Report
Measuring the adverse consequences from gambling
Read how we have developed new questions about adverse consequence from gambling which are included in the GSGB survey.
Further validation of survey questions
Further analyses were conducted to examine the reliability and incremental validity of the adverse consequences items over and above the Problem Gambling Severity Index (PGSI). To measure internal consistency and determine the reliability of the data, we used Cronbach’s alpha which works on a scale on 0 to 1 where a value closed to 1 means a higher reliability. Reliability analysis showed that items assessing severe consequences and other potential adverse consequences (including the 3 items taken from the PGSI), demonstrated good internal consistency, suggesting that all questions assessed the same underlying construct (Cronbach's alpha = 0.894). To assess the incremental validity of the measure, we examined whether the number of items endorsed predicted variance in mental wellbeing scores over and above that accounted for by the PGSI, amongst people who had gambled in the past year. A two-step hierarchical linear regression was conducted with PGSI scores entered in step 1 of the model and the number of items endorsed, excluding PGSI items, entered in step 2. Mental wellbeing scores (assessed using the SWEMWBS (Short Warwick-Edinburgh Mental Well-being Scale)) were included as the outcome variable.
The regression model showed that mental wellbeing was significantly associated with PGSI scores, and the number of harms endorsed. In step 1 of the model, higher PGSI scores were associated with lower scores on the SWEMWBS (indicating lower mental wellbeing). In step 2 of the model, including the number of adverse consequences endorsed significantly improved model fit (probability value (p-value) less than .001); lower SWEMWBS scores were associated with experiencing a greater number of impacts.
Overall, our findings suggest that the selected items provide a reliable and valid measure of the potential adverse consequences of gambling. While we do not aim to develop a psychometric tool, it is important to ensure that the questions adequately capture the key aspects of gambling-related harm. By demonstrating good psychometric properties, such as internal reliability and incremental validity, we can be confident that the selected questions provide a robust foundation for monitoring trends in gambling-related harm and identifying risk factors.
Table 1: Hierarchical regression analysis of mental wellbeing scores (SWEMWBS) predicted by PGSI and number of other potential adverse consequences endorsed (2,255 participants).
| Model Type | B (coefficient) | SE B (coefficient) | t-value | p-value |
|---|---|---|---|---|
| Model 1 Constant | 23.09 | 0.09 | 247.60 | less than 0.001 |
| Model 1 PGSI score | -0.14 | 0.03 | -4.74 | less than 0.001 |
| Model 2 Constant | 23.10 | 0.09 | 248.21 | less than 0.001 |
| Model 2 PGSI score | -0.01 | 0.05 | 0.27 | 0.780 |
| Model 2 Harms (including "occasionally") | -0.40 | 0.11 | -3.49 | less than 0.001 |
Note. SWEMWBS = Short Warwick-Edinburgh Mental Well-being Scale. SE = Standard Error. Model 1: Adj. R2 = 0.009. Model 2: Adj. R2 =0.014. R² change = .005 (p < .001).
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Last updated: 25 July 2024
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