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。
| 多层验证性因子分析 (MCFA)× | 潜剖面分析 (Latent Profile Analysis, LPA)× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1994 | 2010 |
| 提出者≠ | Bengt O. Muthen | Lazarsfeld & Henry; Collins & Lanza |
| 类型≠ | Latent variable model / measurement model | Person-centered finite mixture model |
| 开创性文献≠ | Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7 |
| 别名 | MCFA, multilevel measurement model, two-level CFA, hierarchical CFA | Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi |
| 相关≠ | 6 | 2 |
| 摘要≠ | Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations. | Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile. |
| ScholarGate数据集 ↗ |
|
|