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.

The average citation per article is 9.1 since the debut of the journal based on Google Scholar. JBDS is indexed by Scopus with the 2025 CiteScore of 4.1 (Q1 Statistics and Probability). The current CiteScoreTracker for 2026 is 5.9.

Current Issue

Vol. 6 No. 2 (2026): Volume 6, Number 2
View Vol. 6 No. 2 (2026): Volume 6, Number 2

Wang, L., Yip, T., Fang, Y., Li, R., Lorenzo, K., Park, I. J. K., Valentino, K., Cruz-Gonzalez, M., Zhen-Duan, J., Alvarez, K., & Alegría, M. (2026). Careless Responding in Daily Diary Research: Detection and Impact on Intensive Longitudinal Data Analyses. Journal of Behavioral Data Science, 6(2), 1-24. https://doi.org/10.35566/jbds/wang2026

Kim, H., Qi, J., Feng, Z., Zhang, X., Han, Y., He, J., & Ji, F. (2026). Can Large Language Models (LLMs) be Trusted for Power Analysis? An Empirical Evaluation. Journal of Behavioral Data Science, 6(2), 173-184. https://doi.org/10.35566/jbds/jikim

Gomer, B., Lee, H. B., & Kim, Y. M. (2026). Mosaic Monte Carlo: A New Method of Simulation Design to Improve the Generalizability of Findings. Journal of Behavioral Data Science, 6(2), 67-133. https://doi.org/10.35566/jbds/gomer2026

Bain, C., Manapat, P. D., Manapat, D., Brenna, K., & Grimm, K. (2026). When DIF Goes Unmodeled: Assessing the Viability of Random Forest for Diagnostic Classification. Journal of Behavioral Data Science, 6(2), 134-172. https://doi.org/10.35566/jbds/bainmmbg

Jeon, B., & Kim, Y. (2026). Difference-in-Differences and Treatment x Pretest Interactions: A Critique of Dual-Centered ANCOVA. Journal of Behavioral Data Science, 6(2), 1-12. https://doi.org/10.35566/jbds0208

Minukuri, A. R. (2026). Adaptive Real-Time Churn Prediction in Telecommunications Using Sequential Learning Models: A Dynamic Approach. Journal of Behavioral Data Science, 6(2), 185-205. https://doi.org/10.35566/jbds/minukuri2026

 

DOI: https://doi.org/10.35566/v6n2
Published: 2026-09-25

Theory and Methods

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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.