How does the phrasing of house edge information affect gamblers’ perceptions and level of understanding? A Registered Report

References

• Ai C, Norton EC. 2003. Interaction terms in logit and probit models. Econom Lett. 80(1):123–129.
   Web of Science ®Google Scholar
• Behavioural Insights Team. 2022. Comprehension of gambling odds. https://www.bi.team/wp-content/uploads/2022/05/2022-05-Comprehension-of-gambling-odds-BIT-experimental-results.pdf.
   Google Scholar
• Beresford K, Blaszczynski A. 2020. Return-to-player percentage in gaming machines: impact of informative materials on player understanding. J Gambl Stud. 36(1):51–67.
   PubMed Web of Science ®Google Scholar
• Blackwell AK, Drax K, Attwood AS, Munafò MR, Maynard OM. 2018. Informing drinkers: can current UK alcohol labels be improved? Drug Alcohol Depend. 192:163–170.
   PubMed Web of Science ®Google Scholar
• Bleich SN, Economos CD, Spiker ML, Vercammen KA, VanEpps EM, Block JP, Elbel B, Story M, Roberto CA. 2017. A systematic review of calorie labeling and modified calorie labeling interventions: impact on consumer and restaurant behavior. Obesity. 25(12):2018–2044.
   PubMed Web of Science ®Google Scholar
• Brühlmann F, Petralito S, Aeschbach LF, Opwis K. 2020. The quality of data collected online: an investigation of careless responding in a crowdsourced sample. Methods Psychol. 2:100022.
   Google Scholar
• Collins D, Green S, d’Ardenne J, Wardle H, Williams S-K. 2014. Understanding of return to player messages: findings from user testing. London: NatCen Social Research.
   Google Scholar
• Dubois P, Albuquerque P, Allais O, Bonnet C, Bertail P, Combris P, Lahlou S, Rigal N, Ruffieux B, Chandon P. 2021. Effects of front-of-pack labels on the nutritional quality of supermarket food purchases: evidence from a large-scale randomized controlled trial. J Acad Mark Sci. 49(1):119–138.
   Web of Science ®Google Scholar
• Eggert K. 2004. Truth in gaming: toward consumer protection in the gambling industry. Md L Rev. 63(2):217–286.
   Google Scholar
• Eyal P, David R, Andrew G, Zak E, Ekaterina D. 2021. Data quality of platforms and panels for online behavioral research. Behav Res Methods. 54(4):1643–1662.
   PubMed Web of Science ®Google Scholar
• Ferris J, Wynne HJ. 2001. The Canadian problem gambling index: final report. Ottawa: Canadian Centre on Substance Abuse.
   Google Scholar
• Harrigan KA, Dixon M. 2010. Government sanctioned 'tight” and “loose” slot machines: how having multiple versions of the same slot machine game may impact problem gambling. J Gambl Stud. 26(1):159–174.
   PubMed Web of Science ®Google Scholar
• Harrigan K, Brown D, Barton KR. 2017. Classification of slot machines in Ontario: providing relevant information to players. Ontario, Canada: Gambling Research Exchange Ontario.
   Google Scholar
• Harrigan K, Dixon M. 2009. PAR Sheets, probabilities, and slot machine play: implications for problem and non-problem gambling. JGI. 23(23):81–110.
   Google Scholar
• Heirene RM. 2021. A call for replications of addiction research: which studies should we replicate and what constitutes a ‘successful’ replication? Addict Res Theory. 29(2):89–97.
   Web of Science ®Google Scholar
• Hobin E, Vallance K, Zuo F, Stockwell T, Rosella L, Simniceanu A, White C, Hammond D. 2018. Testing the efficacy of alcohol labels with standard drink information and national drinking guidelines on consumers’ ability to estimate alcohol consumption. Alcohol Alcohol. 53(1):3–11.
   PubMed Web of Science ®Google Scholar
• Jones A, Earnest J, Adam M, Clarke R, Yates J, Pennington CR. 2022. Careless responding in crowdsourced alcohol research: a systematic review and meta-analysis of practices and prevalence. Exp Clin Psychopharmacol. 30(4):381–399.
   PubMed Web of Science ®Google Scholar
• Lades LK, Delaney L. 2022. Nudge FORGOOD. Behav Public Policy. 6(1):75–94.
   Google Scholar
• Lakens D. 2017. Equivalence tests: a practical primer for t tests, correlations, and meta-analyses. Soc Psychol Personal Sci. 8(4):355–362.
   PubMed Web of Science ®Google Scholar
• Livingstone C, Rintoul A, de Lacy-Vawdon C, Borland R, Dietze P, Jenkinson R, Livingston M, Room R, Smith B, Stoove M. 2019. Identifying effective policy interventions to prevent gambling-related harm. Melbourne (AU): Victorian Responsible Gambling Foundation.
   Google Scholar
• McCabe C, Halvorson MA, King K, Cao X, Kim D. 2020. Estimating and interpreting interaction effects in generalized linear models of binary and count data. PsyArXiv. https://psyarxiv.com/th94c/.
   Google Scholar
• Newall PWS, Byrne CA, Russell AMT, Rockloff MJ. 2022. House-edge information and a volatility warning lead to reduced gambling expenditure: potential improvements to return-to-player percentages. Addict Behav. 130:107308.
   PubMed Web of Science ®Google Scholar
• Newall PWS, Walasek L, Hassanniakalager A, Russell AM, Ludvig EA, Browne M. 2023. Statistical risk warnings in gambling. Behav Public Policy. 7(2):219–239.
   Google Scholar
• Newall PWS, Walasek L, Ludvig EA. 2020a. Equivalent gambling warning labels are perceived differently. Addiction. 115(9):1762–1767.
   PubMed Web of Science ®Google Scholar
• Newall PWS, Walasek L, Ludvig EA. 2020b. Risk communication improvements for gambling: house-edge information and volatility statements. Psychol Addict Behav. 36(4):358–363.
   PubMed Web of Science ®Google Scholar
• Newall PWS, Walasek L, Ludvig EA, Rockloff MJ. 2020. House-edge information yields lower subjective chances of winning than equivalent return-to-player percentages: new evidence from support forum participants. JGI. 45:166–172.
   Google Scholar
• Newall PWS, Walasek L, Ludvig EA, Rockloff MJ. 2022. Nudge versus sludge in gambling warning labels: how the effectiveness of a consumer protection measure can be undermined. Behav Sci Policy. 8(1):17–23.
   Google Scholar
• Pickering D, Blaszczynski A. 2021. Paid online convenience samples in gambling studies: questionable data quality. Int Gambl Stud. 21(3):516–536.
   Web of Science ®Google Scholar
• R Core Team. 2020. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/.
   Google Scholar
• Russell AMT, Browne M, Hing N, Rockloff M, Newall P. 2022. Are any samples representative or unbiased? Reply to Pickering and Blaszczynski. Int Gambl Stud. 22(1):102–113.
   Web of Science ®Google Scholar
• Schwartz DG. 2013. Penny wise, player foolish? Slot-hold regulation and consumer preference. J Bus Res. 66(9):1623–1628.
   Web of Science ®Google Scholar
• Walker AC, Stange M, Dixon MJ, Koehler DJ, Fugelsang JA. 2019. Graphical depiction of statistical information improves gambling-related judgments. J Gambl Stud. 35(3):945–968.
   PubMed Web of Science ®Google Scholar
• Woolley R, Livingstone C, Harrigan K, Rintoul A. 2013. House edge: hold percentage and the cost of EGM gambling. Int Gambl Stud. 13(3):388–402.
   Web of Science ®Google Scholar
• Zhang Z, Mai Y, Yang M, Zhang MZ. 2018. Package ‘WebPower’. https://webpower.psychstat.org/.
   Google Scholar