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| Invarianza di misurazione politoma× | Analisi Fattoriale Confermativa Politoma× | |
|---|---|---|
| Campo | Psicometria | Psicometria |
| Famiglia | Latent structure | Latent structure |
| Anno di origine≠ | 2000–2004 | 1984 |
| Ideatore≠ | Roger E. Millsap, Robert J. Vandenberg | Bengt Muthen |
| Tipo≠ | Multi-group confirmatory test | Latent variable / confirmatory measurement model |
| Fonte seminale≠ | 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 ↗ | Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. DOI ↗ |
| Alias | PMI, ordinal measurement invariance, polytomous factorial invariance, polytomous multi-group measurement invariance | CFA for ordered categories, ordinal CFA, categorical CFA, WLSMV-CFA |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | Polytomous confirmatory factor analysis (CFA) tests a pre-specified factor structure when items have three or more ordered response categories (e.g., Likert scales). By working with polychoric correlations and robust estimators such as WLSMV, it avoids the distortions that arise when ordered categorical data are treated as continuous. |
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