Multinomial Logistic Regression for Modeling Contraceptive Use Among Women of Reproductive Age in Kenya
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 242-251
Received: Jun. 14, 2016;
Accepted: Jun. 24, 2016;
Published: Jul. 23, 2016
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Anthony Makau, Macroeconomic Statistics, Kenya National Bureau of Statistics, Nairobi, Kenya
Anthony G. Waititu, Department Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph K. Mung’atu, Department Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Contraceptive use is viewed as a safe and affordable way to halt rapid population growth and reduce maternal and infant mortality. Its use in Kenya remains a challenge despite the existence of family planning programmes initiated by the government and other stakeholders aimed at reducing fertility rate and increasing contraceptive use. This study aimed at modeling contraceptive use in Kenya among women of reproductive age using Multinomial logistic regression technique. A household based cross-sectional study was conducted between November 2008 and March 2009 by Kenya National Bureau of Statistics on women of reproductive age to determine the country’s Contraceptive Prevalence Rate and Total Fertility Rate among other indicators, whose results informed my data source. Multinomial logistic regression analysis was done in R version 3.2.1. statistical package. Modern method was the most preferred contraceptive method, of which Injectable, female sterilization and pills were the common types. Descriptive Analysis showed richest women aged between 30-34 years used modern contraceptives, while poorer women aged 35-39 years preferred traditional method. Multinomial Logistic Regression Analysis found marital status, Wealth category, Education level, place of Residence and the number of children a woman had as significant factors while age, religion and access to a health facility were insignificant. Simulation study showed that MLR parameters estimates converged to their true values while their standard errors reduced as sample size increased. Kolmogorov-Smirnov statistic of the MLR parameter estimates decreased while the P-value increased as the sample size increased and remained statistically insignificant. Marital status, Wealth category, Education level, place of Residence and the number of children a woman had could determine the contraceptive method a woman would choose, while age, religion and access to a health facility had no influence on the decision of choosing folkloric, traditional or modern method of contraception. MLR parameter estimates are consistent and normally distributed.
Anthony G. Waititu,
Joseph K. Mung’atu,
Multinomial Logistic Regression for Modeling Contraceptive Use Among Women of Reproductive Age in Kenya, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 4,
2016, pp. 242-251.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Kaunitz, A. (2008). Global library womens medicine. ISSN: 1756-2228. DOI: 10.3843/GLOWM.10374.
Barber, J. S., Axinn, W., and Thornton, A. (1999). Unwanted childbearing, health, and mother-child relationships. Journal of Health and Social Behavior, 40:(3), 231–257.
Ojakaa, D. (2008). The Fertility Transition in Kenya: Patterns and Determinants. Phd, University of Montreal, Montreal, Canada.
Mohammed, A., Woldeyohannes, D., Feleke, A., and Megabiaw, B. (2014). Determinants of modern contraceptiveutilization among married women of reproductive age group in North Shoa zone, Amhara region, Ethiopia. Reproductive Health, 11:(13), 1–7. doi 10.1186/1742–4755–11–13.
Nketiah-Amponsah, E., Arthur, E., and Abuosi, A. (2012). Contraceptive use among Ghanaian women. Reproductive Health, 16:(3), 154–169.
Kidayi, P., Msuya, S., Todd, J., Mtuya, C., Mtuy, T., and Mahande, M. (2015). Determinants of moderncontraceptive use among women of reproductive age in Tanzania: Evidence from Tanzania demographic and health survey data. Advances in Sexual Medicine, 15:(5), 43–52. http://dx.doi.org/10.4236/asm.2015.53006.
Ettarh, R. and Kyobutungi, C. (2012). Physical access to health facilities and contraceptive use in Kenya: Evidence from the 2008-2009 kenya demographic and health survey. African Journal of Reproductive Health, 16:(3), 47.
Czepiel, S. (2016). Maximum likelihood estimation of logistic regression models: theory and implementation.http://czep.net/stat/mlelr.pdf,15/01/2016.
Agresti, A. (2002). Categorical Data Analysis. 2nd edition, John Wiley and Sons, Inc, USA, p 267–273.
Agresti, A. (2007). An Introduction to Categorical Data. 2nd edition, John Wiley and Sons, Inc, USA, p 173–174.
Cameron, C. and Trivedi, P. (1998). Regression Analysis of Count Data. Cambridge University Press, New York.
Judge, G., Hill, R., and Griffiths, W. E. (1988). Introduction to the Theory and Practice of Economics. 2nd edition, John Wiley and Sons, USA.
McCullagh, P. and Nelder, J. (1989). Generalized linear models. 2nd edition, Chapman and Hall, London.
Mamunur, R. (2008). Inference on Logistic Regression Models. PhD, Graduate College of Bowling Green State University, Ohio, USA.