Saturday, February 25
PS3 Poster Session 3 and Continental Breakfast Sat, Feb 25, 8:00 AM - 9:15 AM
Conference Center AB

Listwise Deletion or Multiple Imputation When Complex Sample Data Are MCAR or MAR: A Guide to Selecting an Appropriate Missing Data Treatment Method (303393)

*Anh P. Kellermann, University of South Florida 
Jeffrey D. Kromrey , University of South Florida 
DeAnn Trevathan, University of South Florida 

Keywords: complex sample surveys, missing data treatment, multiple imputation, listwise deletion, MCAR, MAR

Social scientists from many fields use secondary data analysis of complex sample surveys to answer research questions and test hypotheses. Despite great care taken to obtain the data needed, missing data are frequently found in such samples. Even though missing data is a ubiquitous problem, the methodological literature has provided little guidance to inform the appropriate treatment for such missingness. This Monte Carlo study investigated the impact of missing data treatment (multiple imputations versus listwise deletion) when data are MCAR and MAR. By using 10% to 70% of missing data (along with complete sample conditions as a reference point for interpretation of results), the research focused on the parameter estimates in multiple regression analysis in complex sample data. Results are presented in terms of statistical bias in the parameter estimates and both confidence interval coverage and width.

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