Vertaile menetelmiä

	McDonaldin hierarkkinen omega (ωh)	Rakenteellinen yhtälömallinnus (SEM)
Tieteenala	Psykometriikka	Tilastotiede
Menetelmäperhe	Latent structure	Latent structure
Syntyvuosi	1999	1970
Kehittäjä	Roderick P. McDonald	Karl Jöreskog (LISREL framework, 1970s)
Tyyppi	Reliability / composite score validity coefficient	Latent variable / causal modeling
Alkuperäislähde	Reise, S. P., Scheines, R., Widaman, K. F. & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Rinnakkaisnimet	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Liittyvät	5	5
Tiivistelmä	McDonald's hierarchical omega (ωh) is a coefficient derived from a bifactor confirmatory factor model that quantifies what proportion of total-score variance is attributable to a single general factor rather than to group-specific factors or item-level error. Introduced by Roderick P. McDonald (1999) and elaborated for bifactor applications by Reise and colleagues (2013) and Rodriguez and colleagues (2016), it is the primary index used in psychometrics to evaluate whether a composite total score is a defensible summary of a multidimensional scale.	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.