Journal article
Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks
Structural equation modeling, Vol.31(3), pp.552-570
08/18/2023
DOI: 10.1080/10705511.2023.2222913
Abstract
We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and compo-sites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the lavaan SEM package in R produced results virtually identical to those obtained from the mGENOVA package, which historically has served as the gold standard for conducting multivariate GT analyses. We further extended lavaan analyses beyond what mGENOVA allows to produce Monte Carlo based confidence intervals for key GT parameters and correct score consistency and correlational indi-ces for effects of scale coarseness characteristic of binary and ordinal data. Our comprehensive online Supplemental Material includes code for performing all illustrated analyses using lavaan and mGENOVA.
Details
- Title: Subtitle
- Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks
- Creators
- Walter P. Vispoel - University of IowaHyeryung Lee - University of IowaHyeri Hong - California State University, Fresno
- Resource Type
- Journal article
- Publication Details
- Structural equation modeling, Vol.31(3), pp.552-570
- DOI
- 10.1080/10705511.2023.2222913
- ISSN
- 1070-5511
- eISSN
- 1532-8007
- Publisher
- Taylor & Francis
- Number of pages
- 19
- Language
- English
- Electronic publication date
- 08/18/2023
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984460280102771
Metrics
29 Record Views