Comparative Study of Various Methods of Handling Missing Data

Fredrick Ochieng’ Odhiambo

doi:doi:10.11648/j.mma.20200502.14

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Comparative Study of Various Methods of Handling Missing Data

Fredrick Ochieng’ Odhiambo

Published in Mathematical Modelling and Applications (Volume 5, Issue 2)

Received: 2 October 2019 Accepted: 13 April 2020 Published: 30 April 2020

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Abstract

Scientific literature lack straight forward answer as to the most suitable method for missing data imputation in terms of simplicity, accuracy and ease of use among the existing methods. Exploration various methods of data imputation is done, and then a robust method of data imputation is proposed. The paper uses simulated data sets generated for various distributions. A regression function on the simulated data sets is used and obtained the residual standard errors for the function obtained. Data are randomly from the set of independent variables to create artificial data-non response and use suitable methods to impute the missing data. The method of Mean, regression, hot and cold decking, multiple, median imputation, list wise deletion, EM algorithm and the nearest neighbour method are considered. This paper investigates the three most common traditional methods of handling missing data to establish the most optimal method. The suitability is hence determined by the method whose imputed data sample characteristic does not vary considerably from the original data set before imputation. The variation is here determined using the regression intercept and the residual standard error. R statistical package has been used widely in most of the regression cases. Microsoft excel is used to determine the correlation of columns in hot decking method; this is because it is readily available as a component of Microsoft package. The results from data analysis section indicated an intercept and R-squared values that closely mirror those of original data sets, suggesting that median imputation is a better data imputation method among the conventional methods. This finding is important from the research point of view, given the many cases of data missingness in scientific research. Finding and using the median is simple and as such most researchers have a ready tool at hand for handling missing data.

Published in	Mathematical Modelling and Applications (Volume 5, Issue 2)
DOI	10.11648/j.mma.20200502.14
Page(s)	87-93
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

Regression, Nearest Neighbor, Hot Decking, Median Substitution, Missing Data

References

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APA Style

Fredrick Ochieng’ Odhiambo. (2020). Comparative Study of Various Methods of Handling Missing Data. Mathematical Modelling and Applications, 5(2), 87-93. https://doi.org/10.11648/j.mma.20200502.14

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ACS Style

Fredrick Ochieng’ Odhiambo. Comparative Study of Various Methods of Handling Missing Data. Math. Model. Appl. 2020, 5(2), 87-93. doi: 10.11648/j.mma.20200502.14

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AMA Style

Fredrick Ochieng’ Odhiambo. Comparative Study of Various Methods of Handling Missing Data. Math Model Appl. 2020;5(2):87-93. doi: 10.11648/j.mma.20200502.14

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@article{10.11648/j.mma.20200502.14,
  author = {Fredrick Ochieng’ Odhiambo},
  title = {Comparative Study of Various Methods of Handling Missing Data},
  journal = {Mathematical Modelling and Applications},
  volume = {5},
  number = {2},
  pages = {87-93},
  doi = {10.11648/j.mma.20200502.14},
  url = {https://doi.org/10.11648/j.mma.20200502.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20200502.14},
  abstract = {Scientific literature lack straight forward answer as to the most suitable method for missing data imputation in terms of simplicity, accuracy and ease of use among the existing methods. Exploration various methods of data imputation is done, and then a robust method of data imputation is proposed. The paper uses simulated data sets generated for various distributions. A regression function on the simulated data sets is used and obtained the residual standard errors for the function obtained. Data are randomly from the set of independent variables to create artificial data-non response and use suitable methods to impute the missing data. The method of Mean, regression, hot and cold decking, multiple, median imputation, list wise deletion, EM algorithm and the nearest neighbour method are considered. This paper investigates the three most common traditional methods of handling missing data to establish the most optimal method. The suitability is hence determined by the method whose imputed data sample characteristic does not vary considerably from the original data set before imputation. The variation is here determined using the regression intercept and the residual standard error. R statistical package has been used widely in most of the regression cases. Microsoft excel is used to determine the correlation of columns in hot decking method; this is because it is readily available as a component of Microsoft package. The results from data analysis section indicated an intercept and R-squared values that closely mirror those of original data sets, suggesting that median imputation is a better data imputation method among the conventional methods. This finding is important from the research point of view, given the many cases of data missingness in scientific research. Finding and using the median is simple and as such most researchers have a ready tool at hand for handling missing data.},
 year = {2020}
}

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TY  - JOUR
T1  - Comparative Study of Various Methods of Handling Missing Data
AU  - Fredrick Ochieng’ Odhiambo
Y1  - 2020/04/30
PY  - 2020
N1  - https://doi.org/10.11648/j.mma.20200502.14
DO  - 10.11648/j.mma.20200502.14
T2  - Mathematical Modelling and Applications
JF  - Mathematical Modelling and Applications
JO  - Mathematical Modelling and Applications
SP  - 87
EP  - 93
PB  - Science Publishing Group
SN  - 2575-1794
UR  - https://doi.org/10.11648/j.mma.20200502.14
AB  - Scientific literature lack straight forward answer as to the most suitable method for missing data imputation in terms of simplicity, accuracy and ease of use among the existing methods. Exploration various methods of data imputation is done, and then a robust method of data imputation is proposed. The paper uses simulated data sets generated for various distributions. A regression function on the simulated data sets is used and obtained the residual standard errors for the function obtained. Data are randomly from the set of independent variables to create artificial data-non response and use suitable methods to impute the missing data. The method of Mean, regression, hot and cold decking, multiple, median imputation, list wise deletion, EM algorithm and the nearest neighbour method are considered. This paper investigates the three most common traditional methods of handling missing data to establish the most optimal method. The suitability is hence determined by the method whose imputed data sample characteristic does not vary considerably from the original data set before imputation. The variation is here determined using the regression intercept and the residual standard error. R statistical package has been used widely in most of the regression cases. Microsoft excel is used to determine the correlation of columns in hot decking method; this is because it is readily available as a component of Microsoft package. The results from data analysis section indicated an intercept and R-squared values that closely mirror those of original data sets, suggesting that median imputation is a better data imputation method among the conventional methods. This finding is important from the research point of view, given the many cases of data missingness in scientific research. Finding and using the median is simple and as such most researchers have a ready tool at hand for handling missing data.
VL  - 5
IS  - 2
ER  -

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Author Information

Fredrick Ochieng’ Odhiambo

Department of Mathematics and Actuarial Sciences, South Eastern Kenya University (Seku), Kitui, Kenya

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Plain Text BibTeX RIS

APA Style

Fredrick Ochieng’ Odhiambo. (2020). Comparative Study of Various Methods of Handling Missing Data. Mathematical Modelling and Applications, 5(2), 87-93. https://doi.org/10.11648/j.mma.20200502.14

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ACS Style

Fredrick Ochieng’ Odhiambo. Comparative Study of Various Methods of Handling Missing Data. Math. Model. Appl. 2020, 5(2), 87-93. doi: 10.11648/j.mma.20200502.14

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AMA Style

Fredrick Ochieng’ Odhiambo. Comparative Study of Various Methods of Handling Missing Data. Math Model Appl. 2020;5(2):87-93. doi: 10.11648/j.mma.20200502.14

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@article{10.11648/j.mma.20200502.14,
  author = {Fredrick Ochieng’ Odhiambo},
  title = {Comparative Study of Various Methods of Handling Missing Data},
  journal = {Mathematical Modelling and Applications},
  volume = {5},
  number = {2},
  pages = {87-93},
  doi = {10.11648/j.mma.20200502.14},
  url = {https://doi.org/10.11648/j.mma.20200502.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20200502.14},
  abstract = {Scientific literature lack straight forward answer as to the most suitable method for missing data imputation in terms of simplicity, accuracy and ease of use among the existing methods. Exploration various methods of data imputation is done, and then a robust method of data imputation is proposed. The paper uses simulated data sets generated for various distributions. A regression function on the simulated data sets is used and obtained the residual standard errors for the function obtained. Data are randomly from the set of independent variables to create artificial data-non response and use suitable methods to impute the missing data. The method of Mean, regression, hot and cold decking, multiple, median imputation, list wise deletion, EM algorithm and the nearest neighbour method are considered. This paper investigates the three most common traditional methods of handling missing data to establish the most optimal method. The suitability is hence determined by the method whose imputed data sample characteristic does not vary considerably from the original data set before imputation. The variation is here determined using the regression intercept and the residual standard error. R statistical package has been used widely in most of the regression cases. Microsoft excel is used to determine the correlation of columns in hot decking method; this is because it is readily available as a component of Microsoft package. The results from data analysis section indicated an intercept and R-squared values that closely mirror those of original data sets, suggesting that median imputation is a better data imputation method among the conventional methods. This finding is important from the research point of view, given the many cases of data missingness in scientific research. Finding and using the median is simple and as such most researchers have a ready tool at hand for handling missing data.},
 year = {2020}
}

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TY  - JOUR
T1  - Comparative Study of Various Methods of Handling Missing Data
AU  - Fredrick Ochieng’ Odhiambo
Y1  - 2020/04/30
PY  - 2020
N1  - https://doi.org/10.11648/j.mma.20200502.14
DO  - 10.11648/j.mma.20200502.14
T2  - Mathematical Modelling and Applications
JF  - Mathematical Modelling and Applications
JO  - Mathematical Modelling and Applications
SP  - 87
EP  - 93
PB  - Science Publishing Group
SN  - 2575-1794
UR  - https://doi.org/10.11648/j.mma.20200502.14
AB  - Scientific literature lack straight forward answer as to the most suitable method for missing data imputation in terms of simplicity, accuracy and ease of use among the existing methods. Exploration various methods of data imputation is done, and then a robust method of data imputation is proposed. The paper uses simulated data sets generated for various distributions. A regression function on the simulated data sets is used and obtained the residual standard errors for the function obtained. Data are randomly from the set of independent variables to create artificial data-non response and use suitable methods to impute the missing data. The method of Mean, regression, hot and cold decking, multiple, median imputation, list wise deletion, EM algorithm and the nearest neighbour method are considered. This paper investigates the three most common traditional methods of handling missing data to establish the most optimal method. The suitability is hence determined by the method whose imputed data sample characteristic does not vary considerably from the original data set before imputation. The variation is here determined using the regression intercept and the residual standard error. R statistical package has been used widely in most of the regression cases. Microsoft excel is used to determine the correlation of columns in hot decking method; this is because it is readily available as a component of Microsoft package. The results from data analysis section indicated an intercept and R-squared values that closely mirror those of original data sets, suggesting that median imputation is a better data imputation method among the conventional methods. This finding is important from the research point of view, given the many cases of data missingness in scientific research. Finding and using the median is simple and as such most researchers have a ready tool at hand for handling missing data.
VL  - 5
IS  - 2
ER  -

Copy | Download