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| 다변량 모형 검증 연구× | 확인적 요인 분석 (CFA)× | |
|---|---|---|
| 분야≠ | 연구설계 | 심리측정학 |
| 계열≠ | Process / pipeline | Latent structure |
| 기원 연도≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1969 |
| 창시자≠ | Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis) | Karl Gustav Jöreskog |
| 유형≠ | Quantitative confirmatory research design | Hypothesis-testing latent variable model |
| 원전≠ | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 별칭 | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 관련≠ | 5 | 4 |
| 요약≠ | Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
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