Table 2.
Logistic regression predicting non-problematic online EGM gamblers compared to problematic online EGM gamblers.
| Variable | B | SE | Wald | p | OR | 95% C.I. for OR |
|
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| Education (ref = tertiary) | 1.03 | 0.44 | 5.50 | 0.019 | 2.80 | 1.18 | 6.63 |
| EGM gambling frequency | 0.39 | 0.12 | 11.10 | 0.001 | 1.47 | 1.17 | 1.84 |
| Alcohol use while gambling (ref = no) | 0.97 | 0.40 | 5.81 | 0.016 | 2.63 | 1.20 | 5.79 |
| Drug use while gambling (ref = no) | 1.21 | 0.62 | 3.89 | 0.049 | 3.37 | 1.01 | 11.24 |
| Gambling Attitudes | -0.25 | 0.16 | 2.41 | 0.120 | 0.78 | 0.57 | 1.07 |
| Constant | -2.16 | 0.64 | 11.35 | 0.001 | 0.11 | ||
Non-problematic online EGM gamblers coded as 0, problematic online EGM gamblers coded as 1. Therefore, positive coefficients indicate that a higher score on that variable is associated with problematic online EGM gamblers. Bold text indicates a significant predictor. Income was not included due to missing data, and Kessler 6 was not included due to a high correlation with the PGSI.