JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS203EDI 10.6339/22-JDS203EDI Editorial Editorial: Data Science Meets Social Sciences EroshevaElena A.1 MinhasShahryar2 XuGongjun3 XuRanran.2.xu@uconn.edu4∗ 1Department of Statistics, School of Social Work, and the Center for Statistics and the Social Sciences, University of Washington, Seattle, WA 98195, USA 2Department of Political Science, Michigan State University, East Lansing, MI 48823, USA 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA 4Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA ∗Corresponding author. Email: ran.2.xu@uconn.edu. 20222172022203277278 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license. Anglin K, Boguslav A, Hall T (2022). Improving the science of annotation for natural language processing. Journal of Data Science, 20(3): 339–357. https://doi.org/10.6339/22-JDS1054. Bluhm B, Cutura J (2022). Econometrics at scale: Spark up big data in economics. Journal of Data Science. 20(3): 413–436. https://doi.org/10.6339/22-JDS1035. Lehoucq E (2022). Do Americans think the digital economy is fair? Using supervised learning to explore evaluations of predictive automation. Journal of Data Science, 20(3): 381–399. https://doi.org/10.6339/22-JDS1053. Ma C, Xu G (2021). Hypothesis testing for hierarchical structures in cognitive diagnosis models. Journal of Data Science, 20(3): 279–302. https://doi.org/10.6339/21-JDS1024. Mo Y, Habing B, Sedransk N (2022). Tree-based methods in educational research: A tool for modeling nonlinear complex relationships and generating new insights from data. Journal of Data Science, 20(3): 359–379. https://doi.org/10.6339/22-JDS1056. Sanders S, Gedara NIM, Walia B, Boudreaux C, Silverstein M (2022). Does aging make us grittier? Disentangling the age and generation effect on passion and perseverance. Journal of Data Science, 20(3): 401–411. https://doi.org/10.6339/22-JDS1041. Yang HC, Bradley JR (2021). Bayesian inference for spatial count data that may be over-dispersed or under-dispersed with application to the 2016 US presidential election. Journal of Data Science, 20(3): 325–337. https://doi.org/10.6339/21-JDS1032. Yang Y, Shang HL (2022). Is the group structure important in grouped functional time series? Journal of Data Science. 20(3): 303–324. https://doi.org/10.6339/21-JDS1031.