Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelēšana ar strukturālām vienādojumiem× | Regresijas lineārā analīze× | |
|---|---|---|
| Nozare | Pētniecības statistika | Pētniecības statistika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1921 | 1801 |
| Autors≠ | Sewall Wright | Carl Friedrich Gauss |
| Tips | Method | Method |
| Pirmavots≠ | 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 ↗ | Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons. link ↗ |
| Citi nosaukumi≠ | SEM, path analysis, latent variable modeling, causal modeling | MLR, multivariate regression, linear regression |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research. |
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