ScholarGate
アシスタント

手法を比較

選択した手法を並べて確認できます。異なる行はハイライト表示されます。

項目応答理論 (IRT)×因子分析(EFA)×
分野心理測定学統計学
系統Latent structureLatent structure
提唱年1952–1968
提唱者Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
種類Probabilistic measurement modelLatent variable / dimension reduction
原典Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗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 ↗
別名IRT, latent trait theory, item characteristic curve theory, modern test theorycommon factor analysis, açımlayıcı faktör analizi, factor analysis
関連54
概要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.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.
ScholarGateデータセット
  1. v1
  2. 2 出典
  3. PUBLISHED
  1. v2
  2. 2 出典
  3. PUBLISHED

検索へ スライドをダウンロード

ScholarGate手法を比較: Item Response Theory · EFA. 2026-06-17に以下より取得 https://scholargate.app/ja/compare