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Evaluating federated voluntary associations’ membership data: An application of Benford's Law

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  • Adam Chamberlain
  • Alixandra B. Yanus

Abstract

Objective Large, federated voluntary associations of the late 19th and early 20th centuries produced detailed reports of national meetings that included comprehensive data on state‐level membership. Though used in previous scholarship, the veracity of these data has never been evaluated. Methods We test 16 associations’ state‐level membership data, generally between 1880 and 1920, using Benford's Law, a mathematical principle used to discern whether data are potentially problematic—through error, fraud, or other reasons—and require further investigation. Results Our initial analyses reveal that these data deviate statistically from Benford's predictions. Further investigation, however, finds that these differences are not substantively significant; an aggregate measure of all associations shows close conformity to the Newcomb–Benford distribution. Conclusion The data presented in voluntary associations’ annual reports are likely trustworthy. These findings have important implications for future use of these data, other association data, and the role of associations in promoting civil society.

Suggested Citation

  • Adam Chamberlain & Alixandra B. Yanus, 2021. "Evaluating federated voluntary associations’ membership data: An application of Benford's Law," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1590-1601, July.
  • Handle: RePEc:bla:socsci:v:102:y:2021:i:4:p:1590-1601
    DOI: 10.1111/ssqu.13015
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    References listed on IDEAS

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    1. Adam Chamberlain & Alixandra B. Yanus, 2021. "Monuments as Mobilization? The United Daughters of the Confederacy and the Memorialization of the Lost Cause," Social Science Quarterly, Southwestern Social Science Association, vol. 102(1), pages 125-139, January.
    2. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    3. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
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