About the Journal

ISSN: 2575-8306 (Print)
ISSN: 2574-1284 (Online)
DOI: 10.35566/jbds

The Journal of Behavioral Data Science is a peer-reviewed, open-access journal that aims to provide a free-of-charge-to-publish platform for researchers and practitioners in the area of data science and data analytics. The journal is committed to making high-quality research freely accessible to authors and readers. Publishing in the journal and accessing its content are completely free for both authors and readers. This allows for the widest possible dissemination of research, and promotes interdisciplinary collaboration and innovation in the field of behavioral data science.

Current Issue

Vol. 3 No. 1 (2023): Volume 3 Number 1
  • Marvin, L., Liu, H., & Depaoli, S. (2023). Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories. Journal of Behavioral Data Science, 3(1), 1–33. https://doi.org/10.35566/jbds/v3n1/marvin
  • Ogasawara, H. (2023). On Some Known Derivations and New Ones for The Wishart Distribution: A Didactic. Journal of Behavioral Data Science, 3(1), 34–58. https://doi.org/10.35566/jbds/v3n1/ogasawara
  • Wyman, A., & Zhang, Z. (2023). API Face Value: Evaluating the Current Status and Potential of Emotion Detection Software in Emotional Deficit Interventions. Journal of Behavioral Data Science, 3(1), 59–69. https://doi.org/10.35566/jbds/v3n1/wyman
  • S, V. (2023). Predicting Dyslexia with Machine Learning: A Comprehensive Review of Feature Selection, Algorithms, and Evaluation Metrics. Journal of Behavioral Data Science, 3(1), 70–83. https://doi.org/10.35566/jbds/v3n1/s
  • McClure, K. (2023). Bayesian IRT in JAGS: A Tutorial. Journal of Behavioral Data Science, 3(1), 84–107. https://doi.org/10.35566/jbds/v3n1/mccure
DOI: https://doi.org/10.35566/jbds/v3n1
Published: 2022-12-19

Full Issue

Theory and Methods

  • Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

    Luca Marvin, Haiyan Liu, Sarah Depaoli (Author)
    1-33
    DOI: https://doi.org/10.35566/jbds/v3n1/marvin

  • On Some Known Derivations and New Ones for The Wishart Distribution: A Didactic

    Haruhiko Ogasawara (Author)
    34-58
    DOI: https://doi.org/10.35566/jbds/v3n1/ogasawara

Literature Review

Tutorials

  • Bayesian IRT in JAGS: A Tutorial

    Kenneth McClure (Author)
    84-107
    DOI: https://doi.org/10.35566/jbds/v3n1/mccure
View All Issues

JBDS is supported by the International Society for Data Science and Analytics (ISDSA; EIN: 82-4382236), an exempt organization under section 501(c)(3) of the Internal Revenue Code. You can make a tax-deductible contribution to help the growth of JBDS.