Science Journal of Public Health

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

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.

DOI 10.11648/j.sjph.20190705.13
Published in Science Journal of Public Health (Volume 7, Issue 5, September 2019)
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), 2024. 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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Author Information
  • Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China

  • Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China

  • Beijing Health Promotion Committee Office, Centers for Diseases Control and Prevention, Beijing, China; Center for Preventive Medicine Research, Beijing, China

  • Beijing Health Promotion Committee Office, Centers for Diseases Control and Prevention, Beijing, China; Center for Preventive Medicine Research, Beijing, China

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    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://download.sciencepg.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
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    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
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    PB  - Science Publishing Group
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    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.
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