Journal article
Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale
Structural equation modeling, Vol.31(1), pp.165-181
01/02/2024
DOI: 10.1080/10705511.2023.2187734
Abstract
Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM frameworks using multi-occasion data from the Rosenberg Self-Esteem Scale. We show how GT-SEMs can reproduce variance components for both relative and absolute error obtained from ANOVA models, estimate effects of changes made to measurement procedures and universes of generalization, incorporate estimation methods to correct for scale coarseness, represent essential tau-equivalent or congeneric relationships, include additional method factors for negatively and positively worded items, incorporate bifactor designs, allow for formal tests of model fit when warranted, and derive Monte Carlo confidence intervals for key parameters of interest. We provide code for conducting the demonstrated analyses using several statistical packages in extended online
Supplemental Material
.
Details
- Title: Subtitle
- Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale
- Creators
- Walter P. Vispoel - University of IowaHyeri Hong - California State University, FresnoHyeryung Lee - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Structural equation modeling, Vol.31(1), pp.165-181
- Publisher
- Routledge
- DOI
- 10.1080/10705511.2023.2187734
- ISSN
- 1070-5511
- eISSN
- 1532-8007
- Language
- English
- Electronic publication date
- 05/11/2023
- Date published
- 01/02/2024
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984408398602771
Metrics
6 Record Views