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Activity Number: 558
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Education
Abstract #319733
Title: Missing Data and Complex Sample Surveys: The Impact of Listwise Deletion vs. Multiple Imputation on Point and Interval Estimates When Data Are MCAR and MAR
Author(s): DeAnn Trevathan* and Anh Kellermann and Jeffrey Kromrey
Companies: University of South Florida and University of South Florida and University of South Florida
Keywords: survey research ; complex samples ; missing data ; multiple imputation

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. Character count: 923 of 1200

Authors who are presenting talks have a * after their name.

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