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| 다수준 판별 타당도× | 다층 측정 불변성× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 2005 | 2000s |
| 창시자≠ | Dyer, Hanges, & Hall; Chen, Sousa, & West | Muthén, Asparouhov, and colleagues |
| 유형≠ | Validity evaluation within multilevel CFA | Measurement model evaluation |
| 원전≠ | Dyer, N. G., Hanges, P. J., & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. Leadership Quarterly, 16(1), 149–167. DOI ↗ | Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270. link ↗ |
| 별칭 | multilevel DV, cross-level discriminant validity, hierarchical discriminant validity, ML-DV | MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance |
| 관련≠ | 5 | 3 |
| 요약≠ | Multilevel discriminant validity evaluates whether theoretically distinct constructs are empirically separable when data are nested within higher-level units such as teams, schools, or organizations. It extends single-level discriminant validity checks into a multilevel confirmatory factor analysis framework, verifying that constructs are distinguishable both within and between levels simultaneously. | Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research. |
| ScholarGate데이터셋 ↗ |
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