2015/11/02 by Anthony Rodriguez, Steven P. Reise, Mark G. Haviland · 3 citations
Mathematics · Psychology · #Cognitive Abilities and Testing #Computer science #Data mining #Econometrics #Mathematics #Measure (data warehouse) #Mental Health Research Topics #Personality Traits and Psychology #PsycINFO #Psychology #Psychometrics #Reliability (semiconductor) #Statistical model #Statistics #Structural equation modeling #Variance (accounting)
paper · doi:10.1037/met0000045
openalex publication_date 2015/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Bifactor measurement models are increasingly being applied to personality and psychopathology measures (Reise, 2012). In this work, authors generally have emphasized model fit, and their typical conclusion is that a bifactor model provides a superior fit relative to alternative subordinate models. Often unexplored, however, are important statistical indices that can substantially improve the psychometric analysis of a measure. We provide a review of the particularly valuable statistical indices one can derive from bifactor models. They include omega reliability coefficients, factor determinacy, construct reliability, explained common variance, and percentage of uncontaminated correlations. We describe how these indices can be calculated and used to inform: (a) the quality of unit-weighted total and subscale score composites, as well as factor score estimates, and (b) the specification and quality of a measurement model in structural equation modeling. (PsycINFO Database Record