Knowledge discovery and data mining in psychology: Using decision trees to predict the Sensation Seeking Scale score

Knowledge discovery from data is an interdisciplinary research field combining technology and knowledge from domains of statistics, databases, machine learning and artificial intelligence. Data mining is the most important part of knowledge discovery process. The objective of this paper is twofold. The first objective is to point out the qualitative shift in research methodology due to evolving knowledge discovery technology. The second objective is to introduce the technique of decision trees to psychological domain experts. We illustrate the utility of the decision trees on the prediction model of sensation seeking. Prediction of the Zuckerman's Sensation Seeking Scale (SSS-V) score was based on the bundle of Eysenck's personality traits and Pavlovian temperament properties. Predictors were operationalized on the basis of Eysenck Personality Questionnaire (EPQ) and Slovenian adaptation of the Pavlovian Temperament Survey (SVTP). The standard statistical technique of multiple regression was used as a baseline method to evaluate the decision trees methodology. The multiple regression model was the most accurate model in terms of predictive accuracy. However, the decision trees could serve as a powerful general method for initial exploratory data analysis, data visualization and knowledge discovery