The effect of missing data on estimating a respondent's location using ratings data

In social science research there are a number of instruments that utilize a rating scale such as a Likert response scale. For a number of reasons a respondent's response vector may not contain responses to each item. This study investigated the effect on a respondent's location estimate when a respondent is presented an item, has ample time to answer the item, but decides to not respond to the item. For these situations different strategies have been developed for handling missing data. In this study, four different approaches for handling missing data were investigated for their capability to mitigate against the effect of omitted responses on person location estimation. These methods included ignoring the omitted response, selecting the "midpoint" response category, hot-decking, and a Likelihood-based approach. A Monte Carlo study was performed and the effect of different levels of omissions on the simulees' location estimates was determined. Results showed that the hot-decking procedure performed the best of methods examined. Implications for practitioners were discussed.