Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them

Nicholas Pindar Dibal; Ray Okafor; Hamadu Dallah

doi:doi:10.11648/j.ijsd.20170304.15

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Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them

Nicholas Pindar Dibal, Ray Okafor, Hamadu Dallah

Published in International Journal of Statistical Distributions and Applications (Volume 3, Issue 4)

Received: 29 September 2017 Accepted: 17 October 2017 Published: 20 November 2017

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Abstract

Missing data in surveys and experimental research is a common occurrence which has serious implications on the validity of inferences. Advances in statistical procedures provides better and efficient methods of handling missing data yet many researches still handle incomplete data in ways that affects the results negatively. We review in detail the mechanisms that generates missingness, and the appropriate methods to account for the missing values to enable the researcher have adequate knowledge to make informed decision on the choice of method to account for missingness.

Published in	International Journal of Statistical Distributions and Applications (Volume 3, Issue 4)
DOI	10.11648/j.ijsd.20170304.15
Page(s)	87-94
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

Missing Data, Inference, Missingness Mechanisms, Ignorable, Non-Ignorable Missingness, Multiple Imputation

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APA Style

Nicholas Pindar Dibal, Ray Okafor, Hamadu Dallah. (2017). Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. International Journal of Statistical Distributions and Applications, 3(4), 87-94. https://doi.org/10.11648/j.ijsd.20170304.15

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ACS Style

Nicholas Pindar Dibal; Ray Okafor; Hamadu Dallah. Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. Int. J. Stat. Distrib. Appl. 2017, 3(4), 87-94. doi: 10.11648/j.ijsd.20170304.15

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AMA Style

Nicholas Pindar Dibal, Ray Okafor, Hamadu Dallah. Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. Int J Stat Distrib Appl. 2017;3(4):87-94. doi: 10.11648/j.ijsd.20170304.15

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@article{10.11648/j.ijsd.20170304.15,
  author = {Nicholas Pindar Dibal and Ray Okafor and Hamadu Dallah},
  title = {Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {3},
  number = {4},
  pages = {87-94},
  doi = {10.11648/j.ijsd.20170304.15},
  url = {https://doi.org/10.11648/j.ijsd.20170304.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170304.15},
  abstract = {Missing data in surveys and experimental research is a common occurrence which has serious implications on the validity of inferences. Advances in statistical procedures provides better and efficient methods of handling missing data yet many researches still handle incomplete data in ways that affects the results negatively. We review in detail the mechanisms that generates missingness, and the appropriate methods to account for the missing values to enable the researcher have adequate knowledge to make informed decision on the choice of method to account for missingness.},
 year = {2017}
}

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TY  - JOUR
T1  - Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them
AU  - Nicholas Pindar Dibal
AU  - Ray Okafor
AU  - Hamadu Dallah
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DO  - 10.11648/j.ijsd.20170304.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 87
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PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20170304.15
AB  - Missing data in surveys and experimental research is a common occurrence which has serious implications on the validity of inferences. Advances in statistical procedures provides better and efficient methods of handling missing data yet many researches still handle incomplete data in ways that affects the results negatively. We review in detail the mechanisms that generates missingness, and the appropriate methods to account for the missing values to enable the researcher have adequate knowledge to make informed decision on the choice of method to account for missingness.
VL  - 3
IS  - 4
ER  -

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Author Information

Nicholas Pindar Dibal

Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Ray Okafor

Department of Mathematics, University of Lagos, Lagos, Nigeria
Hamadu Dallah

Department of Actuarial Science, University of Lagos, Lagos, Nigeria

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APA Style

Nicholas Pindar Dibal, Ray Okafor, Hamadu Dallah. (2017). Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. International Journal of Statistical Distributions and Applications, 3(4), 87-94. https://doi.org/10.11648/j.ijsd.20170304.15

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ACS Style

Nicholas Pindar Dibal; Ray Okafor; Hamadu Dallah. Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. Int. J. Stat. Distrib. Appl. 2017, 3(4), 87-94. doi: 10.11648/j.ijsd.20170304.15

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AMA Style

Nicholas Pindar Dibal, Ray Okafor, Hamadu Dallah. Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them. Int J Stat Distrib Appl. 2017;3(4):87-94. doi: 10.11648/j.ijsd.20170304.15

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@article{10.11648/j.ijsd.20170304.15,
  author = {Nicholas Pindar Dibal and Ray Okafor and Hamadu Dallah},
  title = {Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {3},
  number = {4},
  pages = {87-94},
  doi = {10.11648/j.ijsd.20170304.15},
  url = {https://doi.org/10.11648/j.ijsd.20170304.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170304.15},
  abstract = {Missing data in surveys and experimental research is a common occurrence which has serious implications on the validity of inferences. Advances in statistical procedures provides better and efficient methods of handling missing data yet many researches still handle incomplete data in ways that affects the results negatively. We review in detail the mechanisms that generates missingness, and the appropriate methods to account for the missing values to enable the researcher have adequate knowledge to make informed decision on the choice of method to account for missingness.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Challenges and Implications of Missing Data on the Validity of Inferences and Options for Choosing the Right Strategy in Handling Them
AU  - Nicholas Pindar Dibal
AU  - Ray Okafor
AU  - Hamadu Dallah
Y1  - 2017/11/20
PY  - 2017
N1  - https://doi.org/10.11648/j.ijsd.20170304.15
DO  - 10.11648/j.ijsd.20170304.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 87
EP  - 94
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20170304.15
AB  - Missing data in surveys and experimental research is a common occurrence which has serious implications on the validity of inferences. Advances in statistical procedures provides better and efficient methods of handling missing data yet many researches still handle incomplete data in ways that affects the results negatively. We review in detail the mechanisms that generates missingness, and the appropriate methods to account for the missing values to enable the researcher have adequate knowledge to make informed decision on the choice of method to account for missingness.
VL  - 3
IS  - 4
ER  -

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