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| Διασταυρούμενη Λειτουργικότητα Στοιχείων Πολλαπλών Ομάδων (MG-DIF)× | Επιβεβαιωτική Παραγοντική Ανάλυση Πολλαπλών Ομάδων (MG-CFA)× | |
|---|---|---|
| Πεδίο | Ψυχομετρία | Ψυχομετρία |
| Οικογένεια | Latent structure | Latent structure |
| Έτος προέλευσης≠ | 1980s-1990s | 1971 |
| Δημιουργός≠ | Shealy & Stout (SIBTEST framework); Lord (IRT-based DIF) | Karl Jöreskog |
| Τύπος≠ | Measurement bias detection | Measurement model / invariance test |
| Θεμελιώδης πηγή≠ | Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge. ISBN: 978-1848728936 | 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 ↗ |
| Εναλλακτικές ονομασίες | MG-DIF, multi-group DIF, differential item functioning across groups, multiple-group DIF analysis | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | Multi-group differential item functioning examines whether test or scale items function equivalently across three or more distinct groups — such as gender, ethnicity, or country — after matching respondents on the underlying trait being measured. Items that behave differently across groups threaten fair measurement and valid score comparisons. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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