Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Multivariat modelltesting Forskning× | Strukturmodellering× | |
|---|---|---|
| Fagfelt≠ | Forskningsdesign | Forskningsstatistikk |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1921 |
| Opphavsperson≠ | Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis) | Sewall Wright |
| Type≠ | Quantitative confirmatory research design | Method |
| Opprinnelig kilde≠ | 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 ↗ |
| Alias | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | SEM, path analysis, latent variable modeling, causal modeling |
| Relaterte≠ | 5 | 3 |
| Sammendrag≠ | Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis. | 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. |
| ScholarGateDatasett ↗ |
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