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 data, patterns and trends from a large set of data. Techniques like clustering, classification, association and regression, statistics and Bayesian calculations or using intelligent artificial algorithm like neural networks will be used to extract patterns from data and the main goal to achieve getting those patterns will be to explain and to predict its behavior. So data is the source that becomes into relevant information. Researching data are gathered as number (quantitative data) and also as symbolic values (qualitative data). Useful knowledge is extracted (mined) from a huge amount of data. A such kind of knowledge that will allow to set relationships among attributes or data sets, clustering similar data, classifying attribute relationships and showing information that could be hidden or lost in a so 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 coherently integration of result data interpretation starting from separated analysis; and on the other hand, making data transformation from qualitative to quantitative and vice versa. Study developed shows how data mining techniques can be a great and very interesting complement to mixed methods, because those techniques can work with qualitative and quantitative data together, getting numeric analysis from qualitative data based on Bayesian probability calculation or transforming quantitative into qualitative using discretization techniques. As 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.