Abstract :
Children sex preference may have significant effects on fertility behavior, which is an influential component of population dynamics and could control the population size, structure, and composition. The main objective of this study is to investigate affecting factors on Iranian women’s child sex preference through applying Classification and Regression Trees algorithm, which is an effective and easy to interpret non-parametric classification method.
Methods: A cross-sectional study was conducted to collect demographical data of 1250 Iranian women aged 15-49. To classify child sex preference for children, age, educational level, place of residence, and number of siblings for women, were nominated as predictors using the SPSS-22 statistical software.
Results: Women's age, educational level and number of siblings were remained in extracted decision tree. The validity of the resulted tree was confirmed by 0.71 accuracy, which means 71% of women’s sex preference, has been classified correctly.
Conclusions: The most important determinant of women’s child sex preference was age. It could be concluded that educated Iranian women in different age cohorts are in favor of having girls.
Keywords :
Decision trees , Child Women , Fertility preferences , Sex differences