The Transparency Project

Division on Addiction, The Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

Available Datasets

Title: Associations between national gambling policies and disordered gambling prevalence rates within Europe

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching hospital of Harvard Medical School; University of St.Gallen HSG

PI(s): Simon Planzer, Ph.D., M.A

Sponsor(s): bwin, Digital Entertainment

Description: BACKGROUND: This codebook provides information for both the raw and analytic datasets used to generate analyses of the associations between national gambling policies and disordered gambling prevalence rates within Europe (Planzer, Gray, & Shaffer, 2014). These datasets come from the collection of national gambling policy data from key informants and the collection of disordered gambling prevalence estimates from a review of the literature.

Download the codebook

Download the dataset

See related publication(s)

Title: Using Cross-game Behavioral Markers for Early Identification of High-risk Internet Gamblers

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: BACKGROUND: Using actual gambling behavior provides the opportunity to develop behavioral markers that operators can use to predict the development of gambling-related problems among their subscribers. METHODS: Participants were 4,056 Internet gamblers who subscribed to the Internet betting service provider bwin.party. Half of this sample included multiple platform gamblers who were identified by bwin.party’s Responsible Gambling (RG) program; the other half were controls randomly selected from those who had the same first deposit date. Using the daily aggregated Internet betting transactions for gamblers’ first 31 calendar days of online betting activities at bwin.party, we employed a 2-step analytic strategy: (1) applying an exploratory chi-squared automatic interaction detection (CHAID) decision tree method to identify characteristics that distinguished a subgroup of high-risk Internet gamblers from the rest of the sample, and (2) conducting a confirmatory analysis of those characteristics among an independent validation sample. RESULTS: This analysis identified two high-risk groups (i.e., groups in which 90% of the members were identified by bwin.party’s RG program): Group 1 engaged in 3 or more gambling activities and evidenced high wager variability on casino-type games; Group 2 engaged in 2 different gambling activities and evidenced high variability for live action wagers. CONCLUSION: This analysis advances an ongoing research program to identify potentially problematic Internet gamblers during the earliest stages of their Internet gambling. Gambling providers and public policy makers can use these results to inform early intervention programs that target high-risk Internet gamblers.

Download the codebook

Download the dataset

See related publication(s)

Title: Behavioral characteristics of Internet gamblers who trigger corporate responsible gambling interventions

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: As the worldwide popularity of Internet gambling increases, concerns about the potential for gambling-related harm also increase. This paper reports the results of a study examining actual Internet gambling behavior during 10 years of play. We examined the electronic gambling records of subscribers (N=2,066) who triggered a responsible gaming alert system at a large international online gaming company. We compared these cases with control subscribers (N=2,066) who had the same amount of exposure to the Internet gambling service provider. We used discriminant function analysis to explore what aspects of gambling behavior distinguish cases from controls. Indices of the intensity of gambling activity (e.g., total number of bets made, number of bets per betting day) best distinguished cases from controls, particularly in the case of live-action sports betting. Control group players evidenced behavior similar to the population of players using this service. These results add to our understanding of behavioral markers for disordered Internet gambling and will aid in the development of behavior-based algorithms capable of predicting the presence and/or the onset of disordered Internet gambling.

Download the codebook

Download the dataset

See related publication(s)

Title: How Do Gamblers Start Gambling: Identifying Behavioural Markers for High-risk Internet Gambling

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: BACKGROUND: The goal of this study is to identify betting patterns displayed during the first month of actual Internet gambling on a betting site that can serve as behavioural markers to predict the development of gambling-related problems. METHODS: Using longitudinal data, K-means clustering analysis identified a small subgroup of high-risk gamblers. RESULTS: Seventy-three percent of the members of this subgroup eventually closed their account due to gambling-related problems. The characteristics of this high-risk subgroup were as follows: (1) frequent and (2) intensive betting combined with (3) high variability across wager amount and (4) an increasing wager size during the first month of betting. CONCLUSION: This analysis provides important information that can help to identify potentially problematic gamblers during the early stages of gambling-related problems. Public health workers can use these results to develop early interventions that target high-risk Internet gamblers for prevention efforts. However, one study limitation is that the results distinguish only a small proportion of the total sample; therefore, additional research will be necessary to identify markers that can classify larger segments of high-risk gamblers.

Download the codebook

Download the dataset

See related publication(s)

Title: Actual Internet Sports Gambling Activity: February 2005 through September 2005

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: The first available dataset for the Transparency Project comes from the collaborative Internet gambling research project involving the Division and bwin Interactive Entertainment, AG (bwin), an Internet betting service provider headquartered in Vienna, Austria. The dataset provides the first prospective longitudinal data reflecting real-time Internet sports betting behavior. It contains the information from a large cohort of participants (N=40,499) who opened an account with bwin from February 1, 2005 through February 27, 2005; this dataset also describes the actual aggregated Internet sports gambling behavior of participants during the first 8 months of a longitudinal study that took place from February 1, 2005 through September 30, 2005. This bwin Internet gambling dataset includes the following participant information: demographic information (user ID, country of residence, language, gender, registration date, age at registration), and fixed-odds and live-action betting activity (first active date, last active date, total days active, total stakes, total winnings, total bets).

Download the codebook

Download the dataset

See related publication(s)

Title: Meta-analytic Prevalence Estimates of Disordered Gambling in the US & Canada

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor: National Center for Responsible Gaming

Description: This meta-analytic dataset extends the first comprehensive gambling related epidemiological meta-analysis published in the American Journal of Public Health in 1999 by Shaffer et al to update and refine the prevalence estimates of disordered gambling in the United States and Canada. This dataset employs an empirical strategy to synthesize estimates of gambling-related disorders across an array of differing estimation methodologies and population samples. This dataset provides the opportunity to evaluate and integrate the range of assumptions and strategies used by the various scientists who have estimated the prevalence of disordered gambling. This search strategy initially identified 193 prevalence studies and a total of 146 studies were included for analyses in this meta-analysis study.

Download the codebook

Download the dataset

See related publication(s)

Title: Virtual Casino Gambling: February 2005 through February 2007

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: The data includes two years of recorded Internet betting activity by a cohort of gamblers who subscribed to an Internet gambling service during February 2005. The sample included over 4,000 gamblers who played casino games. The available demographic characteristics of the research sample included age, gender, country of residence, and preferred language. The gambling behavior measures are based on participants’ monetary deposits to, and withdrawals from, their wagering accounts, as well as daily aggregates of betting activity records. The daily betting aggregates include the number of bets made, total monies wagered, and winnings credited to the bettors’ accounts. We measured the duration of gambling involvement as the number of days from the first eligible bet to the last (i.e., Duration).

Download the codebook

Download the dataset

See related publication(s)

Title: Sitting at the Virtual Poker Table: February 2005 through February 2007

Source(s): Division on Addiction, Cambridge Health Alliance, a teaching affiliate of Harvard Medical School

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: This codebook provides information about the raw and analytic datasets that provided the evidence base for research focusing on actual Internet poker gambling (LaPlante et al., 2009). These datasets derive from the collaborative Internet gambling research project between the Division on Addictions (DOA) and bwin Interactive Entertainment, AG (bwin), an Internet betting service provider headquartered in Vienna, Austria. These datasets provide evidence from twenty-four months of the prospective longitudinal, real-time, Internet poker-playing behavior.

The datasets contain raw and analytic data representing twenty-four months of aggregated betting behavior data for sequential bwin subscribers who opened an account with bwin during the period from February 1, 2005 through February 28, 2005. The raw datasets RawDataSet1_DemographicsPoker and RawDataSet2_AggregatePoker represent data from 48,114 people (100% of people who subscribed during February, 2005). Of the full cohort, 4,459 elected to play poker online. Of these, we excluded 951 participants who played fewer than four poker sessions during the study period and 63 poker players who did not begin poker play until the last month of the study period (i.e., began playing poker after January 31, 2007). The resulting sample, included in the analytic data set AnalyticDataSet_Poker, consists of the remaining 3,445 people who contributed data to the analyses reported in LaPlante et al. (2009).

Download the codebook

Download the dataset

See related publication(s)


Datasets Coming Soon

Title: 10 Years of Data from the St. Francis House Moving Ahead Program

PI(s): Dr. Sarah E. Nelson

Sponsor(s): St. Francis House

Description: This dataset will include the baseline assessments and, where available, graduation and follow-up assessments for 668 individuals who participated in the Moving Ahead Program (MAP) between 1999 and 2007. MAP is a 14-week life skills and work readiness instruction program available to guests at one of the largest day shelters in New England. Data include raw and scored measures of demographics, homelessness history, employment history, substance use and gambling history, criminal history, sexual history, treatment history, income, work and life skills, mental health, physical health, as well as measures of involvement and satisfaction with MAP.

Download codebook (Coming soon!)

Dataset will be available for download soon!

Title: Actual Internet Sport Gambling among Subscribers who Attempt to Exceed Corporate Deposit Limits

PI(s): Dr. Howard J. Shaffer

Sponsor(s): bwin, Interactive Entertainment, AG

Description: This dataset will include 2 years of the actual sports gambling behavior records of over 40,000 subscribers to bwin, 100 of whom attempted to exceed corporate deposit limits. This dataset included the daily aggregates of betting activity (i.e., the aggregate number of bets, amount of money wagered, and amount of money won for fixed-odds and live-action sports betting per calendar day) for all participants in the cohort. The data also includes information related to whether subscribers attempted to exceed corporate deposit limits.

Download codebook (Coming soon!)

Dataset will be available for download soon!

See related publication(s)

© Division on Addiction. All Rights Reserved. Last Updated:  March 04, 2013