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Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison Approach

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Dependent Data in Social Sciences Research

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 145))

Abstract

As methodologists have increasingly noted, the role of psychometrics in operationalizing a construct is often overlooked when evaluating research claims (Borsboom, 2006). In a related vein, others have noted that psychological research appears to move away from assessment and interpretation of a single a priori statistical model to a more nuanced comparison of models which assess the trade-off between a model’s parsimony and complexity in explaining behavior (e.g., Rodgers, 2010). The genetic factor model is one such statistical model often used to estimate the relative contributions of genetic and environmental components of observed behavior in genetically informative designs (Heath, Neale, Hewitt, Eaves, & Fulker, 1989; Martin, Eaves et al., 1977; Neale & Cardon, 1992). Mathematically, the genetic factor model decomposes observed phenotypic variability into additive genetic (A), common (C), and unique (E) environmental components and is, for that reason, often referred to as the ACE model.

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Notes

  1. 1.

    It should be noted that when all Bayesian models which included a common environmental effect failed to find environmental effects greater than zero, regardless of whether a tau equivalent, congeneric or random intercept model was used to model the component.

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  1. Department of Psychological Sciences, University of Missouri, Columbia, MO, USA

    Ting Wang, Phillip K. Wood & Andrew C. Heath

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  1. Ting Wang
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  2. Phillip K. Wood
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  1. Institute of Psychology, University of Erlangen-Nuremberg, Erlangen, Germany

    Mark Stemmler

  2. Department of Psychology, Michigan State University, East Lansing, Michigan, USA

    Alexander von Eye

  3. Department of Educational, School, and Counseling Psychology,College of Education, University Of Missouri, Columbia, USA

    Wolfgang Wiedermann

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Mplus Program for Fitting Bayesian One-Factor ACE model & Mplus Program for Fitting Final Bayesian Random Intercept Model for Simulated Data (DOCX 25 kb)

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Wang, T., Wood, P.K., Heath, A.C. (2015). Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison Approach. In: Stemmler, M., von Eye, A., Wiedermann, W. (eds) Dependent Data in Social Sciences Research. Springer Proceedings in Mathematics & Statistics, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-319-20585-4_10

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