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Examining Multiple Sources of Differential Item Functioning on the C linician & G roup CAHPS ® Survey
Author(s) -
Rodriguez Hector P.,
Crane Paul K.
Publication year - 2011
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2011.01299.x
Subject(s) - differential item functioning , item response theory , ordered logit , logistic regression , medicine , patient reported outcome , scale (ratio) , patient satisfaction , psychometrics , measurement invariance , clinical psychology , quality of life (healthcare) , statistics , nursing , mathematics , structural equation modeling , physics , quantum mechanics , confirmatory factor analysis
Objective To evaluate psychometric properties of a widely used patient experience survey. Data Sources English‐language responses to the Clinician & Group Consumer Assessment of Healthcare Providers and Systems ( CG‐CAHPS ® ) survey ( n = 12,244) from a 2008 quality improvement initiative involving eight southern California medical groups. Methods We used an iterative hybrid ordinal logistic regression/item response theory differential item functioning ( DIF ) algorithm to identify items with DIF related to patient sociodemographic characteristics, duration of the physician–patient relationship, number of physician visits, and self‐rated physical and mental health. We accounted for all sources of DIF and determined its cumulative impact. Principal Findings The upper end of the CG‐CAHPS ® performance range is measured with low precision. With sensitive settings, some items were found to have DIF . However, overall DIF impact was negligible, as 0.14 percent of participants had salient DIF impact. Latinos who spoke predominantly English at home had the highest prevalence of salient DIF impact at 0.26 percent. Conclusions The CG‐CAHPS ® functions similarly across commercially insured respondents from diverse backgrounds. Consequently, previously documented racial and ethnic group differences likely reflect true differences rather than measurement bias. The impact of low precision at the upper end of the scale should be clarified.