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| Invariance de mesure polytomique× | Analyse factorielle confirmatoire multi-groupes (AFC-MG)× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2000–2004 | 1971 |
| Auteur d'origine≠ | Roger E. Millsap, Robert J. Vandenberg | Karl Jöreskog |
| Type≠ | Multi-group confirmatory test | Measurement model / invariance test |
| Source fondatrice≠ | Millsap, R. E. & Kwok, O.-M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection utility. Psychological Methods, 9(2), 200–215. link ↗ | Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. DOI ↗ |
| Alias | PMI, ordinal measurement invariance, polytomous factorial invariance, polytomous multi-group measurement invariance | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA |
| Apparentées≠ | 5 | 6 |
| Résumé≠ | Polytomous measurement invariance testing evaluates whether a scale with ordered categorical (polytomous) response options — such as Likert-type items — measures the same latent construct in the same way across two or more groups. It extends classical multi-group CFA invariance testing to properly account for the ordinal nature of item responses, ensuring that group comparisons of latent means or factor structures are substantively valid. | Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified. |
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