Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многомерные панельные исследования× | Структурное моделирование (Structural Equation Modeling)× | |
|---|---|---|
| Область≠ | Дизайн исследования | Статистика исследований |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1960s–1980s (econometrics); broader social-science uptake 1990s–2000s | 1921 |
| Автор метода≠ | Econometric tradition; formalized by Cheng Hsiao and Badi Baltagi | Sewall Wright |
| Тип≠ | Quantitative panel research design | Method |
| Основополагающий источник≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | 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 ↗ |
| Другие названия | multivariate panel data analysis, panel data multivariate modeling, multi-outcome panel study, longitudinal multivariate panel design | SEM, path analysis, latent variable modeling, causal modeling |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Multivariate panel research combines the repeated-measurement structure of panel data — the same subjects observed at multiple time points — with the simultaneous analysis of two or more outcome or predictor variables. By modeling joint trajectories across units and time, it controls for unobserved individual heterogeneity while capturing the interplay among variables, making it one of the most powerful non-experimental designs available for causal and predictive inference in the social, behavioral, and economic sciences. | 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. |
| ScholarGateНабор данных ↗ |
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