Editorial: Parsing Psychology: Statistical and Computational Methods Using Physiological, Behavioral, Social, and Cognitive Data
Jason C. Immekus, Pietro Cipresso · Frontiers in Psychology · 2019
Advancements in statistical and computational methods have much to contribute to promoting new discoveries in psychology.In particular, as found in diverse disciplines as marketing, health care, and medicine, machine learning and big data offer similarly unique potential to inform, test, and advance our knowledge of mental processes, brain functioning, and behavior in ways that were not previously possible.Psychological data science represents the integration of data science into psychology to explore the abundant information provided by physiological, behavioral, social, and cognitive data alone and together.The merging of psychology and data science is due to a host of factors, including, for example, more readily accessible data, cheaper and more powerful computational processing, and larger data storage capacity.As a result, social science researchers are well-positioned in their ability to incorporate data science into their work to advance and promote our understanding of key factors associated with important psychological outcomes (e.g., mental health).This Research Topic represents a collection of papers that demonstrate the use, or potential, of psychological data science to pursue both hypothesis-driven experiments and bottom-up data exploration through modern statistical and computational models.As these studies illustrate, machine learning algorithms are already expanding our knowledge of key factors associated with designated outcomes (e.g., academic and brain-based disorders) or have the potential to build on existing and emerging areas of research (e.g., scale development).Broadly, the papers comprising this Research Topic fall into three general areas as related to the application of data science in psychological and psychometric research, including: Supervised and Unsupervised Machine Learning Computing, Contemporary Psychometrics, and Emerging Areas of Research. SUPERVISED AND UNSUPERVISED MACHINE LEARNING COMPUTINGMachine learning represents a branch of artificial intelligence in which statistical model building is based on automatically implemented algorithms to identify data patterns for classification and