Akademik Veri Yönetim Sistemi
2nd BigSurv Conference (BigSurv20), 6 Kasım - 04 Aralık 2020
Dr Ayse Abbasoglu Ozgoren (Hacettepe University Institute of Population Studies) - Presenting Author
Dr Anil Boz Semerci (Hacettepe University)
Dr Duygu Icen (Hacettepe University)
This study aims to focus on opportunities provided by data mining methods in social sciences with an application of data mining method, namely decision trees, to a conventional data source of demography, which is demographic and health survey. We plan to analyze attitudes of women towards gender roles and domestic violence in Turkey by employing both decision trees and classical logistic regression methodologies using the most recent Turkey Demographic and Health Survey (TDHS), 2018 TDHS, and compare the findings. The reason to choose the context of Turkey is twofold. First, Turkey is a unique context in terms of recent changes in norms within its demographic realm. In general, basic cultural norms change slowly and there has been a transformation from “traditional pro-fertility norms” to “individual-choice norms” in advanced industrial societies. According to TDHS data, the recent development in Turkey is contrary to this development, where fertility ideals are moving towards a pro-fertility norm, but gender norms are becoming more egalitarian and intolerance against domestic violence among women is on the rise. Hence, beforehand, Turkey presents an interesting case within its cultural context. Second, although there is a tendency to use the attitude variables as explanatory ones in population studies in Turkey, little has been done to analyze these variables as dependent variables. Contrary to this evidence, understanding and studying transitions in gender roles is important because traditional commitment to gender roles leads to (re) production of gender inequalities in the life course and egalitarian commitments may lead to deproduction of these inequalities. This paper aims to contribute to such perspective by having ideals and attitudes as dependent or outcome variables in population studies related to Turkey. This study relies on the necessity to study these recent developments in norms in Turkey employing techniques of data mining methods and data visualizations, namely decision trees. In other words, we aim to find out the profiles and attributes of women whose (i) attitudes towards gender roles are more egalitarian, and (ii) attitudes against domestic violence are more intolerant, using 2018 TDHS data. 2018 TDHS is the most recent TDHS with completed interviews with 7,346 women age 15-49 from 11,056 households. This study employs decision trees, which are the most popular classification algorithms being used in data mining and machine learning problems. Decision trees allow developing classification systems that predict or classify future observations based on a set of decision rules. Several algorithms are used in order to generate trees such as, C5.0, Classification and regression tree (C&RT), Quick, Unbiased, Efficient Statistical Tree (QUEST) and Chi-squared Automatic Interaction Detection (CHAID). Our covariates are age group, region, urban rural place of residence, wealth level, children ever born, marital status, mother literacy, father literacy, mother tongue, employment status and educational level. We compare our findings with classical logistic regression analyses results as well. Finally, based on our profiling analyses, we conclude and speculate on possible scenarios for the post-transitional stage of the demographic transition in Turkey.