Ölçek Geliştirmede Açımlayıcı Faktör Analizi, Karar Ağacı ve Yapay Sinir Ağları Sonuçlarının Karşılaştırılması
Comparison of Exploratory Factor Analysis, Decision Tree and Artificial Neural Network Results in Scale Development

Summary/Abstract: In this study, it was aimed to compare the resultss of exploratory factor analysis, artificial neural networks and decision trees in test development in terms of item selection and construct validity evidence. Within the scope of the research, data were collected by applying the "Statistical Attitude Scale" preform used in a previous study to individuals with undergraduate, graduate and alumni status. Decision regression tree analyses and self-organizing maps analyses, which can be used within the scope of data mining with exploratory factor analysis, were performed on the obtained data. After the analyses, it was seen that the number of dimensions and the distribution of the items to the dimensions could change in different methods. According to the comparison of fit indices obtained from confirmatory factor analysis, it was seen that the structure consisting of the items selected by exploratory factor analysis based on polychoric correlation and self-organizing mapping analysis was valid and the decision tree method was impractical in construct validity analyses.

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