Using structural equation modelling to detect meas

Authors: B L KingKallimanis F J Oort G J A Garst

Publish Date: 2010/05/26

Volume: 94, Issue: 2, Pages: 139-156

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Abstract

We propose a three step procedure to investigate measurement bias and response shift a special case of measurement bias in longitudinal data Structural equation modelling is used in each of the three steps which can be described as 1 establishing a measurement model using confirmatory factor analysis 2 detecting measurement bias by testing the equivalence of model parameters across measurement occasions 3 detecting measurement bias with respect to additional exogenous variables by testing their direct effects on the indicator variables The resulting model can be used to investigate true change in the attributes of interest by testing changes in common factor means Solutions for the issue of constraint interaction and for chance capitalisation in model specification searches are discussed as part of the procedure The procedure is illustrated by applying it to longitudinal healthrelated qualityoflife data of HIV/AIDS patients collected at four semiannual measurement occasionsThis article is published under an open access license Please check the Copyright Information section for details of this license and what reuse is permitted If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and reuse information please contact the Rights and Permissions team

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