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| 베이지안 척도 개발× | 탐색적 요인 분석 (EFA)× | |
|---|---|---|
| 분야≠ | 심리측정학 | 통계학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1990s–2000s | — |
| 창시자≠ | Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues | — |
| 유형≠ | Bayesian probabilistic scale construction | Latent variable / dimension reduction |
| 원전≠ | De Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press. ISBN: 978-1593858698 | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| 별칭≠ | Bayesian psychometric scale construction, Bayesian measurement modeling, Bayesian item development, BSD | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 관련≠ | 5 | 4 |
| 요약≠ | Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principled decisions about item retention, reliability, and validity in small or complex samples. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
| ScholarGate데이터셋 ↗ |
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