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Data Resources for Sociology and Sociologists

Citation and Replication

Proper documentation of sources is a key component of scholarly research, and the need for documentation is no less important for data sources than for bibliographic sources. It is vital that scholars in the social sciences and elsewhere who use quantitative data in their work be clear and explicit as to the sources for their data. This is in part because of the importance of replication in the sciences - in principle, your analysis must be capable of being replicated by other scholars so as to better-assess the soundness of your work, which requires that those other scholars have access to the data used in your original analysis.

Greater transparency in sources also encourages greater accountability in research and increases confidence that the data used in your work are suited for the questions you are asking, which in turn will make others more confident in whatever conclusions your research presents. In addition, thorough documentation of data sources makes it easier for scholars to assess whether sources used in your research are appropriate for use in their own work.

More generally, the scholarly community is placing a growing emphasis on greater transparency in and documentation of data sources, to the point where some scholarly journals even require authors to submit their data to archives where others can download them. We have also included examples of data availability policies from different journals in different fields as examples of how different fields choose to operationalize disciplinary norms of transparency in empirical research. - The editors of the American Economic Association have put together a guide for citing secondary data that provides general guidelines, specific examples/scenarios, and explanations for why citation of data is importance for replication/reproducibility and greater rigor in empirical research. See for additional guidance on matters of transparency and replication in empirical work. - This guide will help you figure out how to cite your data in a way that is informative and useful to others. See and for additional information on and discussion of data citation, including its benefits for both collectors/producers of data and users of data. - This .pdf on data citation from the UK's Economic and Social Research Council is another guide to help you figure out how to cite your data in transparent and useful ways. - The American Sociological Review's submission guidelines, which refer to the American Sociological Association's ethical standards for data sharing. - Sociological Methods & Research's Data Availability Policy - an example of expectations of transparency in quantitative research in Sociology. - The American Sociological Association's Code of Ethics. See Section 12.5 for the ASA's ethical standards with regard to data sharing. - "Introduction to the Special Section on Replication and Data Access " - an introduction to a symposium in Sociological Methods & Research on replication of quantitative research in Sociology. - "More than Manuscripts: Reproducibility, Rigor, and Research Productivity in the Big Data Era" - a commentary in Toxiological Sciences that argues in favor of two things: (1) providing both data underlying publications and the code which transforms and analyzes those data to produce the results in those publications, and (2) treating data and code as citable contributions to science in their own right. The commentary was co-authored by Dr. Lance Waller in Emory's School of Public Health. - "A Replication Database for Economics and Social Sciences: The ReplicationWiki" - A commentary on the ReplicationWiki database of replication studies, which can be filtered by variables such as software used, keywords for the contents of the studies, and journals in which the studies were published. The database focuses largely on Economics, but it includes studies from other fields as well. - "Data Journalism at the Guardian: What Is It and How Do We Do It?" - The Guardian newspaper is a first-rate practitioner of data journalism and has much practical advice on how to do data journalism well. It also has a nice visualization of its workflow for prepping data for stories. Their key, and very valuable, insight: working with data is "80% perspiration, 10% great idea, 10% output".