Literature DB >> 24708074

Accuracy of self-reported versus actual online gambling wins and losses.

Julia Braverman1, Matthew A Tom1, Howard J Shaffer1.   

Abstract

This study is the first to compare the accuracy of self-reported with actual monetary outcomes of online fixed odds sports betting, live action sports betting, and online casino gambling at the individual level of analysis. Subscribers to bwin.party digital entertainment's online gambling service volunteered to respond to the Brief Bio-Social Gambling Screen and questions about their estimated gambling results on specific games for the last 3 or 12 months. We compared the estimated results of each subscriber with his or her actual betting results data. On average, between 34% and 40% of the participants expressed a favorable distortion of their gambling outcomes (i.e., they underestimated losses or overestimated gains) depending on the time period and game. The size of the discrepancy between actual and self-reported results was consistently associated with the self-reported presence of gambling-related problems. However, the specific direction of the reported discrepancy (i.e., favorable vs. unfavorable bias) was not associated with gambling-related problems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Entities:  

Mesh:

Year:  2014        PMID: 24708074     DOI: 10.1037/a0036428

Source DB:  PubMed          Journal:  Psychol Assess        ISSN: 1040-3590


  17 in total

1.  Differences in the Gambling Behavior of Online and Non-online Student Gamblers in a Controlled Laboratory Environment.

Authors:  Kevin S Montes; Jeffrey N Weatherly
Journal:  J Gambl Stud       Date:  2017-03

Review 2.  When Criticizing Others It is Helpful to Focus on Actual Behavior: A Comment About Auer and Griffiths (2016).

Authors:  Howard J Shaffer; Matthew A Tom; Julia Braverman
Journal:  J Gambl Stud       Date:  2017-12

Review 3.  Systematic Review of Empirically Evaluated School-Based Gambling Education Programs.

Authors:  Brittany Keen; Alex Blaszczynski; Fadi Anjoul
Journal:  J Gambl Stud       Date:  2017-03

4.  Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting.

Authors:  Michael Auer; Mark D Griffiths
Journal:  J Gambl Stud       Date:  2022-07-19

5.  Impact of wagering inducements on the gambling behaviors of on-line gamblers: A longitudinal study based on gambling tracking data.

Authors:  Marianne Balem; Bastien Perrot; Jean-Benoit Hardouin; Elsa Thiabaud; Anaïs Saillard; Marie Grall-Bronnec; Gaëlle Challet-Bouju
Journal:  Addiction       Date:  2021-09-23       Impact factor: 7.256

6.  The relationship between gambling expenditure, socio-demographics, health-related correlates and gambling behaviour-a cross-sectional population-based survey in Finland.

Authors:  Sari Castrén; Jukka Kontto; Hannu Alho; Anne H Salonen
Journal:  Addiction       Date:  2017-09-05       Impact factor: 6.526

7.  Self-Reported Losses Versus Actual Losses in Online Gambling: An Empirical Study.

Authors:  Michael Auer; Mark D Griffiths
Journal:  J Gambl Stud       Date:  2017-09

8.  Gambling expenditure by game type among weekly gamblers in Finland.

Authors:  Anne H Salonen; Jukka Kontto; Riku Perhoniemi; Hannu Alho; Sari Castrén
Journal:  BMC Public Health       Date:  2018-06-05       Impact factor: 3.295

9.  Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis.

Authors:  Gaëlle Challet-Bouju; Jean-Benoit Hardouin; Elsa Thiabaud; Anaïs Saillard; Yann Donnio; Marie Grall-Bronnec; Bastien Perrot
Journal:  J Med Internet Res       Date:  2020-08-12       Impact factor: 5.428

10.  Level of Agreement Between Problem Gamblers' and Collaterals' Reports: A Bayesian Random-Effects Two-Part Model.

Authors:  Kristoffer Magnusson; Anders Nilsson; Gerhard Andersson; Clara Hellner; Per Carlbring
Journal:  J Gambl Stud       Date:  2019-12
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.