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. KolmogorovSmirnov
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.