Quality of Life Research

, Volume 22, Issue 9, pp 2595–2601

Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)

Commentary

DOI: 10.1007/s11136-013-0465-y

Cite this article as:
McIntosh, C.N. Qual Life Res (2013) 22: 2595. doi:10.1007/s11136-013-0465-y

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.

Keywords

Factorial invarianceFatiguePatient-reported outcome measuresQuality of lifeBayesian analysis

List of Abbreviations

AFI

Approximate fit index

CFA

Confirmatory factor analysis

HRQoL

Health-related quality of life

MCMC

Markov chain Monte Carlo

SEM

Structural equation modeling

PedsQL™ MFS

Pediatric Quality of Life Inventory™ Multidimensional Fatigue Scale

PPP

Posterior predictive p value

PROM

Patient-reported outcome measure

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.National Crime Prevention CentrePublic Safety CanadaOttawaCanada