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01-05-2012 | Uitgave 4/2012

Quality of Life Research 4/2012

Latent variable mixture models: a promising approach for the validation of patient reported outcomes

Quality of Life Research > Uitgave 4/2012
Richard Sawatzky, Pamela A. Ratner, Jacek A. Kopec, Bruno D. Zumbo
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Electronic supplementary material

The online version of this article (doi:10.​1007/​s11136-011-9976-6) contains supplementary material, which is available to authorized users.



A fundamental assumption of patient-reported outcomes (PRO) measurement is that all individuals interpret questions about their health status in a consistent manner, such that a measurement model can be constructed that is equivalently applicable to all people in the target population. The related assumption of sample homogeneity has been assessed in various ways, including the many approaches to differential item functioning analysis.


This expository paper describes the use of latent variable mixture modeling (LVMM), in conjunction with item response theory (IRT), to examine: (a) whether a sample is homogeneous with respect to a unidimensional measurement model, (b) implications of sample heterogeneity with respect to model-predicted scores (theta), and (c) sources of sample heterogeneity. An example is provided using the 10 items of the Short-Form Health Status (SF-36®) physical functioning subscale with data from the Canadian Community Health Survey (2003) (N = 7,030 adults in Manitoba).


The sample was not homogeneous with respect to a unidimensional measurement structure. Specification of three latent classes, to account for sample heterogeneity, resulted in significantly improved model fit. The latent classes were partially explained by demographic and health-related variables.


The illustrative analyses demonstrate the value of LVMM in revealing the potential implications of sample heterogeneity in the measurement of PROs.

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