Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzvariēblu modelēšanas pētījumi× | Ceļu analīze× | |
|---|---|---|
| Nozare≠ | Pētījuma dizains | Statistika |
| Saime≠ | Process / pipeline | Latent structure |
| Izcelsmes gads≠ | 1970s–1980s (multivariate model testing as a distinct approach) | 1921 |
| Autors≠ | Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis) | Sewall Wright |
| Tips≠ | Quantitative confirmatory research design | Causal / mediation model |
| Pirmavots≠ | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585. link ↗ |
| Citi nosaukumi | multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research | PA, path coefficient analysis, observed-variable SEM, causal path modeling |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference. |
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