Latent variable mixture models to test for differential item functioning: a population-based analysis

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dc.contributor.authorWu, Xiuyun
dc.contributor.authorSawatzky, Richard
dc.contributor.authorHopman, Wilma
dc.contributor.authorMayo, Nancy
dc.contributor.authorSajobi, Tolulope T
dc.contributor.authorLiu, Juxin
dc.contributor.authorPrior, Jerilynn
dc.contributor.authorPapaioannou, Alexandra
dc.contributor.authorJosse, Robert G
dc.contributor.authorTowheed, Tanveer
dc.contributor.authorDavison, K. S
dc.contributor.authorLix, Lisa M
dc.date.accessioned2018-09-26T12:06:00Z
dc.date.available2018-09-26T12:06:00Z
dc.date.issued2017-05-15
dc.date.updated2018-09-26T12:06:00Z
dc.description.abstractAbstract Background Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs). Methods Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables. Results The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership. Conclusions This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.
dc.identifier.citationHealth and Quality of Life Outcomes. 2017 May 15;15(1):102
dc.identifier.doihttps://doi.org/10.1186/s12955-017-0674-0
dc.identifier.urihttp://hdl.handle.net/1880/107964
dc.identifier.urihttps://doi.org/10.11575/PRISM/44094
dc.language.rfc3066en
dc.rights.holderThe Author(s).
dc.titleLatent variable mixture models to test for differential item functioning: a population-based analysis
dc.typeJournal Article
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