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）× | 多层模型× | |
|---|---|---|
| 领域≠ | 心理测量学 | 研究统计学 |
| 方法族≠ | Latent structure | Process / pipeline |
| 起源年份≠ | 1969 | 1992 |
| 提出者≠ | Karl Gustav Jöreskog | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Hypothesis-testing latent variable model | Method |
| 开创性文献≠ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | CFA, confirmatory FA, measurement model, restricted factor analysis | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 4 | 3 |
| 摘要≠ | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
| ScholarGate数据集 ↗ |
|
|