Data Mining in the Mixed Methods: Application to the Study of the Psychological Profiles of Athletes - Pastrana, José L.; Reigal, Rafael E.; Morales-Sánchez, Verónica; Morillo-Baro, Juan P.; Mier, Rocío Juárez-Ruiz de; Alves, José; Hernández-Mendo, Antonio (Frontiers in Psychology, 2019)
Data mining is seen as a set of techniques and technologies allowing to extract, automatically or semi-automatically, a lot of useful information, models, and tendencies from a big set of data. Techniques like “clustering,” “classification,” “association,” and “regression”; statistics and Bayesian calculations; or intelligent artificial algorithms like neural networks will be used to extract patterns from data, and the main goal to achieve those patterns will be to explain and to predict their behavior. So, data are the source that becomes relevant information. Research data are gathered as numbers (quantitative data) as well as symbolic values (qualitative data). Useful knowledge is extracted (mined) from a huge amount of data. Such kind of knowledge will allow setting relationships among attributes or data sets, clustering similar data, classifying attribute relationships, and showing information that could be hidden or lost in a vast quantity of data when data mining is not used. Combination of quantitative and qualitative data is the essence of mixed methods: on one hand, a coherent integration of result data interpretation starting from separate analysis, and on the other hand, making data transformation from qualitative to quantitative and 1 vice versa. A study developed shows how data mining techniques can be a very interesting complement to mixed methods, because such techniques can work with qualitative and quantitative data together, obtaining numeric analysis from qualitative data based on Bayesian probability calculation or transforming quantitative into qualitative data using discretization techniques. As a study case, the Psychological Inventory of Sports Performance (IPED) has been mined and decision trees have been developed in order to check any relationships among the “Self-confidence” (AC), “Negative Coping Control” (CAN), “Attention Control” (CAT), “Visuoimaginative Control” (CVI), “Motivational Level” (NM), “Positive Coping Control” (CAP), and “Attitudinal Control” (CACT) factors against gender and age of athletes. These decision trees can also be used for future data predictions or assumptions.