Modelling Childbearing Desire: Comparison of Logistic Regression and Classification Tree Approaches

Abstract

Objectives: According to health surveys, population growth and total fertility rate (TFR) are decreasing in Iran. The economic and social factors in addition to the changing values and attitudes in the Iranian society have had a major impact on fertility decisions and the actions of families, especially women towards childbearing. This is an important issue for policymakers and many researchers in demography and public health thus the investigation of factors that affect low TFR is considered as a necessity. Materials and Methods: The classification and regression trees (CART) algorithm, as one of the most applicable classification trees, along with logistic regression was applied to model the tendency of 4898 women for childbearing in provinces with a TFR lower than the replacement level in Iran. The secondary data were then analysed by SPSS version 24.0. Results: Based on these two approaches, it was concluded that despite the CART algorithm, logistic regression suffers from some shortcomings including the difficult interpretation of three levels of interactions while not containing a specific method for handling the outliers. In addition, CART results demonstrated that women’s children ever born (CEB), age, and opinion had significant impacts on their desire to have a child. The groups encompassing "10-39-year-old women with CEB≤2" and "40-49-year-old women with positive attitudes towards childbearing" desired to have more children while "women with CEB ≥3" showed no tendency for childbearing. Conclusions: In general, the results revealed that adopting policies for changing women’s views on childbearing and creating the necessary resources for preventing the delays in marriage are regarded as important actions toward altering fertility rates. Another important conclusion is applying the CART algorithm as a convenient method for classifying demographical data.

Keywords

• fertility preferences
• child
• women
• decision trees
• logistic regression