Conjoint Use of Regression Analysis and Functional Measurement to Test Models of Combination of Factors Predicting Negative Attitude to Women
The present study was aimed at showing that by conjointly using two techniques that are rarely used in combination – regression analysis and functional measurement, it may be possible to rigorously tests models of combination of factors using data obtained in traditional multi-item/multi-scale surveys. The data used for this demonstration were taken from a large survey (N = 3,235) of Turkish students' attitude to women (ATW). As it included 12 types of predictors (e.g., age, geographic
... geographic location, score on collectivism scales), stepwise regression analysis was firstly used to select a subset of predictors. Three of them explained the major part of variance: biological gender (sex), level of political conservatism in the area (the place factor), and personal score on the vertical individualism scale (the culture factor). Secondly, continuous conservatism and collectivism scores were categorized into three levels and three factorial plots were created -- one for each level of support for conservatism -- with ATW on the y-axis, vertical collectivism on the x-axis, and biological sex on curves. Divergence of curves observed in all panels supported a multiplicative-type model of combination of the gender and culture factors. As a result, the model of combination of factors suggested was: Negative Attitude to Women = (Gender x Culture) + Place.