The Performance of Multiple Imputation for Likert-type Items with Missing Data

Authors

Walter Leite, University of FloridaFollow
S. Natasha Beretvas, The University of Texas at AustinFollow

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.

DOI

10.22237/jmasm/1272686820