The Impact of Unmodeled Heteroskedasticity on Assessing Measurement Invariance in Single-group Models
Datos Bibliográficos
| ID | 21641638 |
|---|---|
| Autores | Laura Kolbe (0000-0003-0769-3391, University of Amsterdam, autor de correspondencia), Terrence D Jorgensen (0000-0001-5111-6773, University of Amsterdam), Dylan Molenaar (0000-0001-7108-4545, University of Amsterdam) |
| Año | 2021 |
| Volumen | 28 |
| Número | 1 |
| Páginas | 82-98 |
| Fecha de publicación | 2021-01-02 |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Identificadores de la revista | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2020.1766357 |
| OpenAlex | W3041720839 |
| Idioma | EN |
| Citas recibidas | 4 |
| Referencias citadas | 58 |
This study compared two single-group approaches for assessing measurement invariance across an observed background variable: restricted factor analysis (RFA) and moderated nonlinear factor analysis (MNLFA). In MNLFA models, heteroskedasticity can be accounted for by allowing the common-factor variance and the residual variances to differ as a function of the background variable. In contrast, RFA models assume homoskedasticity of both the common factor and the residuals. We conducted a simulation study to examine the performance of RFA and MNLFA under common-factor and residual homoskedasticity and heteroskedasticity. Results suggest that MNLFA and RFA with product indicators outperform RFA with latent moderated structural equations in conditions with heteroskedastic common-factors, and MNLFA outperforms RFA in conditions with residual heteroskedasticity. We provide an explanation for the robustness of RFA with product indicators to violations of common-factor homoskedasticity
Algorithm · Econometrics · Economics · Factor analysis · Heteroscedasticity · Homoscedasticity · Residual · Statistics · Chemistry · Computer Science · Functional Brain Connectivity Studies · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference · Accounting
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| Citas por año | 4 |
|---|---|
| Intervalo de citas | 2025 - 2026 (2) |
| Altamente citado | No |