Aims and Scope
Journal of Behavioral Data Science is an international, peer-reviewed, free-to-publish, and open-access journal that aims to publish high quality, original research from researchers and practitioners in the area of data science and data analytics. The journal covers all the topics regarding data science, data analytics, and data analysis with the focus of the data science methods in humanity, education, behavioral and social science. Topics can include but are not limited to:
- Quantitative psychology, computational psychology, behavioral economics, quantitative sociology, and political behavior and opinion analysis.
- Data science, data analytics and machine learning in behavioral science
- Computational modeling of human behavior
- Predictive modeling of consumer behavior
- Data-driven decision making
- Social network analysis and computational social science
- Human-computer interaction and user experience
- Big data and data visualization in behavioral research
We divide the journal into the following 6 categories, which are not mutually exclusive.
Theory and Methods
This section contains articles on the development of methods and models for data analysis including new mathematical and statistical techniques and methods development.
Application and Case Studies
This section highlights the connection between data analysis techniques and its application to particular data in social, behavioral, education, physical, biological sciences, and related areas to deepen the substantive understanding of phenomena in one or more disciplines.
This section publishes papers explaining how to apply a particular method or technique.
This section publishes new algorithms to improve the efficiency of methods and techniques.
This section publishes papers regarding data analysis software and programs such as R functions, R packages, SAS macros, or standard-alone programs.
This section publishes original data collected.
Book and Software Reviews
This section publishes book and software reviews.