Comparar métodos

	Análise de Confiabilidade Multinível	Alfa de Cronbach (Análise de Confiabilidade)	Ômega Hierárquico de McDonald (ωh)
Área	Psicometria	Estatística	Psicometria
Família	Latent structure	Latent structure	Latent structure
Ano de origem	2014	1951	1999
Autor original	Geldhof, Preacher & Zyphur	Lee J. Cronbach	Roderick P. McDonald
Tipo	Reliability estimation / psychometric modeling	Reliability / internal consistency coefficient	Reliability / composite score validity coefficient
Fonte seminal	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.	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	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.
Outros nomes	multilevel omega, within-group reliability, between-group reliability, hierarchical reliability	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	omega hierarchical, omega-h, bifactor omega, composite score validity coefficient
Relacionados	3	4	5
Resumo	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.	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.	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.