Methods for Handling Missing Data for Multiple-Item Questionnaires

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)
Sydney R Siver (Creator)
East Carolina University (ECU )
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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 Recommendations for handling missing data and default options in software packages often use outdated , suboptimal methods for missing data.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.

Additional Information

Language: English
Date: 2017
person mean imputation, Monte Carlo

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