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Logistic Regression with Missing Data: A Comparison of Handling Methods, and Effects of Percent Missing Values

Sutthipong Meeyai
School of Transportation Engineering, Suranaree University of Technology, 111 University Ave., Muang, Nakhon Ratchasima, 30000, Thailand

Abstract—The aim of this article is to compare five popular missing data handling methods: listwise deletion, mean substitution, regression imputation, stochastic imputation, and multiple imputation. Three missing data mechanisms are investigated: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). A Monte Carlo simulation is applied to simulate data and then logistic regression parameters are estimated. Our findings show that, among the five missing data handling methods, multiple imputation performs well on both MCAR and MAR. There is no evidence indicating that listwise deletion and multiple imputation produce biased parameters for MCAR. None of these techniques can handle MNAR. Finally, this article suggests maximum percent missing data and a sample size for listwise deletion and multiple imputation techniques.

Index Terms—logistic regression, multiple imputation, listwise deletion, missing at random, incomplete data

Cite: Sutthipong Meeyai, "Logistic Regression with Missing Data: A Comparison of Handling Methods, and Effects of Percent Missing Values," Journal of Traffic and Logistics Engineering, Vol. 4, No. 2, pp. 128-134, December 2016. doi: 10.18178/jtle.4.2.128-134
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