Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss

Jeremiah Kiingati; Samuel Mwalili; Anthony Waititu

doi:doi:10.11648/j.ijdsa.20200601.17

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Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss

Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu

Published in International Journal of Data Science and Analysis (Volume 6, Issue 1)

Received: 15 January 2020 Accepted: 4 February 2020 Published: 24 March 2020

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Abstract

There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.

Published in	International Journal of Data Science and Analysis (Volume 6, Issue 1)
DOI	10.11648/j.ijdsa.20200601.17
Page(s)	58-63
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

Presidential Elections, Election Forecasting, Operations Research, Bayesian Prediction Models

References

[1]	Abaramowitz, Alan I. 2012. “Forecasting in a Polarized Era: The Time for Change Model and the 2012 Presidential Election.” P. S. Political Science and Politics 45, 4, 618-619.
[2]	Abramowitz, Alan I. 2008. “Forecasting the 2008 Presidential Election with the Time-for-Change Model.” P. S. Political Science and Politics 41, 4, 691-695.
[3]	Abrams, Burton A. and James L. Butkiewicz. 1995. “The Influence of State-Level Economic Conditions on the 1992 U.S. Presidential Election.” Public Choice 85, 1, 1-10.
[4]	Ahammed, M. S., Newaz, M. N., & Dey, A. (2019). Analyzing Political Opinions and Prediction of Voting Patterns in the US Election with Data Mining Approaches. Global Journal of Computer Science and Technology.
[5]	Althaus, S. L., Nardulli, P. F., & Shaw, D. R. (2002). Candidate appearances in presidential elections, 1972-2000. Political Communication, 19 (1), 49-72.
[6]	Campbell, J. E. (2014). Issues in presidential election forecasting: election margins, incumbency, and model credibility. PS: Political Science & Politics, 47 (2), 301-303.
[7]	Campbell, J. E. (2016). The Trial-Heat and Seats-in-Trouble forecasts of the 2016 Presidential.
[8]	Congressional elections. PS: Political Science & Politics, 49 (4), 664-668.
[9]	Cohen, J. E. (2006). Public opinion in state politics. Stanford University Press.
[10]	Cohen, J. E. (2015). Presidential Leadership in Public Opinion. Cambridge University Press.
[11]	Dorsey, Margaret E., and Miguel Díaz-Barriga. "Senator Barack Obama and immigration reform." Journal of Black Studies 38, no. 1 (2007): 90-104.
[12]	Feld, S. L., & Grofman, B. (2010). Puzzles and paradoxes involving averages: An intuitive approach. In Collective Decision Making (pp. 137-150). Springer, Berlin, Heidelberg.
[13]	Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31 (6), 649-679.
[14]	Gelman, A., Katz, J. N., & Tuerlinckx, F. (2002). The mathematics and statistics of voting power. Statistical Science, 420-435.
[15]	Holbrook, T. M., & DeSart, J. A. (1999). Using state polls to forecast presidential election outcomes in the American states. International Journal of Forecasting, 15 (2), 137-142.
[16]	Kennedy, R., Wojcik, S., & Lazer, D. (2017). Improving election prediction internationally. Science, 355 (6324), 515-520.
[17]	Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Political Analysis, 12 (4), 375-385.
[18]	Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31 (3), 980-991.

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Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. (2020). Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. International Journal of Data Science and Analysis, 6(1), 58-63. https://doi.org/10.11648/j.ijdsa.20200601.17

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ACS Style

Jeremiah Kiingati; Samuel Mwalili; Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int. J. Data Sci. Anal. 2020, 6(1), 58-63. doi: 10.11648/j.ijdsa.20200601.17

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AMA Style

Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int J Data Sci Anal. 2020;6(1):58-63. doi: 10.11648/j.ijdsa.20200601.17

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@article{10.11648/j.ijdsa.20200601.17,
  author = {Jeremiah Kiingati and Samuel Mwalili and Anthony Waititu},
  title = {Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {58-63},
  doi = {10.11648/j.ijdsa.20200601.17},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.17},
  abstract = {There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.},
 year = {2020}
}

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TY  - JOUR
T1  - Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss
AU  - Jeremiah Kiingati
AU  - Samuel Mwalili
AU  - Anthony Waititu
Y1  - 2020/03/24
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200601.17
DO  - 10.11648/j.ijdsa.20200601.17
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 58
EP  - 63
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200601.17
AB  - There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.
VL  - 6
IS  - 1
ER  -

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Author Information

Jeremiah Kiingati

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya
Samuel Mwalili

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya
Anthony Waititu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya

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APA Style

Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. (2020). Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. International Journal of Data Science and Analysis, 6(1), 58-63. https://doi.org/10.11648/j.ijdsa.20200601.17

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ACS Style

Jeremiah Kiingati; Samuel Mwalili; Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int. J. Data Sci. Anal. 2020, 6(1), 58-63. doi: 10.11648/j.ijdsa.20200601.17

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AMA Style

Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int J Data Sci Anal. 2020;6(1):58-63. doi: 10.11648/j.ijdsa.20200601.17

Copy | Download

@article{10.11648/j.ijdsa.20200601.17,
  author = {Jeremiah Kiingati and Samuel Mwalili and Anthony Waititu},
  title = {Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {58-63},
  doi = {10.11648/j.ijdsa.20200601.17},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.17},
  abstract = {There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss
AU  - Jeremiah Kiingati
AU  - Samuel Mwalili
AU  - Anthony Waititu
Y1  - 2020/03/24
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200601.17
DO  - 10.11648/j.ijdsa.20200601.17
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 58
EP  - 63
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200601.17
AB  - There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.
VL  - 6
IS  - 1
ER  -

Copy | Download