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Published in:
The Observed Dependency of Longitudinal Data Read chapter Read first chapter

2015 | OriginalPaper | Chapter

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

Authors : Ting Wang, Phillip K. Wood, Andrew C. Heath

Published in: Dependent Data in Social Sciences Research

Publisher: Springer International Publishing

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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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previous chapter Longitudinal Analysis of Dyads Using Latent Variable Models: Current Practices and Constraints
next chapter Item Response Models for Dependent Data: Quasi-exact Tests for the Investigation of Some Preconditions for Measuring Change
Appendix
Available only for authorised users
Footnotes
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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Metadata
Title
Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison Approach
Authors
Ting Wang
Phillip K. Wood
Andrew C. Heath
Copyright Year
2015
Book
Dependent Data in Social Sciences Research

Print ISBN: 978-3-319-20584-7

Electronic ISBN: 978-3-319-20585-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20585-4_10

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