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| ベイズ判別妥当性評価× | 確認的因子分析（CFA）× | |
|---|---|---|
| 分野 | 心理測定学 | 心理測定学 |
| 系統 | Latent structure | Latent structure |
| 提唱年≠ | 2020 (Bayesian HTMT formalization); 1959 (discriminant validity concept) | 1969 |
| 提唱者≠ | Adaptation of Campbell & Fiske (1959) discriminant validity into Bayesian CFA framework; Bayesian HTMT formalization by Garnier-Villarreal & Jorgensen (2020) | Karl Gustav Jöreskog |
| 種類≠ | Validity assessment | Hypothesis-testing latent variable model |
| 原典≠ | Garnier-Villarreal, M. & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 別名 | Bayesian HTMT, Bayesian HTMTb, Bayesian discriminant evidence, Bayesian CFA discriminant validity | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 関連≠ | 6 | 4 |
| 概要≠ | Bayesian discriminant validity assessment evaluates whether two theoretically distinct latent constructs are empirically separable, using posterior distributions and credible intervals rather than single-point null-hypothesis tests. It is applied within Bayesian confirmatory factor analysis or via the Bayesian heterotrait-monotrait ratio (HTMTb) to determine whether constructs measuring different traits are sufficiently differentiated. | 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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