A Monte Carlo Confidence Interval Method for Testing Measurement Invariance
Datos Bibliográficos
| ID | 21641902 |
|---|---|
| Autores | Hui Li (0000-0001-9355-1116, Beijing Normal University), Hongyun Liu (0000-0002-3472-9102, Beijing Normal University, autor de correspondencia) |
| Año | 2022 |
| Volumen | 29 |
| Número | 4 |
| Páginas | 600-610 |
| Fecha de publicación | 2022-07-04 |
| Open Access | No |
| 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 | 10.1080/10705511.2022.2034114 |
| OpenAlex | W4220682926 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 39 |
We propose a new method, the Monte Carlo confidence interval (MCCI) method, for studying measurement invariance. This method allows researchers to examine the invariance of all items simultaneously by comparing the observed between-group differences in parameters with those obtained under the null hypothesis of invariance. We compare the performance of our method to two other methods: the multigroup confirmatory factor analysis (MG-CFA) and the forward method using confidence intervals (forward CI). The results show that our method performed better than the MG-CFA method and comparably with the forward CI method.
Algorithm · Combinatorics · Confidence interval · Confirmatory factor analysis · Data mining · Measurement invariance · Monte Carlo method · Null hypothesis · Statistics · Structural equation modeling · Advanced Statistical Methods and Models · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Psychometric Methodologies and Testing
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| Citas por año | 1 |
|---|---|
| Intervalo de citas | 2026 - 2026 (1) |
| Altamente citado | No |