Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Eksploratiivinen faktorianalyysi (EFA)× | Cronbachin alfa (Reliability Analysis)× | Rakenteellinen yhtälömallinnus (SEM)× | |
|---|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Latent structure | Latent structure | Latent structure |
| Syntyvuosi≠ | — | 1951 | 1970 |
| Kehittäjä≠ | — | Lee J. Cronbach | Karl Jöreskog (LISREL framework, 1970s) |
| Tyyppi≠ | Latent variable / dimension reduction | Reliability / internal consistency coefficient | Latent variable / causal modeling |
| Alkuperäislähde≠ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. DOI ↗ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| Rinnakkaisnimet≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| Liittyvät≠ | 4 | 4 | 5 |
| Tiivistelmä≠ | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. | Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research. | 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. |
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