方法对比
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| 多项式项目分析× | 探索性因子分析（EFA）× | |
|---|---|---|
| 领域≠ | 心理测量学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1969–1982 | — |
| 提出者≠ | Fumiko Samejima (graded response model, 1969); David Andrich (rating scale model, 1978); Geoffrey Masters (partial credit model, 1982) | — |
| 类型≠ | Item-level psychometric analysis | Latent variable / dimension reduction |
| 开创性文献≠ | Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1–97. DOI ↗ | 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 ↗ |
| 别名≠ | ordered-category item analysis, graded response analysis, polytomous IRT, rated-scale item analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关 | 4 | 4 |
| 摘要≠ | Polytomous item analysis examines the psychometric behavior of items that have more than two ordered response categories — such as Likert-type scales or partial-credit tasks. It evaluates each item's difficulty thresholds, discriminating power, and category functioning to determine whether the full response scale is being used as intended and whether each item contributes reliably to measuring the underlying 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. |
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