Abstract
PurposeComparison of patient-reported outcomes may be invalidated by the occurrence of item bias, also known as differential item functioning. We show two ways of using structural equation modeling (SEM) to detect item bias: (1) multigroup SEM, which enables the detection of both uniform and nonuniform bias, and (2) multidimensional SEM, which enables the investigation of item bias with respect to several variables simultaneously.MethodGender- and age-related bias in the items of the Hospital Anxiety and Depression Scale (HADS; Zigmond and Snaith in Acta Psychiatr Scand 67:361–370, 1983) from a sample of 1068 patients was investigated using the multigroup SEM approach and the multidimensional SEM approach. Results were compared to the results of the ordinal logistic regression, item response theory, and contingency tables methods reported by Cameron et al. (Qual Life Res 23:2883–2888, 2014).ResultsBoth SEM approaches identified two items with gender-related bias and two items with age-related bias in the Anxiety subscale, and four items with age-related bias in the Depression subscale. Results from the SEM approaches generally agreed with the results of Cameron et al., although the SEM approaches identified more items as biased.ConclusionSEM provides a flexible tool for the investigation of item bias in health-related questionnaires. Multidimensional SEM has practical and statistical advantages over multigroup SEM, and over other item bias detection methods, as it enables item bias detection with respect to multiple variables, of various measurement levels, and with more statistical power, ultimately providing more valid comparisons of patients’ well-being in both research and clinical practice.
Highlights
Assessment of patient-reported outcomes (PROs) is becoming standard practice in health care and medicine [23]
Results were compared to the results of the ordinal logistic regression, item response theory, and contingency tables methods reported by Cameron et al (Qual Life Res 23:2883–2888, 2014)
We used a multigroup structural equation modeling (SEM) approach to investigate both uniform and nonuniform item bias in each subscale of the Hospital Anxiety and Depression Scale (HADS) separately, and a multidimensional SEM approach that enabled the investigation of uniform item bias in both subscales of the HADS and with regard to both gender and age simultaneously
Summary
Assessment of patient-reported outcomes (PROs) is becoming standard practice in health care and medicine [23]. As such, comparing assessments of PROs is becoming increasingly important in both clinical practice and research. Such comparisons may be invalidated by the occurrence of differential item functioning (DIF). In the presence of item bias, differences between two people on observed item scores may not reflect ‘‘true’’ differences on the trait variable (e.g., men and women may score differently on an item that measures well-being, even though their well-being does not differ). When the bias is nonuniform, it differs for different levels of the latent trait (e.g., the difference may be larger for higher levels of wellbeing)
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