Abstract:
Family planning services enable individuals or couples to determine freely the number and spacing of their children and to select the means by which it is achieved. Population growth in Ethiopia is not in parallel with the development of health services and other basic infrastructures. The objective of this study was to identify the factors that affect utilization of family planning service among reproductive age women‟s in Ethiopia. Cross-sectional data from Ethiopian Demographic and Health Survey was used for the analysis. Data was collected by the Central Statistical Agency from January 18, 2016 through June 27, 2016 and the sampling technique employed was multistage. A total of 9824 women were considered in this study. Descriptive analysis, single level, multilevel and Bayesian logistic regression were used for data analysis using socio-economic, demographic, and proximate variables and utilization of family planning service as the dependent variable. The results of the study show that, out of a total of 9824 sampled women 35.83 percent used the family planning services while 64.17 percent did not. The single level, multilevel and Bayesian logistic regression analyses revealed that the variables that affect the women‟s utilization of family planning service in Ethiopia were place of residence, age of a woman, religion of a woman, visited by family planning worker, educational level of women, economic status, knowledge about family planning method, occupation of women, exposure to mass media, husband education level, husband occupation and number of having children. The multilevel logistic regression analysis revealed that there was significant variation with regard to women‟s utilization of family planning services across the regions under investigation. From the methodological aspect, it was found that random coefficient model is better compared to the other two models in setting the data well. The results obtained by applying Bayesian logistic regression analysis show that the standard errors for the variables incorporated in the model were smaller than the classical logistic regression analysis. This implies that the Bayesian logistic regression model give a better estimation than the classical approach.
