ArticleHealth and quality of life outcomes2017
Latent variable mixture models to test for differential item functioning: a population-based analysis.
Article in Health and quality of life outcomes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Tree-based item-response theory model for evaluating differential item functioning in patient-reported outcome measures: a web-based R Shiny implementation.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- Measurement invariance of the Seattle Angina Questionnaire in coronary artery disease.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- Commentary: Matching IRT Models to PRO Constructs- Modeling Alternatives, and Some Thoughts on What Makes a Model Different.Psychometrika · 2021Article
- Demographic and functional differences among social security disability claimants.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021Article
- Differential item functioning of the SF-12 in a population-based regional joint replacement registry.Health and quality of life outcomes · 2019Article
- Differential item functioning and its relevance to epidemiology.Current epidemiology reports · 2019Article
- The use of latent variable mixture models to identify invariant items in test construction.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018Article
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12 authors.
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Abstract
backgroundComparisons 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).
methodsData 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.
resultsThe 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.
conclusionsThis 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.
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