方法对比
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| 多分类构念效度× | 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≠ | 1992–2000 | — |
| 提出者≠ | Building on Messick (1989) and IRT extensions by Masters, Muraki, and Samejima | — |
| 类型≠ | Psychometric validity framework | Latent variable / dimension reduction |
| 开创性文献≠ | Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176. 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≠ | polytomous item construct validity, ordered-category construct validity, polytomous measurement validity, multi-category scale validity | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. |
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