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Abstract Background Women’s education is the base for faster economic growth, longer life expectancy, lower population growth, improved quality of life, and a high rate of investment return in developing countries. Historically, girls were denied opportunities for schooling in most of the regions and societies of Ethiopia. So this study targeted a multilevel analysis of women’s education in Ethiopia using the 2016 Ethiopian Demographic and Health Survey data. Methods Secondary data on women’s data sets were obtained from the 2016 Ethiopia Demographic and Health Survey. A population-based cross-sectional study design was used for the survey. The sampling technique used for the survey was the two-stage sampling technique, which is stratified in the first stage and equal probability systematic selection technique in the second stage. The multi-level ordinal logistic regression model was fitted to identify the determinants of women’s education in Ethiopia. Results Among the random sample of 17137 women, the majority, 65.6 percent were rural residents. Somali regional state (75.3 percent) and the capital city Addis Ababa (8.6 percent) had the highest and lowest percentages of women illiteracy respectively than the remaining administrative units of Ethiopia. The minimum values for the fit statistics and the indicative value of the intra-class correlation (68.3%) of the multilevel model showed its appropriateness to the data. Among the predictors in the final multilevel ordinal logistic regression analysis, women’s age at first marriage, residence, and family’s wealth index were significant predictors of women’s education in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more variation in women’s education between the enumeration areas than within the enumeration areas. Conclusion A multi-level ordinal logistic regression analysis has determined higher-level differences in women's education that could not be addressed by a single-level approach. So, the application of standard models by ignoring this variation ought to embrace spurious results, then for such hierarchical data, multilevel modeling is recommended. |
