Logit Models for Household Food Insecurity Classification

Abraham Yeyo Owino; Leonard Kiboijana Atuhaire; Ronald Wesonga; Fabian Nabugoomu; Elijah Muwanga-Zaake

doi:doi:10.11648/j.ajtas.20140302.14

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Logit Models for Household Food Insecurity Classification

Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 2)

Received: 20 March 2014 Accepted: 22 April 2014 Published: 30 April 2014

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Abstract

Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify which of them significantly influence household food insecurity. Logit models were then developed using the variables identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models. Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the combined households for two districts. However, when fitted for separate districts, it was established that 99% of households in Busia and 96% in Tororo district respectively, were found to be food insecure

Published in	American Journal of Theoretical and Applied Statistics (Volume 3, Issue 2)
DOI	10.11648/j.ajtas.20140302.14
Page(s)	49-54
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), 2014. Published by Science Publishing Group

Keywords

Hybrid Dependent Variable, Latent Variable, Factor Analysis, Logit Model

References

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[2]	A. D. Jones, F. M. Ngure, G. Pelto, and S. L. Young, "What are we assessing when we measure food security? A compendium and review of current metrics," Advances in Nutrition: An International Review Journal, vol. 4, pp. 481-505, 2013.
[3]	A. Y. Owino, L. K. Atuhaire, R. Wesonga, F. Nabugoomu, and E. S. K. Muwanga-Zaake, "Determining Factors that Influence Household Food Insecurity in Uganda: A case study of Tororo and Busia districts," International Journal of Sciences: Basic and Applied Research (IJSBAR), vol. 14, pp. 394 - 404, 2014.
[4]	G. Bickel, M. Nord, C. Price, W. Hamilton, and J. Cook, "Guide to measuring household food security," Alexandria. Department of Agriculture Food and Nutrition Service, 2000.
[5]	L. L. Ching, E. Dano, and H. Jhamtani, "Rethinking agriculture," Third World Resurgence, 2010.
[6]	J. G. M. Majaliwa, M. K. Magunda, M. M. Tenywa, and F. Musitwa, "Soil and nutrient losses from major agricultural land-use practices in the Lake Victoria basin," 2012.
[7]	C. Carletto, A. Zezza, and R. Banerjee, "Towards better measurement of household food security: Harmonizing indicators and the role of household surveys," Global Food Security, vol. 2, pp. 30-40, 2013.
[8]	J. L. Dzanja, M. Christie, I. Fazey, and T. Hyde, "The role of social capital on rural food security: the case study of Dowa and Lilongwe Districts in Central Malawi," 2013.
[9]	L. R. Fabrigar, D. T. Wegener, R. C. MacCallum, and E. J. Strahan, "Evaluating the use of exploratory factor analysis in psychological research," Psychological methods, vol. 4, p. 272, 1999.
[10]	O. Faye, A. Baschieri, J. Falkingham, and K. Muindi, "Hunger and food insecurity in Nairobi's slums: an assessment using IRT models," Journal of Urban Health, vol. 88, pp. 235-255, 2011.
[11]	J. Olson and L. Berry, "Land degradation in Uganda: its extent and impact," available at lada. virtualcentre. org/eims/download. asp, 2003.
[12]	P. McMichael and M. Schneider, "Food security politics and the Millennium Development Goals," Third World Quarterly, vol. 32, pp. 119-139, 2011.

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Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake. (2014). Logit Models for Household Food Insecurity Classification. American Journal of Theoretical and Applied Statistics, 3(2), 49-54. https://doi.org/10.11648/j.ajtas.20140302.14

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ACS Style

Abraham Yeyo Owino; Leonard Kiboijana Atuhaire; Ronald Wesonga; Fabian Nabugoomu; Elijah Muwanga-Zaake. Logit Models for Household Food Insecurity Classification. Am. J. Theor. Appl. Stat. 2014, 3(2), 49-54. doi: 10.11648/j.ajtas.20140302.14

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AMA Style

Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake. Logit Models for Household Food Insecurity Classification. Am J Theor Appl Stat. 2014;3(2):49-54. doi: 10.11648/j.ajtas.20140302.14

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@article{10.11648/j.ajtas.20140302.14,
  author = {Abraham Yeyo Owino and Leonard Kiboijana Atuhaire and Ronald Wesonga and Fabian Nabugoomu and Elijah Muwanga-Zaake},
  title = {Logit Models for Household Food Insecurity Classification},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {3},
  number = {2},
  pages = {49-54},
  doi = {10.11648/j.ajtas.20140302.14},
  url = {https://doi.org/10.11648/j.ajtas.20140302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20140302.14},
  abstract = {Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify which of them significantly influence household food insecurity. Logit models were then developed using the variables identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models. Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the combined households for two districts. However, when fitted for separate districts, it was established that 99% of households in Busia and 96% in Tororo district respectively, were found to be food insecure},
 year = {2014}
}

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TY  - JOUR
T1  - Logit Models for Household Food Insecurity Classification
AU  - Abraham Yeyo Owino
AU  - Leonard Kiboijana Atuhaire
AU  - Ronald Wesonga
AU  - Fabian Nabugoomu
AU  - Elijah Muwanga-Zaake
Y1  - 2014/04/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ajtas.20140302.14
DO  - 10.11648/j.ajtas.20140302.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 49
EP  - 54
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140302.14
AB  - Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify which of them significantly influence household food insecurity. Logit models were then developed using the variables identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models. Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the combined households for two districts. However, when fitted for separate districts, it was established that 99% of households in Busia and 96% in Tororo district respectively, were found to be food insecure
VL  - 3
IS  - 2
ER  -

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Author Information

Abraham Yeyo Owino

Department of Statistics and Actuarial Sciences, College of Business and Management Sciences, Kampala, Uganda
Leonard Kiboijana Atuhaire

Department of Statistics and Actuarial Sciences, College of Business and Management Sciences, Kampala, Uganda
Ronald Wesonga

Department of Statistics and Actuarial Sciences, College of Business and Management Sciences, Kampala, Uganda
Fabian Nabugoomu

Department of Statistics and Actuarial Sciences, College of Business and Management Sciences, Kampala, Uganda
Elijah Muwanga-Zaake

Department of Statistics and Actuarial Sciences, College of Business and Management Sciences, Kampala, Uganda

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APA Style

Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake. (2014). Logit Models for Household Food Insecurity Classification. American Journal of Theoretical and Applied Statistics, 3(2), 49-54. https://doi.org/10.11648/j.ajtas.20140302.14

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ACS Style

Abraham Yeyo Owino; Leonard Kiboijana Atuhaire; Ronald Wesonga; Fabian Nabugoomu; Elijah Muwanga-Zaake. Logit Models for Household Food Insecurity Classification. Am. J. Theor. Appl. Stat. 2014, 3(2), 49-54. doi: 10.11648/j.ajtas.20140302.14

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AMA Style

Abraham Yeyo Owino, Leonard Kiboijana Atuhaire, Ronald Wesonga, Fabian Nabugoomu, Elijah Muwanga-Zaake. Logit Models for Household Food Insecurity Classification. Am J Theor Appl Stat. 2014;3(2):49-54. doi: 10.11648/j.ajtas.20140302.14

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@article{10.11648/j.ajtas.20140302.14,
  author = {Abraham Yeyo Owino and Leonard Kiboijana Atuhaire and Ronald Wesonga and Fabian Nabugoomu and Elijah Muwanga-Zaake},
  title = {Logit Models for Household Food Insecurity Classification},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {3},
  number = {2},
  pages = {49-54},
  doi = {10.11648/j.ajtas.20140302.14},
  url = {https://doi.org/10.11648/j.ajtas.20140302.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20140302.14},
  abstract = {Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify which of them significantly influence household food insecurity. Logit models were then developed using the variables identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models. Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the combined households for two districts. However, when fitted for separate districts, it was established that 99% of households in Busia and 96% in Tororo district respectively, were found to be food insecure},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Logit Models for Household Food Insecurity Classification
AU  - Abraham Yeyo Owino
AU  - Leonard Kiboijana Atuhaire
AU  - Ronald Wesonga
AU  - Fabian Nabugoomu
AU  - Elijah Muwanga-Zaake
Y1  - 2014/04/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ajtas.20140302.14
DO  - 10.11648/j.ajtas.20140302.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 49
EP  - 54
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140302.14
AB  - Micro-level measurement of food insecurity is a necessary approach towards a more feasible solution to the global problem for proper classification of households by food insecurity status. Measurement of food insecurity is a challenge because it is a multi-faceted latent and continuous phenomenon explained by a wide range of both quantitative and qualitative variables. In this paper, we examined the quantitative variables and applied exploratory factor analysis to identify which of them significantly influence household food insecurity. Logit models were then developed using the variables identified. Further, empirical data obtained from Tororo and Busia rural households in Uganda were used to fit the models. Four logit models based on four scenarios were developed and compared. The key findings pointed to the fact that if households were to be correctly analyzed and classified into the right food security category, a hybrid dependent variable that represents as many aspects of food insecurity as possible should be used. The model correctly classified 90 % of the combined households for two districts. However, when fitted for separate districts, it was established that 99% of households in Busia and 96% in Tororo district respectively, were found to be food insecure
VL  - 3
IS  - 2
ER  -

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