Science Journal of Public Health
Volume 7, Issue 5, September 2019, Pages: 151-158
Received: Aug. 25, 2019;
Accepted: Sep. 16, 2019;
Published: Sep. 26, 2019
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Lingling Wang, Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
Dandan Zhang, Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
Jiali Duan, Beijing Health Promotion Committee Office, Centers for Diseases Control and Prevention, Beijing, China; Center for Preventive Medicine Research, Beijing, China
Ruoran Lyu, Beijing Health Promotion Committee Office, Centers for Diseases Control and Prevention, Beijing, China; Center for Preventive Medicine Research, Beijing, China
Missing data occurs in every field and most researchers choose simple approach to deal with. But this approach may introduce bias and result in inaccurate results. In this study, we will explore the method suitable for large sample and multivariate missing data patterns. In this paper, we utilized a cross-sectional survey data, providing information about youth health risk behavior in Beijing. Using R to simulate random missing data sets with different proportion of missing data based on the survey data set. For each of the missing data set, complete case analysis (CCA), single imputation (SI) and multiple imputation (MI) were adopted to process this and overall 30 complete data sets were obtained. Finally, logistic regression was used to analysis these complete data sets. The indicator (Akaike's Information Criterion, AIC) is used to evaluate both advantages and disadvantages of the three methods and the other indicators such as the significance of the regression coefficients (β), the fraction of missing information (FMI) are utilized to evaluate the applicability of the MI. Compared with the original data set K, the value of AIC of data sets processed by CCA and SI gradually decreases and the relative error gradually increases with the increase of the proportion of missing data. The value of AIC of data sets processed by MI changes slightly. With the increase of the proportion of missing data, especially more than 30%, the meaningless variables of the regression coefficient and the value of FMI gradually increased. Under different proportion of missing data, the MI performs well compared with CCA and SI. When dealing with missing values under MCAR, we recommend using MI instead of CCA and SI. Second, the changing of FMI can also be used as an indicator of MI to process missing data. Third, it is suitable for MI to process large sample survey data, and no more than 30% of proportion of missing data is the proper scope of application of MI.
Comparison of Methods for Processing Missing Values in Large Sample Survey Data, Science Journal of Public Health.
Vol. 7, No. 5,
2019, pp. 151-158.
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