Missing data methods for arbitrary missingness with small samples

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. ARTICLE HISTORY

Computerized Sign Language-Based Literacy Training for Deaf and Hard-of-Hearing Children

“…Nevertheless, this finding is a replication of earlier findings reporting concurrent connections between sign language skills and reading skills in DHH signing children ( Hermans et al, 2008b ; McQuarrie & Abbott, 2013 ; Schönström, 2010 ), with the added value of being longitudinal. However, it should be interpreted with caution, given the small and heterogeneous sample and a relatively high degree of missing data ( Davis et al, 2013 ; Maas & Hox, 2005 ; McNeish, 2017 ).…”

“…HLMs provide unbiased estimates if data is missing under the MCAR (missing completely at random) or MAR (missing at random) mechanisms ( Enders, 2010 ). However, in small samples missing data can further increase the risk of inflated Type 1 errors ( McNeish, 2017 ), which warrants even more caution when interpreting results. Given that missing data was mainly due to technical errors in the present study, a MAR mechanism was assumed for all missing data.…”

A feasibility study comparing a web‐based intervention to a workshop intervention for caregivers of adults with eating disorders

“…Studies have supported using multiple imputation with small samples (Barnes, Lindborg, & Seaman, ; Cheema, ; Hardt, Herke, & Leonhart, ). For example, studies with samples of 50 or more with 10% missing data at random utilizing multiple imputation demonstrate acceptable type I error rates (McNeish, ).…”

Statistical Inference in Missing Data by MCMC and Non-MCMC Multiple Imputation Algorithms: Assessing the Effects of Between-Imputation Iterations

“…Seven studies compared different multiple imputation algorithms (King et al 2001;Horton and Lipsitz 2002;Horton and Kleinman 2007;Lee and Carlin 2010;Hardt, Herke, and Leonhart 2012;Kropko et al 2014;McNeish 2017). The comparative perspective in most of the seven studies, except King et al (2001), is based on the difference between joint modeling and conditional modeling.…”