Archives

  • Newly accepted
    Vol. 4 No. 1 (2024)

    Newly accepted articles

  • Volume 3 Number 1
    Vol. 3 No. 1 (2023)

    • 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

  • Volume 3 Number 2
    Vol. 3 No. 2 (2023)

  • Volume 2 Number 2
    Vol. 2 No. 2 (2022)

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    • Zhang, T., Tong, X., & Zhou, J. (2022). Disentangling the Influence of Data Contamination in Growth Curve Modeling: A Median Based Bayesian Approach. Journal of Behavioral Data Science, 2(2), 1–22. https://doi.org/10.35566/jbds/v2n2/p1
    • Liu, H. ., Qu, W., Zhang, Z., & Wu, H. (2022). A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter. Journal of Behavioral Data Science, 2(2), 23–46. https://doi.org/10.35566/jbds/v2n2/p2
    • Du, H., Ke, Z., Jiang, G., & Huang, S. (2022). The Performances of Gelman-Rubin and Geweke’s Convergence Diagnostics of Monte Carlo Markov Chains in Bayesian Analysis. Journal of Behavioral Data Science, 2(2), 47–72. https://doi.org/10.35566/jbds/v2n2/p3
    • Suzuki, H., & Gonzalez, O. (2022). Relative Predictive Performance of Treatments of Ordinal Outcome Variables across Machine Learning Algorithms and Class Distributions. Journal of Behavioral Data Science, 2(2), 73–98. https://doi.org/10.35566/jbds/v2n2/suzuki
    • Xu, Z. (2022). Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS. Journal of Behavioral Data Science, 2(2), 99–126. https://doi.org/10.35566/jbds/v2n2/xu
    • Qiu, M. (2022). A Tutorial on Bayesian Latent Class Analysis Using JAGS. Journal of Behavioral Data Science, 2(2), 127–155. https://doi.org/10.35566/jbds/v2n2/qiu
    • Shao, S. (2022). A Tutorial on Bayesian Analysis of Count Data Using JAGS. Journal of Behavioral Data Science, 2(2), 156–173. https://doi.org/10.35566/jbds/v2n2/shao

  • Volume 2 Number 1
    Vol. 2 No. 1 (2022)

    This issue includes 6 articles.

    • Borsboom, D., Blanken, T., Dablander, F., van Harreveld, F., Tanis, C., & Van Mieghem, P. (2022). The Lighting of the BECONs: A Behavioral Data Science Approach to Tracking Interventions in COVID-19 Research. Journal of Behavioral Data Science, 2(1), 1–34. https://doi.org/10.35566/jbds/v2n1/p1
    • Lu, L., & Zhang, Z. (2022). How to Select the Best Fit Model among Bayesian Latent Growth Models for Complex Data. Journal of Behavioral Data Science, 2(1), 35–58. https://doi.org/10.35566/jbds/v2n1/p2
    • Jacobucci, R., & Li, X. (2022). Does Minority Case Sampling Improve Performance with Imbalanced Outcomes in Psychological Research?. Journal of Behavioral Data Science, 2(1), 59–74. https://doi.org/10.35566/jbds/v2n1/p3
    • Marcoulides, K., Quan, J., & Wright, E. (2022). The Impact of Sample Size on Exchangeability in the Bayesian Synthesis Approach to Data Fusion. Journal of Behavioral Data Science, 2(1), 75–105. https://doi.org/10.35566/jbds/v2n1/p5
    • Waggoner, P., & Kennedy, R. (2022). The Role of Personality in Trust in Public Policy Automation. Journal of Behavioral Data Science, 2(1), 106–123. https://doi.org/10.35566/jbds/v2n1/p4/
    • Sales Rodrigues, K. A. (2022). Book Review: An Introduction to Nonparametric Statistics. Journal of Behavioral Data Science, 2(1), 124–127. https://doi.org/10.35566/jbds/v2n1/p8

  • Vol. 1 No. 2 (2021)

    This issue includes 7 articles.

    • Lu, Z. (Laura), & Zhang, Z. (2021). Bayesian Approach to Non-ignorable Missingness in Latent Growth Models. Journal of Behavioral Data Science, 1(2), 1–30. https://doi.org/10.35566/jbds/v1n2/p1
    • Serang, S., & Sears, J. (2021). Tree-based Matching on Structural Equation Model Parameters. Journal of Behavioral Data Science, 1(2), 31–53. https://doi.org/10.35566/jbds/v1n2/p3
    • Liu, J., Kang, L., Sabo, R. T., Kirkpatrick, R. M., & Perera, R. A. (2021). Two-step growth mixture model to examine heterogeneity in nonlinear trajectories. Journal of Behavioral Data Science, 1(2), 54–88. https://doi.org/10.35566/jbds/v1n2/p4
    • Luo, W., & Lai, H. C. (2021). A Weighted Residual Bootstrap Method for Multilevel Modeling with Sampling Weights. Journal of Behavioral Data Science, 1(2), 89–118. https://doi.org/10.35566/jbds/v1n2/p6
    • Zhang, Z. (2021). A Note on Wishart and Inverse Wishart Priors for Covariance Matrix. Journal of Behavioral Data Science, 1(2), 119–126. https://doi.org/10.35566/jbds/v1n2/p2
    • Zhou, S., Li, Y., Chi, G., Yin, J., Oravecz, Z., Bodovski, Y., Friedman, N. P., Vrieze, S. I., & Chow, S.-M. (2021). GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. Journal of Behavioral Data Science, 1(2), 127–155. https://doi.org/10.35566/jbds/v1n2/p5
    • Cain, M. (2021). Structural Equation Modeling using Stata. Journal of Behavioral Data Science, 1(2), 156–177. https://doi.org/10.35566/jbds/v1n2/p7

  • Vol. 1 No. 1 (2021)

    The issue has 8 articles.

    • Zhang, Z., & Zhang, D. (2021). What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula. Journal of Behavioral Data Science, 1(1), 1–16. https://doi.org/10.35566/jbds/v1n1/p1
    • Manjunath, B. G., & Wilhelm, S. (2021). Moments Calculation for the Doubly Truncated Multivariate Normal Density. Journal of Behavioral Data Science, 1(1), 17–33. https://doi.org/10.35566/jbds/v1n1/p2
    • Liu, H., & Zhang, Z. (2021). Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship. Journal of Behavioral Data Science, 1(1), 34–52. https://doi.org/10.35566/jbds/v1n1/p3
    • Tong, X. (2021). Semiparametric Bayesian Methods in Growth Curve Modeling for Nonnormal Data Analysis. Journal of Behavioral Data Science, 1(1), 53–84. https://doi.org/10.35566/jbds/v1n1/p4
    • Christensen, A. P., & Golino, H. (2021). Factor or Network Model? Predictions From Neural Networks. Journal of Behavioral Data Science, 1(1), 85–126. https://doi.org/10.35566/jbds/v1n1/p5
    • Rodgers, D. M., Jacobucci, R., & Grimm, K. J. (2021). A Multiple Imputation Approach for Handling Missing Data in Classification and Regression Trees. Journal of Behavioral Data Science, 1(1), 127–153. https://doi.org/10.35566/jbds/v1n1/p6
    • Sukumaran, R., Patwa, P., V, S. T., Shankar, S., Kanaparti, R., Bae, J., Mathur, Y., Singh, A., Chopra, A., Kang, M., Ramaswamy, P., & Raskar, R. (2021). COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms. Journal of Behavioral Data Science, 1(1), 154–169. https://doi.org/10.35566/jbds/v1n1/p8
    • Rodrigues, K. A. S. (2021). Book Review: Mastering Software Development in R. Journal of Behavioral Data Science, 1(1), 170–172. https://doi.org/10.35566/jbds/v1n1/p7