In recent years, there has been a rise in Data Science. This multidisciplinary field combines the knowledge generated in the areas of Statistics/Mathematics with advances in Computer Science to provide solutions to existing problems in several areas of knowledge. In the field of Psychology, it is having an enormous impact. For example, the predictive capacity of scales is being improved, or short screening versions are being made by selecting the most discriminative items of these scales. Scales are also being used with a different purpose than the original one, assigning a weight to each item that reflects its importance in the new problem. These are just a few examples but there are many more, such as obtaining new predictors through new equipment and digital tools or applying new machine learning techniques to existing problems.
The objective of this collection is to bring together research papers that address current problems in the field of psychology through Data Science. We are interested in papers that focus on improving one or both elements involved in a data analysis problem. That is, proposing new predictors that are more discriminative than existing ones, or the novel application of Data Science techniques to problems that have not been previously applied and that provide significant results. Possible areas of application include, among other, psychopathology, developmental psychology, educational psychology, addictive behaviors, labor psychology, or personality psychology.
This collection searches for interesting applications of data science in the field of Psychology. Concisely, we are interested in:
New predictors obtained, for example, through:
• Computer vision
• Eye trackers
• Virtual Reality
• Movement Recognition
• EEG devices
• Electronic Health Record
Application of supervised techniques in both classification and regression to relevant problems in the field of Psychology. Some of these techniques are:
• Advanced Statistical Techniques
• Neural Networks
• Kernel methods
• Ensemble methods (Gradient Boosting)
• Tree-based techniques (Decision Trees, Random Forest)
• Discriminants
• Bayesian methods
Application of Unsupervised techniques in solving current problems in the field of Psychology. These include, but are not limited to:
• Autoencoders
• Principal and Independent Components
• Factor Analysis,
• Item Response Theory
• Clustering
In recent years, there has been a rise in Data Science. This multidisciplinary field combines the knowledge generated in the areas of Statistics/Mathematics with advances in Computer Science to provide solutions to existing problems in several areas of knowledge. In the field of Psychology, it is having an enormous impact. For example, the predictive capacity of scales is being improved, or short screening versions are being made by selecting the most discriminative items of these scales. Scales are also being used with a different purpose than the original one, assigning a weight to each item that reflects its importance in the new problem. These are just a few examples but there are many more, such as obtaining new predictors through new equipment and digital tools or applying new machine learning techniques to existing problems.
The objective of this collection is to bring together research papers that address current problems in the field of psychology through Data Science. We are interested in papers that focus on improving one or both elements involved in a data analysis problem. That is, proposing new predictors that are more discriminative than existing ones, or the novel application of Data Science techniques to problems that have not been previously applied and that provide significant results. Possible areas of application include, among other, psychopathology, developmental psychology, educational psychology, addictive behaviors, labor psychology, or personality psychology.
This collection searches for interesting applications of data science in the field of Psychology. Concisely, we are interested in:
New predictors obtained, for example, through:
• Computer vision
• Eye trackers
• Virtual Reality
• Movement Recognition
• EEG devices
• Electronic Health Record
Application of supervised techniques in both classification and regression to relevant problems in the field of Psychology. Some of these techniques are:
• Advanced Statistical Techniques
• Neural Networks
• Kernel methods
• Ensemble methods (Gradient Boosting)
• Tree-based techniques (Decision Trees, Random Forest)
• Discriminants
• Bayesian methods
Application of Unsupervised techniques in solving current problems in the field of Psychology. These include, but are not limited to:
• Autoencoders
• Principal and Independent Components
• Factor Analysis,
• Item Response Theory
• Clustering