Difference in method of administration did not significantly impact item response: an IRT-based analysis from the Patient-Reported Outcomes Measurement Information System (PROMIS) initiative
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- Bjorner, J.B., Rose, M., Gandek, B. et al. Qual Life Res (2014) 23: 217. doi:10.1007/s11136-013-0451-4
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To test the impact of method of administration (MOA) on the measurement characteristics of items developed in the Patient-Reported Outcomes Measurement Information System (PROMIS).
Two non-overlapping parallel 8-item forms from each of three PROMIS domains (physical function, fatigue, and depression) were completed by 923 adults (age 18–89) with chronic obstructive pulmonary disease, depression, or rheumatoid arthritis. In a randomized cross-over design, subjects answered one form by interactive voice response (IVR) technology, paper questionnaire (PQ), personal digital assistant (PDA), or personal computer (PC) on the Internet, and a second form by PC, in the same administration. Structural invariance, equivalence of item responses, and measurement precision were evaluated using confirmatory factor analysis and item response theory methods.
Multigroup confirmatory factor analysis supported equivalence of factor structure across MOA. Analyses by item response theory found no differences in item location parameters and strongly supported the equivalence of scores across MOA.
We found no statistically or clinically significant differences in score levels in IVR, PQ, or PDA administration as compared to PC. Availability of large item response theory-calibrated PROMIS item banks allowed for innovations in study design and analysis.
KeywordsPatient-reported outcomes Quality of life Questionnaire Mode of administration Method of administration Item response theory
Computerized adaptive testing
Chronic obstructive pulmonary disease
Item response theory
Interactive voice response
Method of administration
Personal digital assistant
SAS procedure for estimating mixed models
Patient-Reported Outcomes Measurement Information System
Weighted least squares with mean and variance adjustment