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Updated: Jul 10, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
Published on: May 31, 2019
A Latent Class Analysis (LCA) of problem gambling among a sample of community-recruited gamblers
Renee M Cunningham-Williams1, Song Iee Hong
1George Warren Brown School of Social Work, Washington University, St. Louis, Missouri 63130, USA. williamsr@wustl.edu
Abstract:
Problem gambling rates are relatively low (2%-4%), yet these gamblers experience multisystemic negative consequences, high comorbidity, and low treatment utilization. We aimed to characterize variations in gambling patterns to inform prevention and intervention efforts. Using community advertising, we recruited a diverse sample of lifetime gamblers (n = 312) for telephone interviews for a psychometric study of the newly developed Computerized-Gambling Assessment Module. Latent Class Analysis enumerated and classified gambling subgroups by distinctive gambling patterns, based on 8 composite scales functioning as validators of latent class membership (i.e., diagnostic gambling symptoms, reasons for gambling, gambling "withdrawal-like" symptoms, problem gambling perceptions, gambling venues, financial sources for gambling, gambling treatment/help-seeking, and religiosity/spirituality). Based on a distinguishing clustering pattern driven by 6 of 8 factors, we found a 6-class solution was the best-fitting solution. Gambling severity is most strongly characterized not only by symptomatology but also by the number of gambling treatment/help-seeking sources used.
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