waluk youssef, dowling the relationship between gambling and adhd 2015.pdf

9 to 14 for all of the major scales with a mean of 65

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missing data varied from 0.9 to 14 % for all of the major scales, with a mean of 6.5 % missing pre-imputation. Little’s MCAR test [ v 2 (95) = 101.09, p = .32] supported the assumption that the remaining missing data were missing completely at random (Rubin 1976 ). As such, the remaining missing data were accounted for using multiple imputation (Rubin 2004 ; Graham 2009 ). Specifically, we imputed 20 complete datasets under a fully conditional specification implemented in SPSS v.22. Only the variables that were used in analyses were included in the multiple imputation estimation. All inferential analyses are based on pooled estimates combined using Rubin’s rules (Rubin 2004 ). A one-sample z-test was used to compare the prevalence of probable ADHD in the current sample to the prevalence of probable ADHD in an adult general population sample (14 %) (Kessler et al. 2007 ). This prevalence estimate of 14 % was identified in a study (Kessler et al. 2007 ) testing the validity of the ASRS 6 question screener via a convenience sample of 668 subscribers to a large health plan in the US. The data in this US study was weighted to adjust for discrepancies between the sample and the popu- lation on socio-demographics and past medical claims. The relationships between the variables (ADHD, problem gambling severity, motor impulsivity, Cluster B personality disorders, alcohol and substance use, gender, and age) were explored using a series of non-parametric Spearman’s Rho correlations due to the ordinal nature of many of the variables (ADHD and gender). Finally, a series of moderated linear regressions were employed to explore whether personality traits (motor impulsivity and Cluster B per- sonality disorders) substance use (alcohol and substance use), and demographic char- acteristics (gender and age) moderated the relationship between ADHD and problem gambling severity. Problem gambling severity was employed as the dependent variable given findings that imply that ADHD symptoms temporally precede the development of gambling problems (Breyer et al. 2009 ; Rugle and Melamed 1993 ; Specker et al. 1995 ). These parametric tests were appropriate given that the dependent variable (PGSI problem gambling severity) was normally distributed. All continuous independent variables were centered prior to the computation of interaction terms. Model diagnostics found no evidence that the assumptions of the linear regression models (i.e., outliers, normality, linearity, homoscedasticity) were violated. Results Scale Descriptives Table 2 displays the descriptive statistics of the measures employed in this study, identi- fying good internal consistency for problem gambling severity (PGSI) ( a = .88), ADHD J Gambl Stud 123
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(ASRS-v1.1) ( a = .86), motor impulsivity (BIS-SF) ( a = .83), and acceptable internal consistency for cluster B disorders (IOWA) ( a = .64). Prevalence of Probable ADHD A one-sample z-test revealed that a significantly higher proportion of the sample of treatment-seeking problem gamblers received a positive endorsement for probable ADHD (24.9 %, n = 47/189 participants with no missing ASRS item responses pre-imputation, 95 % CI 19.3–31.3) than the general US community (14.0 %, Kessler et al. 2007 , z = 4.60, p \ .0001).
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