What You Don't Know Can Hurt You: Missing Data and Partial Credit Model Estimates.

Abstract

The authors investigated the effect of missing completely at random (MCAR) item responses on partial credit model (PCM) parameter estimates in a longitudinal study of Positive Affect. Participants were 307 adults from the older cohort of the Notre Dame Study of Health and Well-Being (Bergeman and Deboeck, 2014) who completed questionnaires including Positive Affect items for 56 days. Additional missing responses were introduced to the data, randomly replacing 20%, 50%, and 70% of the responses on each item and each day with missing values, in addition to the existing missing data. Results indicated that item locations and person trait level measures diverged from the original estimates as the level of degradation from induced missing data increased. In addition, standard errors of these estimates increased with the level of degradation. Thus, MCAR data does damage the quality and precision of PCM estimates.