The Performance of Multiple Imputation for Likert-type Items with Missing Data
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May 1, 2010
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Walter Leite
University of Florida
S. Natasha Beretvas
The University of Texas at Austin
Abstract
The performance of multiple imputation (MI) for missing data in Likert-type items assuming multivariate normality was assessed using simulation methods. MI was robust to violations of continuity and normality. With 30% of missing data, MAR conditions resulted in negatively biased correlations. With 50% missingness, all results were negatively biased.
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