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Abstract

Missing-data is a problem that occurs frequently in survey data. Missing-data can result in bias and reduced efficiency for regression estimates. The objective of the current study is to analyze the impact of missing-data imputation, using multiple imputation methods, on regression estimates for survey data. The results of the current study show that multiple imputation methods result in lower standard errors for regression estimates than the regression using only complete observations. Multiple imputation methods also resulted in chances in magnitude, sign, and statistical significance for some the regression coefficient estimates. Hence, ignoring the missing-data problem might lead to significant differences in the results for regression analysis and the policy recommendations based on these results. Keywords: Missing-data, Multiple Imputation, Bayesian Inference, Household Surveys.

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