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| Rakenteellinen yhtälömallinnus× | Faktorianalyysi× | |
|---|---|---|
| Tieteenala | Tutkimuksen tilastomenetelmät | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1921 | 1931 |
| Kehittäjä≠ | Sewall Wright | Louis Leon Thurstone |
| Tyyppi | Method | Method |
| Alkuperäislähde≠ | 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 ↗ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ |
| Rinnakkaisnimet≠ | SEM, path analysis, latent variable modeling, causal modeling | EFA, CFA, latent variable modeling |
| Liittyvät | 3 | 3 |
| Tiivistelmä≠ | 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. | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. |
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