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| ロバストな判別妥当性× | 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≠ | 1959 (foundational); 2015 (HTMT criterion) | 1969 |
| 提唱者≠ | Henseler, Ringle & Sarstedt (HTMT); Campbell & Fiske (foundational framework) | Karl Gustav Jöreskog |
| 種類≠ | Validity assessment / measurement quality criterion | Hypothesis-testing latent variable model |
| 原典≠ | Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 別名 | HTMT criterion, heterotrait-monotrait ratio, discriminant validity testing, RDV | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 関連 | 4 | 4 |
| 概要≠ | Robust discriminant validity assessment determines whether distinct latent constructs in a measurement model are sufficiently different from one another. Unlike traditional AVE-based approaches, robust methods such as the Heterotrait-Monotrait (HTMT) ratio use the pattern of inter-indicator correlations to provide a more sensitive and simulation-validated criterion for judging discriminant validity in structural equation modeling contexts. | 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. |
| ScholarGateデータセット ↗ |
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