Vertaile menetelmiä

	Monitasoinen reliabiliteettianalyysi	Vahvistava faktorianalyysi (CFA)	McDonaldin hierarkkinen omega (ωh)
Tieteenala	Psykometriikka	Psykometriikka	Psykometriikka
Menetelmäperhe	Latent structure	Latent structure	Latent structure
Syntyvuosi	2014	1969	1999
Kehittäjä	Geldhof, Preacher & Zyphur	Karl Gustav Jöreskog	Roderick P. McDonald
Tyyppi	Reliability estimation / psychometric modeling	Hypothesis-testing latent variable model	Reliability / composite score validity coefficient
Alkuperäislähde	Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	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.
Rinnakkaisnimet	multilevel omega, within-group reliability, between-group reliability, hierarchical reliability	CFA, confirmatory FA, measurement model, restricted factor analysis	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient
Liittyvät	3	4	5
Tiivistelmä	Multilevel reliability analysis estimates the internal consistency of scale scores separately at the within-group (individual) and between-group (cluster) levels. It corrects the bias that arises when ordinary alpha or omega is applied to hierarchically nested data, such as employees within organizations or students within classrooms.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.