Missing data methods for arbitrary missingness with small samples. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: McNeish, Daniel
• Is Part Of: Journal of applied statistics. Volume 44:Issue 1(2017)
• Page Start: 24
• Page End: 39
• Publication Date: 2017-01-02
• Subjects: Small sample -- missing data -- multiple imputation -- full information maximum likelihood -- incomplete data -- finite sample -- Monte Carlo simulation; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1158246
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Missing data are a prevalent and widespread data analytic issue and previous studies have performed simulations to compare the performance of missing data methods in various contexts and for various models; however, one such context that has yet to receive much attention in the literature is the handling of missing data with small samples, particularly when the missingness is arbitrary. Prior studies have either compared methods for small samples with monotone missingness commonly found in longitudinal studies or have investigated the performance of a single method to handle arbitrary missingness with small samples but studies have yet to compare the relative performance of commonly implemented missing data methods for small samples with arbitrary missingness. This study conducts a simulation study to compare and assess the small sample performance of maximum likelihood, listwise deletion, joint multiple imputation, and fully conditional specification multiple imputation for a single-level regression model with a continuous outcome. Results showed that, provided assumptions are met, joint multiple imputation unanimously performed best of the methods examined in the conditions under study.