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| Multivariate Correlational Research× | Strukturális egyenlet modellezés× | |
|---|---|---|
| Tudományterület≠ | Kutatástervezés | Kutatási statisztika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s | 1921 |
| Megalkotó≠ | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others | Sewall Wright |
| Típus≠ | Non-experimental quantitative research design | Method |
| Alapmű≠ | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | 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 ↗ |
| Alternatív nevek | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research | SEM, path analysis, latent variable modeling, causal modeling |
| Kapcsolódó≠ | 2 | 3 |
| Összefoglaló≠ | Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables. | 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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