手法を比較

	多カテゴリ構成概念妥当性	因子分析（EFA）
分野	心理測定学	統計学
系統	Latent structure	Latent structure
提唱年	1992–2000	—
提唱者	Building on Messick (1989) and IRT extensions by Masters, Muraki, and Samejima	—
種類	Psychometric validity framework	Latent variable / dimension reduction
原典	Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.	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.
別名	polytomous item construct validity, ordered-category construct validity, polytomous measurement validity, multi-category scale validity	common factor analysis, açımlayıcı faktör analizi, factor analysis
関連	6	4
概要	Polytomous construct validity refers to the evaluation of whether a scale composed of ordered, multi-category items (e.g., Likert or rating-scale items) genuinely measures the intended latent construct. It extends classical validity frameworks to polytomous measurement models — such as the Graded Response Model or Generalized Partial Credit Model — ensuring that ordered response categories function as designed and that the resulting scores reflect the target construct.	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.