Comparison of Methods for Processing Missing Values in Large Sample Survey Data

Lingling Wang; Dandan Zhang; Jiali Duan; Ruoran Lyu

doi:doi:10.11648/j.sjph.20190705.13

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Comparison of Methods for Processing Missing Values in Large Sample Survey Data

Lingling Wang, Dandan Zhang, Jiali Duan, Ruoran Lyu

Published in Science Journal of Public Health (Volume 7, Issue 5)

Received: 25 August 2019 Accepted: 16 September 2019 Published: 26 September 2019

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Abstract

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.

Published in	Science Journal of Public Health (Volume 7, Issue 5)
DOI	10.11648/j.sjph.20190705.13
Page(s)	151-158
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Survey Data, Missing Value, Multiple Imputation (MI), Complete Case Analysis (CCA), Single Imputation (SI)

References

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APA Style

Lingling Wang, Dandan Zhang, Jiali Duan, Ruoran Lyu. (2019). Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Science Journal of Public Health, 7(5), 151-158. https://doi.org/10.11648/j.sjph.20190705.13

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ACS Style

Lingling Wang; Dandan Zhang; Jiali Duan; Ruoran Lyu. Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Sci. J. Public Health 2019, 7(5), 151-158. doi: 10.11648/j.sjph.20190705.13

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AMA Style

Lingling Wang, Dandan Zhang, Jiali Duan, Ruoran Lyu. Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Sci J Public Health. 2019;7(5):151-158. doi: 10.11648/j.sjph.20190705.13

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@article{10.11648/j.sjph.20190705.13,
  author = {Lingling Wang and Dandan Zhang and Jiali Duan and Ruoran Lyu},
  title = {Comparison of Methods for Processing Missing Values in Large Sample Survey Data},
  journal = {Science Journal of Public Health},
  volume = {7},
  number = {5},
  pages = {151-158},
  doi = {10.11648/j.sjph.20190705.13},
  url = {https://doi.org/10.11648/j.sjph.20190705.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20190705.13},
  abstract = {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.},
 year = {2019}
}

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TY - JOUR
T1 - Comparison of Methods for Processing Missing Values in Large Sample Survey Data
AU - Lingling Wang
AU - Dandan Zhang
AU - Jiali Duan
AU - Ruoran Lyu
Y1 - 2019/09/26
PY - 2019
N1 - https://doi.org/10.11648/j.sjph.20190705.13
DO - 10.11648/j.sjph.20190705.13
T2 - Science Journal of Public Health
JF - Science Journal of Public Health
JO - Science Journal of Public Health
SP - 151
EP - 158
PB - Science Publishing Group
SN - 2328-7950
UR - https://doi.org/10.11648/j.sjph.20190705.13
AB - 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.
VL - 7
IS - 5
ER -

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Author Information

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
Ruoran Lyu

Beijing Health Promotion Committee Office, Centers for Diseases Control and Prevention, Beijing, China

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APA Style

Lingling Wang, Dandan Zhang, Jiali Duan, Ruoran Lyu. (2019). Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Science Journal of Public Health, 7(5), 151-158. https://doi.org/10.11648/j.sjph.20190705.13

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ACS Style

Lingling Wang; Dandan Zhang; Jiali Duan; Ruoran Lyu. Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Sci. J. Public Health 2019, 7(5), 151-158. doi: 10.11648/j.sjph.20190705.13

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AMA Style

Lingling Wang, Dandan Zhang, Jiali Duan, Ruoran Lyu. Comparison of Methods for Processing Missing Values in Large Sample Survey Data. Sci J Public Health. 2019;7(5):151-158. doi: 10.11648/j.sjph.20190705.13

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@article{10.11648/j.sjph.20190705.13,
  author = {Lingling Wang and Dandan Zhang and Jiali Duan and Ruoran Lyu},
  title = {Comparison of Methods for Processing Missing Values in Large Sample Survey Data},
  journal = {Science Journal of Public Health},
  volume = {7},
  number = {5},
  pages = {151-158},
  doi = {10.11648/j.sjph.20190705.13},
  url = {https://doi.org/10.11648/j.sjph.20190705.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20190705.13},
  abstract = {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.},
 year = {2019}
}

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