Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Strukturālā vienādojumu modelēšana (SEM)× | Ceļu analīze× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1970 | 1921 |
| Autors≠ | Karl Jöreskog (LISREL framework, 1970s) | Sewall Wright |
| Tips≠ | Latent variable / causal modeling | Causal / mediation model |
| Pirmavots≠ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585. link ↗ |
| Citi nosaukumi | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling | PA, path coefficient analysis, observed-variable SEM, causal path modeling |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences. | 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. |
| ScholarGateDatu kopa ↗ |
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