About this Research Topic
Contemporary data analysis can be used to integrate several measures more than ever. Psychology is hugely pressed to integrate many channels conveying the idea that physiological, behavioral, social, and cognitive data need to be understood both alone and together.
Psychological data science is becoming more popular thanks to computational and statistical techniques and paradigms enacting analyses based on data with or without specific hypotheses.
The aim of this Research Topic is to shed new light on psychological data science with both hypothesis driven experiments and bottom up data exploration by using supervised and unsupervised machine learning computing, statistical and computational models, and new possible ways to parse data, highlighting their importance in psychology for both mental health and disease.
This Research Topic is open to any methodological paradigm and platform or other useful ways to highlight the emergence of psychological information. However, the call is centered on data, and while the description of pure philosophic paradigm is appreciated, authors should focus primarily on data and their treatment, analysis, and manipulation.
Keywords: Psychometrics, Computational, Statistics, Physiology, Psychophysiology, Behavioral, Cognitive, Emotions, Computational Psychometrics, Machine learning, Classification, Regression, Mathematical Psychology
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