Latent structureScale / measurement
多项探索性因子分析
多项探索性因子分析（Polytomous exploratory factor analysis）通过用多列相关矩阵（polychoric correlation matrix）替换皮尔逊相关矩阵（Pearson correlation matrix），将标准EFA扩展到有序分类（如李克特量表式）的反应数据。它恢复了每个多项条目所假设反映的潜在连续变量，从而比将有序分数视为连续变量能获得更准确的因子载荷和更清晰的因子结构。
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来源
- 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. DOI: 10.1037/1082-989X.9.4.466 ↗
- Muthén, B. (1978). Contributions to factor analysis of dichotomous variables. Psychometrika, 43(4), 551–560. DOI: 10.1007/BF02293813 ↗
如何引用本页
ScholarGate. (2026, June 3). Polytomous Exploratory Factor Analysis. ScholarGate. https://scholargate.app/zh/psychometrics/polytomous-exploratory-factor-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 验证性因子分析（CFA）心理测量学↔ compare
- 探索性因子分析（EFA）统计学↔ compare
- 分级反应模型 (GRM)心理测量学↔ compare
- 项目反应理论 (IRT)心理测量学↔ compare