An Empirical Assessment of Data Sharing and Computational Reproducibility in Sociology
Public sharing of data and analysis code enables independent verication of published ndings, yet systematic evidence on such sharing in sociology remains scarce, even as audits have been conducted in psychology, economics, and political science. We examine 730 empirical articles published between January 2019 and June 2024 in three leading general-interest journals: American Sociological Review, American Journal of Sociology, and Social Forces. For each article, we record whether a publicly accessible replication package is provided, and for those that share materials, we attempt to computationally reproduce the main-text tables and gures. Across the sample, 9.9% of articles provide replication packages, with substantial variation across journals (5.2% to 22.9%) and research types (0% for qualitative studies, 30% for experiments). These rates fall well below those reported for political science and economics. Among 72 packages examined in detail, more than half cannot be veried due to missing or incomplete materials. At the same time, no article is wholly non-reproducible, and 22% are fully or largely reproducible. Sociology is not unusually prone to errors when materials are shared and runnable—it simply shares them far less often. We discuss implications for journal policies and for transparency standards that re ect the eld’s methodological diversity.