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

Xiuyun Wu1,2, Richard Sawatzky3, Wilma Hopman4

  • 1School of Public Health, University of Alberta, Edmonton, AB, Canada.

Health and Quality of Life Outcomes
|May 17, 2017
PubMed
Summary
This summary is machine-generated.

Differential item functioning (DIF) was found in the SF-36 physical functioning and mental health scales. This suggests SF-36 scores may not be comparable across different demographic and health groups in population health studies.

Keywords:
Item response theoryLatent class analysisMental healthPatient-reported outcome measuresPhysical functioningPopulation health

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Area of Science:

  • Health Services Research
  • Psychometrics
  • Biostatistics

Background:

  • Population health comparisons using self-report measures like the SF-36 assume common item interpretation across groups.
  • Differential item functioning (DIF) can occur when individuals with the same health status have different response probabilities, compromising validity.
  • This study investigated DIF in the SF-36 physical functioning (PF) and mental health (MH) sub-scales using population-based data.

Purpose of the Study:

  • To test for differential item functioning (DIF) in the SF-36 physical functioning (PF) and mental health (MH) sub-scales.
  • To assess the impact of DIF on the comparability of SF-36 scores across demographic and health subgroups.
  • To evaluate the utility of latent variable mixture models (LVMMs) for detecting DIF in large-scale population health surveys.

Main Methods:

  • Latent variable mixture models (LVMMs) were applied to SF-36 PF (10 items) and MH (5 items) data from the Canadian Multicentre Osteoporosis Study (CaMos).
  • A standard two-parameter graded response model (one latent class) was compared against multi-class LVMMs to identify DIF.
  • Demographic and health variables were used to characterize latent classes identified by the LVMMs via multivariable logistic regression.

Main Results:

  • A three-class LVMM fit the PF sub-scale (class proportions: 0.59, 0.24, 0.17), and a two-class model fit the MH sub-scale (0.69, 0.31).
  • Respondents in higher classes reported consistently higher limitations for PF and more health problems for MH compared to the reference class.
  • Significant differences in item thresholds and factor loadings were observed between one-class and multi-class models, indicating DIF. Demographic and health variables were associated with class membership.

Conclusions:

  • Differential item functioning (DIF) was detected in population-based SF-36 PF and MH data.
  • SF-36 PF and MH sub-scale scores may not be directly comparable across subgroups defined by demographic and health status.
  • Routine evaluation of DIF is recommended for population-based self-report data analysis to ensure valid subgroup comparisons.