方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 验证性因子分析 (CFA)× | 项目功能差异 (DIF) 分析× | 结构方程模型 (SEM)× | |
|---|---|---|---|
| 领域≠ | 统计学 | 心理测量学 | 统计学 |
| 方法族 | Latent structure | Latent structure | Latent structure |
| 起源年份≠ | 1969 | 1988 | 1970 |
| 提出者≠ | Karl Jöreskog | Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988) | Karl Jöreskog (LISREL framework, 1970s) |
| 类型≠ | Confirmatory latent variable model | Item-level fairness / measurement equivalence analysis | Latent variable / causal modeling |
| 开创性文献≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Holland, P. W. & Thayer, D. T. (1988). Differential Item Performance and the Mantel-Haenszel Procedure. ETS Research Report Series. link ↗ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| 别名≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | Madde Yanlılık Analizi (DIF — Differential Item Functioning), item bias analysis, Mantel-Haenszel DIF, Lord chi-square DIF | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| 相关≠ | 4 | 4 | 5 |
| 摘要≠ | Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships. | Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal tool in modern test development for detecting and removing item bias. | Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences. |
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