So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Mô hình phương trình cấu trúc× | Mô hình đa cấp× | |
|---|---|---|
| Lĩnh vực | Thống kê nghiên cứu | Thống kê nghiên cứu |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1921 | 1992 |
| Người khởi xướng≠ | Sewall Wright | Anthony Bryk and Stephen Raudenbush |
| Loại | Method | Method |
| Công trình gốc≠ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Tên gọi khác | SEM, path analysis, latent variable modeling, causal modeling | HLM, mixed-effects models, random effects models, MLM |
| Liên quan | 3 | 3 |
| Tóm tắt≠ | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|