방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다집단 수렴 타당도× | 다집단 확인적 요인분석 (MG-CFA)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1981 / 2000 | 1971 |
| 창시자≠ | Fornell & Larcker (convergent validity criteria); Vandenberg & Lance (multi-group extension) | Karl Jöreskog |
| 유형≠ | Validity assessment procedure | Measurement model / invariance test |
| 원전≠ | Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. DOI ↗ | 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 ↗ |
| 별칭 | cross-group convergent validity, multi-sample convergent validity, MGCFA convergent validity, AVE across groups | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA |
| 관련 | 6 | 6 |
| 요약≠ | Multi-group convergent validity examines whether items purported to measure the same latent construct relate strongly to that construct consistently across distinct subgroups such as demographic categories, cultures, or experimental conditions. It extends single-sample convergent validity checks into a comparative multi-group confirmatory factor analysis framework. | 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. |
| ScholarGate데이터셋 ↗ |
|
|