手法を比較

	ポリトマス探索的因子分析	項目応答理論 (IRT)
分野	心理測定学	心理測定学
系統	Latent structure	Latent structure
提唱年	1978	1952–1968
提唱者	Bengt Muthén	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
種類	Latent variable / dimension reduction	Probabilistic measurement model
原典	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.	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
別名	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
関連	4	5
概要	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.