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The Lee-Campbell Research Group constructs, analyzes, and disseminates Big Social Science Data collections and an associated Scholarship of Discovery related largely to historical and contemporary China. Group members collaborate with James Z. Lee and Cameron D. Campbell on these projects and attend weekly group meetings to discuss their research. One distinctive feature of our group is our interdisciplinarity, with a diverse mix of social scientific historians and historical social scientists. Another distinctive feature of our group is our solidarity with many faculty group members who began as Lee-Campbell students and postdocs and now besides their own independent research continue to collaborate on major group projects. In addition, we also work closely with other professors, students, and research assistants outside our group on specific sub-projects.
Our main data projects include 1) the prize winning China Multi-Generational Panel Datasets CMGPD, 2) the China University Student Datasets CUSD, 3) the prize winning China Government Employee Dataset CGED-Qing and the CGED-ROC, 4) the China Rural Revolution Datasets CRRD-LR and CRRD-SQ, and 5) the China Professional Occupation Datasets CPOD. For a complete history of these projects, going back more than four decades, please see the 2020 retrospective by James Lee and Cameron Campbell in Historical Life Course Studies. These five data projects currently have individual level information for almost 9 million records for 1.7 million unique historical individuals, two-thirds from elite urban populations and one-third from rural peasant populations. Including several hundred thousand related individuals, these records provide individual-level information for almost 2 million persons who lived between the eighteenth century and the present. One million lived during the Qing dynasty. Another million are from the Republic of China and People’s Republic of China, almost entirely from the twentieth century. One-third, are from specific North and Northeast China rural populations, many of whom are longitudinally linked over their life course and across generations. The remaining two-thirds are almost entirely university educated or the historical equivalent, urban populations of government officials, professionals, and university students and faculty from all over China, many of whose records we have linked during the course of their education and careers, and for some within and across generations. Our group shares a strong commitment to making the non-proprietary portion of these data publicly available to interested researchers. We have already publicly released 4 of our almost 9 million records in two series of pubic releases. First, in 2010 and 2014, we publicly released 3 million longitudinally linked triennial / annual records for 368 thousand unique individuals from the China Multigenerational Panel Database-Liaoning (CMGPD-LN) and China Multigenerational Panel Database-Shuangcheng (CMGPD-SC), through the Inter-university Consortium for Political and Social Research. Second, beginning 2019, we have also publicly released nearly one million quarterly linked records for 77,874 Qing Dynasty officials who served either between 1850-1864 or between 1900-1912 from the China Government Employee Database – Qing (CGED-Q). We plan to release more than three million additional records of Qing officials from the CGED-Q in the years to come. For more details, including links to download the public releases, please visit our CGED-Q Project Page. We also plan to release the non-proprietary portions of the CGED-ROC, CUSD, CRRD, and CPOD datasets, once we have finished data construction and our own initial analyses of these data. Our best known publications, including five prize winning or CHOICE award books published by Harvard University Press, 北京三联出版社, MIT Press, and Stanford University Press, are based largely on a scholarship of discovery from our analyses of the above data. We have also published a number of articles using such data in many Chinese and English-language learned journals as well as such high impact international journals as American Sociological Review, Annual Review of Sociology, China Quarterly, Chinese Sociological Review, Demography, Evolution and Human Behavior, and Social Science and Medicine, which have awarded us eight additional best article or editors choice awards. For a preliminary summary of many of our research findings, please see Understanding China, 1700-2000: A Data Analytic Approach available both as a shorter Coursera MOOC and as a longer advanced undergraduate / beginning postgraduate on-line course. We use these course videos in our teaching at HKUST and elsewhere to develop flipped classroom approaches to train students to work together in groups rather than individually, to improve their oral and written communication skills as well as their critical thinking, and to develop their EQ and use and enhance their IQ. We have also started to share some of our experiences in historical dataset construction, data analysis, and data analytic teaching in a series of methodological articles published in such major Chinese language journals as 《历史研究》 (Historical Research), 《社会》 (Society), 《文史哲》 (Literature, History, Philosophy), and 《清华大学学报》 (Journal of Tsinghua University), The first of which won a 优秀青年成果奖 (Talented Young Scholar Achievement Award) from the Ministry of Education and the last of which won the Parkson Best Article Award from Tsinghua University, to encourage new efforts in construction, analysis, and teaching of Big Social Science Data from archival sources in China. For further information about our activities, please consult
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