Cameron N McIntosh1. 1. National Crime Prevention Centre, Public Safety Canada, 269 Laurier Avenue West, Ottawa, ON, K1A 0P8, Canada, cameron.mcintosh@ps-sp.gc.ca.
Abstract
OBJECTIVES: This article provides a commentary in response to "Varni et al. (Qual Life Res. doi: 10.1007/s11136-013-0370-4 , 2013)." METHODS AND RESULTS: The commentary argues that the approximate model fit indexes commonly used in maximum-likelihood confirmatory factor analysis and factorial invariance testing are seriously flawed, as they overlook potentially serious model misspecifications that could bias parameter estimates and compromise inference. CONCLUSIONS: Flexible and convenient Bayesian estimation approaches are presented that can substantially aid in: (1) resolving commonly encountered specification errors in confirmatory factor models and (2) locating specific measurement parameters that are non-invariant across population subgroups. It is recommended that these methods should be more widely adopted for evaluating the factorial invariance of patient-reported outcome measures and other types of instruments.
OBJECTIVES: This article provides a commentary in response to "Varni et al. (Qual Life Res. doi: 10.1007/s11136-013-0370-4 , 2013)." METHODS AND RESULTS: The commentary argues that the approximate model fit indexes commonly used in maximum-likelihood confirmatory factor analysis and factorial invariance testing are seriously flawed, as they overlook potentially serious model misspecifications that could bias parameter estimates and compromise inference. CONCLUSIONS: Flexible and convenient Bayesian estimation approaches are presented that can substantially aid in: (1) resolving commonly encountered specification errors in confirmatory factor models and (2) locating specific measurement parameters that are non-invariant across population subgroups. It is recommended that these methods should be more widely adopted for evaluating the factorial invariance of patient-reported outcome measures and other types of instruments.
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