By applying factor analyses, two self-efficacy dimensions were identified that displayed high internal coherence. Both also displayed significant partial correlations with various constructs and behaviors associated with problem gambling.
Hierarchical logistic regressions used hierarchical logistic models to examine sport betting in relation to MGSES dimensions and continuous predictors such as age, gender, level of boredom proneness and agreement with stay-at-home orders and PGSI scores. Factor loading constraints did not significantly compromise models’ fit while conditions for metric invariance were met.
Gambling Intensity
Gambling intensity levels or betting activity levels are an invaluable way to evaluate the risk of problem gambling. They measure both frequency of betting activity and amount wagered, as well as participation levels across different types of gambling activities.
This study explores online gambling and its intensity measures using self-report and cognitive-behavioral tasks. A survey was conducted using a nested design, with subsample invited in-person for clinical interviewing as well as cognitive-behavioral tasks.
Results indicate that gambling intensity levels remained fairly constant across time points; however, in Wave 3 (W3) participants experienced a reduction in online gambling activity, driven by decreased days spent gambling as well as declining numbers in Never category and an inclination toward categories with lower frequency of gambling activity.
Betting Frequency
By applying the Theory of Planned Behavior (TPB), we explored the antecedents, mediators, and moderators of college student sports betting behavior. We discovered that their intention to bet was significantly influenced by subjective norm, attitudes toward sports betting, motivation to comply with others, perceived behavioral control (PBC) was not an important predictor of intention or problem sports betting behaviors; furthermore impulsive betting tendency and previous sports betting experiences served as key moderators between SBI and PBC.
We performed mediation analysis on gambling account data from the National Gambling Accounts. Our results demonstrated that psychological distress is uniquely associated with online and venue gambling using EGMs or sports betting, with frequency having an indirect impact on problem gambling via bet switching frequency; larger win magnitude scaled both negatively with Spin Initiation Latency as well as positively with Bet Switching Frequency.
Betting Discrepancy
Disparate betting odds may occur for various reasons, including injuries to teams, weather conditions and venues. One way of detecting such discrepancies and preventing further ones is comparing payouts offered by different bookmakers.
Models distinguishing winning and losing trials revealed a credit balance that scales modestly with Spin Initiation Latency and Bet Switching; additionally it displayed two-way and three-way interactions with Trial Number and Session length.
Loss streak length was found to become increasingly associated with gambling behavior as participants gained experience, although its influence was moderated by both within- and between-session predictors. This may be the result of habit formation where losses trigger an automatic response based on audio-visual feedback from previous outcomes, thus leading to habit formation. Furthermore, regular gamblers often adopted more rigid betting strategies as losses mounted – this was particularly prevalent among sports bettors.
Boredom Proneness
As expected, state boredom positively correlated with the number of trial-to-trial choice alternations attempts in a decision-making task, suggesting that people who are easily bored tend to switch between options regardless of risk level more frequently in decision-making tasks – potentially mimicking patterns seen in self-reported risk-taking studies.
Notably, the model also revealed that boredom proneness positively correlates with anxiety (p=0.028). The correlation was mediated through the Behavioral Inhibition System; specifically, boredom proneness increased responsiveness of this system when exposed to reward-based stimulus.
Tables 4 and 5 contain hierarchical multiple regression analyses using responses to sports betting questions as dependent variables, with models that control for gender, age, COVID-19 rule-breaking factor, overdispersion Poisson model predictors such as boredom proneness and self-control the strongest predictors associated with betting behavior; however when combined into models with the number of sports bets as an independent variable the results were less clear-cut.
