Methods for Handling Missing Data for Multiple-Item Questionnaires

Abstract

Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human resource management literature. Recommendations for handling missing data and default options in software packages often use outdated, suboptimal methods for missing data. Resulting analyses tend to be biased, underpowered, or both. Best practice recommends for the handling of missing data includes the use of multiple imputation (MI) methods. However, this method is often ignored in favor of more convenient methods. For industrial/organizational psychologists, missing data is particularly problematic on multiple-item questionnaires, such as the Survey of Perceived Organizational Support (SPOS). Person mean imputation is one of the most common methods used to handle missing data on multiple-item questionnaires. However, it makes strong assumptions about the missing data mechanism and the underlying factor structure of a measure and should be avoided, particularly if there is a high rate of non-response. MI does not make the same assumptions as person mean imputation and may be a superior method when items are missing from a multiple-item questionnaire. Results indicate that PMI and MI provide similar results, however PMI may outperform MI when the number of variables is large.