Wu, WeiLang, Kyle Matthew2015-12-032015-12-032015-05-312015http://dissertations.umi.com/ku:14018https://hdl.handle.net/1808/19062Correctly specifying the imputation model when conducting multiple imputation remains one of the most significant challenges in missing data analysis. This dissertation introduces a robust multiple imputation technique, Multiple Imputation with the Bayesian Elastic Net (MIBEN), as a remedy for this difficulty. A Monte Carlo simulation study was conducted to assess the performance of the MIBEN technique and compare it to several state-of-the-art multiple imputation methods.106 pagesenCopyright held by the author.Quantitative psychology and psychometricsStatisticsBayesian StatisticsBig DataMissing DataMultiple ImputationP > NRegularized RegressionDissertationhttps://orcid.org/0000-0001-5340-7849openAccess