the continuously cumulating meta-analytic (CCMA) approach. For example, a CCMA analysis on a replication attempt that does not reach significance might nonetheless provide more, not less, evidence that the effect is real. Alternatively, measures of heterogeneity might show that two studies that differ in whether they are significant might have only trivially different effect sizes. We present a nontechnical introduction to the CCMA framework (referencing relevant software), and then explain how it can be used to address aspects of replicability or more generally to assess quantitative evidence from numerous studies. We then present some examples and simulation results using the CCMA approach that show how the combination of evidence can yield improved results over the consideration of single studies.
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http://dx.doi.org/10.1177/1745691614529796DOI Listing
May 2014

Propensity scores as a basis for equating groups: basic principles and application in clinical treatment outcome research.

J Consult Clin Psychol 2014 Oct 7;82(5):906-19. Epub 2014 Apr 7.

Department of Education and Psychology, Freie Universität Berlin.

A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome.
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http://dx.doi.org/10.1037/a0036387DOI Listing
October 2014

Understanding the durability of a fire department wellness program.

Am J Health Behav 2013 Sep;37(5):693-702

Washington State University Vancouver, Department of Teaching and Learning, Vancouver, WA, USA.

Objectives: To understand the influences associated with durability and diffusion of benefits of a fire service wellness program.

Methods: Qualitative assessment of group interviews.

Results: Five years following a controlled worksite wellness trial, behavioral improvements were durable and had diffused to control participants. These findings were associated with firefighters' team orientation, enacted healthy norms and competitiveness regarding the results of annual health assessments. The original intervention trial appeared to initiate individual change that coalesced into group effects. Secondary influences included increasing public awareness about health, newly hired younger firefighters, and a modicum of administrative support. Culture shift was achieved at the workplace.

Conclusions: Although the fire service is a unique occupation, these findings suggest general strategies to achieve durable positive health change in other occupational settings.
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http://dx.doi.org/10.5993/AJHB.37.5.13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3761399PMC
September 2013

Personality traits and living arrangements in young adulthood: selection and socialization.

Dev Psychol 2014 Mar 26;50(3):683-98. Epub 2013 Aug 26.

Center for Educational Science and Psychology, University of Tübingen.

Based on the social investment principle and theories of social relationship differentiation, the present study was conducted to investigate whether personality differences in high school predict young adults' living arrangements (with roommates or a romantic partner, alone, or staying with parents) 2 years later (selection) and whether these different social contexts provoke long-term personality changes (socialization). Using data from a 3-wave longitudinal study of 8,052 high school graduates in Germany, multinomial logistic regression analyses revealed substantial selection effects of the Big Five traits on living arrangements. Propensity score matching was applied to provide a strong test of the socialization effects of these living arrangements. Young adults who lived with roommates showed increases in Openness but the smallest increases in Conscientiousness. Living with a romantic partner was the most beneficial arrangement for the development of Conscientiousness. These results highlight the importance of social contexts for personality development in young adulthood.
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http://dx.doi.org/10.1037/a0034239DOI Listing
March 2014

Ambulatory monitoring in the genetics of psychosomatic medicine.

Psychosom Med 2012 May;74(4):349-55

Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Psychosomatic disorders are composed of an array of psychological, biologic, and environmental features. The existing evidence points to a role for genetic factors in explaining individual differences in the development and maintenance of a variety of disorders, but studies to date have not shown consistent and replicable effects. As such, the attempt to uncover individual differences in the expression of psychosomatic disorders as a function of genetic architecture requires careful attention to their phenotypic architecture or the various intermediate phenotypes that make up a heterogeneous disorder. Ambulatory monitoring offers a novel approach to measuring time-variant and situation-dependent intermediate phenotypes. Recent examples of the use of ambulatory monitoring in genetic studies of stress reactivity, chronic pain, alcohol use disorders, and psychosocial resilience are reviewed in an effort to highlight the benefits of ambulatory monitoring for genetic study designs.
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http://dx.doi.org/10.1097/PSY.0b013e3182544a74DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4564065PMC
May 2012

Military training and personality trait development: does the military make the man, or does the man make the military?

Psychol Sci 2012 Mar 24;23(3):270-7. Epub 2012 Jan 24.

Washington University in St. Louis, St. Louis, MO, USA.

Military experience is an important turning point in a person's life and, consequently, is associated with important life outcomes. Using a large longitudinal sample of German males, we examined whether personality traits played a role during this period. Results indicated that personality traits prospectively predicted the decision to enter the military. People lower in agreeableness, neuroticism, and openness to experience during high school were more likely to enter the military after graduation. In addition, military training was associated with changes in personality. Compared with a control group, military recruits had lower levels of agreeableness after training. These levels persisted 5 years after training, even after participants entered college or the labor market. This study is one of the first to identify life experiences associated with changes in personality traits. Moreover, our results suggest that military experiences may have a long-lasting influence on individual-level characteristics.
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http://dx.doi.org/10.1177/0956797611423545DOI Listing
March 2012

The Use of Propensity Scores for Nonrandomized Designs With Clustered Data.

Multivariate Behav Res 2011 May;46(3):514-43

b Arizona State University.

In this article we propose several modeling choices to extend propensity score analysis to clustered data. We describe different possible model specifications for estimation of the propensity score: single-level model, fixed effects model, and two random effects models. We also consider both conditioning within clusters and conditioning across clusters. We examine the underlying assumptions of these modeling choices and the type of randomized experiment approximated by each approach. Using a simulation study, we compare the relative performance of these modeling and conditioning choices in reducing bias due to confounding variables at both the person and cluster levels. An applied example based on a study by Hughes, Chen, Thoemmes, and Kwok (2010) is provided in which the effect of retention in Grade 1 on passing an achievement test in Grade 3 is evaluated. We find that models that consider the clustered nature of the data both in estimation of the propensity score and conditioning on the propensity score performed best in our simulation study; however, other modeling choices also performed well. The applied example illustrates practical limitations of these models when cluster sizes are small.
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http://dx.doi.org/10.1080/00273171.2011.569395DOI Listing
May 2011

A Systematic Review of Propensity Score Methods in the Social Sciences.

Multivariate Behav Res 2011 Feb;46(1):90-118

b Texas A&M University.

The use of propensity scores in psychological and educational research has been steadily increasing in the last 2 to 3 years. However, there are some common misconceptions about the use of different estimation techniques and conditioning choices in the context of propensity score analysis. In addition, reporting practices for propensity score analyses often lack important details that allow other researchers to confidently judge the appropriateness of reported analyses and potentially to replicate published findings. In this article we conduct a systematic literature review of a large number of published articles in major areas of social science that used propensity scores up until the fall of 2009. We identify common errors in estimation, conditioning, and reporting of propensity score analyses and suggest possible solutions.
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http://dx.doi.org/10.1080/00273171.2011.540475DOI Listing
February 2011

An Investigation of the Relationship Between Retention in First Grade and Performance on High Stakes Tests in 3 Grade.

Educ Eval Policy Anal 2010 May;32(2):166-182

Texas A&M University, Department of Educational Psychology.

The association between grade retention in first grade and passing the third grade state accountability tests, the Texas Assessment of Knowledge and Skills (TAKS) reading and math, was investigated in a sample of 769 students who were recruited into the study when they were in first grade. Of these 769 students, 165 were retained in first grade and 604 were promoted. Using propensity matching, we created five imputed datasets (average N=321) in which promoted and retained students were matched on 67 comprehensive covariates. Using GEE models, we obtained the association between retention and passing the 3(rd) grade TAKS reading and math tests. The positive association between retention and math scores was significant while the association was marginally significant for reading scores.
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http://dx.doi.org/10.3102/0162373710367682DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2901899PMC
May 2010

Long-term effects of a worksite health promotion program for firefighters.

Am J Health Behav 2010 Nov-Dec;34(6):695-706

Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA.

Objective: To describe effects of 2 worksite health promotion programs for firefighters, both immediate outcomes and the long-term consequences for 4 years following the interventions.

Methods: At baseline, 599 firefighters were assessed, randomized by fire station to control and 2 different intervention conditions, and reevaluated with 6 annual follow-up measurements.

Results: Both a team-centered peer-taught curriculum and an individual motivational interviewing intervention demonstrated positive effects on BMI, with team effects on nutrition behavior and physical activity at one year. Most differences between intervention and control groups dissipated at later annual assessments. However, the trajectory of behaviors across time generally was positive for all groups, consistent with lasting effects and diffusion of program benefits across experimental groups within the worksites.

Conclusions: Although one-year programmatic effects did not remain over time, the long-term pattern of behaviors suggested these worksites as a whole were healthier more than 3 years following the interventions.
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http://dx.doi.org/10.5993/ajhb.34.6.6DOI Listing
October 2010

Campbell's and Rubin's perspectives on causal inference.

Psychol Methods 2010 Mar;15(1):18-37

Psychology Department, Arizona State University, Tempe, AZ 85287-1104, USA.

Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on the identification of threats to validity and the inclusion of design features that may prevent those threats from occurring or render them implausible. Rubin's approach focuses on the precise specification of both the possible outcomes for each participant and assumptions that are mathematically sufficient to estimate the causal effect. In this article, the authors compare the perspectives provided by the 2 approaches on randomized experiments, broken randomized experiments in which treatment nonadherence or attrition occurs, and observational studies in which participants are assigned to treatments on an unknown basis. The authors highlight dimensions on which the 2 approaches have different emphases, including the roles of constructs versus operations, threats to validity versus assumptions, methods of addressing threats to internal validity and violations of assumptions, direction versus magnitude of causal effects, role of measurement, and causal generalization. The authors conclude that investigators can benefit from drawing on the strengths of both approaches in designing research.
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http://dx.doi.org/10.1037/a0015917DOI Listing
March 2010

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS.

Struct Equ Modeling 2010 ;17(3):510-534

Department of Educational Psychology, Texas A&M University.

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models.
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http://dx.doi.org/10.1080/10705511.2010.489379DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737006PMC
January 2010

Race as a moderator of the relationship between religiosity and political alignment.

Pers Soc Psychol Bull 2009 Mar 29;35(3):271-82. Epub 2008 Dec 29.

Department of Psychology, Arizona State University, P.O. Box 871104, Tempe, AZ 85287-1104, USA.

Religiosity, especially religious fundamentalism, is often assumed to have an inherent connection with conservative politics. This article proposes that the relationship varies by race in the United States. In Study 1, race moderated the relationships between religiosity indicators and political alignment in a nationally representative sample. In Study 2, the effect replicated in a student sample with more reliable measures. Among both Black and Latino Americans, the relationship between religiosity and conservative politics is far weaker than it is among White Americans, and it is sometimes altogether absent. In Study 3, a tradition-focused view of religion was found to more strongly mediate the link between religiosity and political attitudes among Whites than it did among Blacks and Latinos. It is argued that the relationship between religiosity and political alignment is best understood as a product of cultural-historical conditions associated with group memberships.
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http://dx.doi.org/10.1177/0146167208328064DOI Listing
March 2009

Two ways to be complex and why they matter: implications for attitude strength and lying.

J Pers Soc Psychol 2008 Nov;95(5):1029-44

Department of Psychology, University of Montana, Missoula, MT 59812, USA.

Integrative complexity broadly measures the structural complexity of statements. This breadth, although beneficial in multiple ways, can potentially hamper the development of specific theories. In response, the authors developed a model of complex thinking, focusing on 2 different ways that people can be complex within the integrative complexity system and subsequently developed measurements of each of these 2 routes: Dialectical complexity focuses on a dialectical tension between 2 or more competing perspectives, whereas elaborative complexity focuses on complexly elaborating on 1 singular perspective. The authors posit that many variables have different effects on these 2 forms of complexity and subsequently test this idea in 2 different theoretical domains. In Studies 1a, 1b, and 2, the authors demonstrate that variables related to attitude strength (e.g., domain importance, extremism, domain accessibility) decrease dialectical complexity but increase elaborative complexity. In Study 3, the authors show that counterattitudinal lying decreases dialectical complexity but increases elaborative complexity, implicating a strategic (as opposed to a cognitive strain) view of the lying-complexity relationship. The authors argue that this dual demonstration across 2 different theoretical domains helps establish the utility of the new model and measurements as well as offer the potential to reconcile apparent conflicts in the area of cognitive complexity.
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http://dx.doi.org/10.1037/a0013336DOI Listing
November 2008