(Sage Publications Ltd, 2016) N/A; N/A; Şen, Celile Katrine Naivar; Kumkale, Gökçe Tarcan; PhD Student; PhD Student; Graduate School of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; N/A; 105011
With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logistic regression analysis. Our findings suggest that incorporating personality and religiousness attributes may increase non-attender identification. Furthermore, the simple profile generated by decision trees provides a clear map useful for intervention planning.
