विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| क्रमिक मापन निश्चरता परीक्षण× | बहु-समूह पुष्टिकारी कारक विश्लेषण (MG-CFA)× | |
|---|---|---|
| क्षेत्र | मनोमिति | मनोमिति |
| परिवार | Latent structure | Latent structure |
| उद्भव वर्ष≠ | 1984–2011 | 1971 |
| प्रवर्तक≠ | Roger Millsap; Bengt Muthén | Karl Jöreskog |
| प्रकार≠ | Multi-group model comparison | Measurement model / invariance test |
| मौलिक स्रोत≠ | Millsap, R. E. (2011). 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 ↗ |
| उपनाम | ordinal MI, measurement invariance for ordinal data, ordinal CFA invariance, categorical measurement invariance | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA |
| संबंधित | 6 | 6 |
| सारांश≠ | Ordinal measurement invariance testing evaluates whether a multi-group confirmatory factor model holds equivalent measurement properties across groups when scale items are ordinal — such as Likert-type response scales. It uses polychoric correlations and categorical estimators (WLSMV/DWLS) rather than Pearson-based methods, correcting the systematic bias that arises when ordinal data are treated as continuous. | 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. |
| ScholarGateडेटासेट ↗ |
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