Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Cercetare explicativă multivariată× | Modelarea ecuațiilor structurale× | |
|---|---|---|
| Domeniu≠ | Design de cercetare | Statistică pentru cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | Mid-to-late 20th century (consolidated ~1960s–1980s) | 1921 |
| Autorul original≠ | Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others | Sewall Wright |
| Tip≠ | Quantitative research design | Method |
| Sursa seminală≠ | Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | 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 ↗ |
| Denumiri alternative | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER | SEM, path analysis, latent variable modeling, causal modeling |
| Înrudite≠ | 4 | 3 |
| Rezumat≠ | Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data. | 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. |
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