Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Cercetare prin testarea modelelor multivariate× | Analiza factoriala confirmatorie (CFA)× | |
|---|---|---|
| Domeniu≠ | Design de cercetare | Psihometrie |
| Familie≠ | Process / pipeline | Latent structure |
| Anul apariției≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1969 |
| Autorul original≠ | Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis) | Karl Gustav Jöreskog |
| Tip≠ | Quantitative confirmatory research design | Hypothesis-testing latent variable model |
| Sursa seminală≠ | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Denumiri alternative | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | 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. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
| ScholarGateSet de date ↗ |
|
|