A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables

Wei Wu, Fan Jia, Craig Enders

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naïvely rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.

Original languageEnglish (US)
Pages (from-to)484-503
Number of pages20
JournalMultivariate Behavioral Research
Volume50
Issue number5
DOIs
StatePublished - Sep 3 2015

Keywords

  • missing data
  • multiple imputation
  • ordinal data

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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