![]() However, in many disciplines, particularly in psychology and social sciences, statistical modeling for explanation is the predominant, if not the exclusive approach. In the second approach, the data-generating process is unknown, and researchers are interested in finding an algorithm capable of recognizing different patterns hidden in data, which then gives the best prediction for the output values through the input values of new observations ( Shmueli, 2010). One uses an explanation-oriented approach to science, the explanatory modeling that Breiman (2001) defines as “data modeling culture.” The other uses a prediction-oriented approach, defined by Breiman as “algorithmic modeling culture.” In the former approach, data is assumed to be drawn from a given stochastic model, researchers are interested in testing the hypothesized “true” relationship between two or more variables and the mechanisms governing their intercorrelation, and the main objective is to reproduce model parameters using statistical inference and to improve the explanatory power of models. Statistical modeling is traditionally separated into two different cultures. At the same time, it reduced the redundancy of the items and eliminated those with less consistency. Results show that the machine learning procedure selected a set of items that drastically improved the prediction accuracy of the model (167 items reached a prediction accuracy of 88.5%, that is 25.6% of incorrectly classified), compared to the predictions obtained using all the original items (260 items with a prediction accuracy of 74.4%). The proposed procedure is based on a sequence of steps that allows the construction of an ANN capable of predicting the diagnosis of a group of subjects based on their item responses to a questionnaire and subsequently automatically selects the most predictive items by preserving the factorial structure of the scale. Such approach helps to construct a diagnostic tool that both conforms with the theory and with the individual characteristics of the population at hand, by providing insights on the power of the scale in precisely identifying out-of-sample pathological subjects. At the same time, it permits the selection of those items that have the most relevance in terms of prediction by therefore considering the relationship of the items with the actual psychopathological diagnosis. ![]() Such blending allows deriving theoretical insights on the characteristics of the items selected and their conformity with the theoretical framework of reference. It presents the methodology and the results of a procedure for items selection that considers both the explanatory power of the theory and the predictive power of modern computational techniques, namely exploratory data analysis for investigating the dimensional structure and artificial neural networks (ANNs) for predicting the psychopathological diagnosis of clinical subjects. This paper presents a procedure that aims to combine explanatory and predictive modeling for the construction of new psychometric questionnaires based on psychological and neuroscientific theoretical grounding. 4SiPGI Postgraduate School in Gestalt Integrated Psychotherapy, Torre Annunziata, Italy.3Department of Neuroscience and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.2Department of Humanistic Studies, University of Naples Federico II, Naples, Italy.1Department of Public Health, University of Naples Federico II, Naples, Italy.Pasquale Dolce 1 Davide Marocco 2* Mauro Nelson Maldonato 3 Raffaele Sperandeo 4
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