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| 다항 탐색적 요인 분석× | 문항 반응 이론 (IRT)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1978 | 1952–1968 |
| 창시자≠ | Bengt Muthén | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| 유형≠ | Latent variable / dimension reduction | Probabilistic measurement model |
| 원전≠ | Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| 별칭 | EFA for ordered-categorical data, polychoric EFA, ordinal exploratory factor analysis, polytomous factor analysis | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| 관련≠ | 4 | 5 |
| 요약≠ | Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadings and better-defined factor structures than treating ordinal scores as if they were continuous. | Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons. |
| ScholarGate데이터셋 ↗ |
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