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01-12-2015 | Original Paper

Problem Gambling and the Youth-to-Adulthood Transition: Assessing Problem Gambling Severity Trajectories in a Sample of Young Adults

Authors: Jason D. Edgerton, Timothy S. Melnyk, Lance W. Roberts

Published in: Journal of Gambling Studies | Issue 4/2015

Abstract

In this study, using four wave longitudinal data, we examined problem gambling severity trajectories in a sample of young adults. Using latent growth curve modeling, we examined how initial level of problem gambling severity and the rate of change were affected by 11 time-invariant predictors: gender, age of onset of gambling, experiencing a big win early in gambling career, experiencing a big loss early in gambling career, alcohol dependence, drug dependence, anxiety, depression, perceived social support, illusion of control, and impulsiveness. Five of the eleven predictors affected initial levels of problem gambling severity; however only impulsiveness affected the rate of change across time. The mean trajectory was negative (lessening of problem gambling risk severity across time), but there was significant inter-individual variation in trajectories and initial levels of problem gambling severity. The main finding of problem gambling risk diminishing over time challenges the conventional picture of problem gambling as an inevitable “downward spiral,” at least among young adults, and suggests that targeted prevention campaigns may be a cost-effective alternative for reaching treatment resistant youth.

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18 years of age is the legal age to gamble or consume alcohol in Manitoba.

Information accessed at: www.manitobalotteries.com/news/year:2008.

Experts were counselors employed by the Addictions Foundation of Manitoba.

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More detail on selected scales is presented in Appendix 1.

Techniques that assume MNAR, such as pattern-mixture models and selection models, are also becoming more common, but these models rest on a set of unverifiable assumptions (e.g. distributional normality, specifiable starting values for inestimable parameters) the violation of which can actually result in worse, not better, estimates (Enders 2010). Given the stringent conditions underlying MNAR models, “…a well-executed MAR analysis may be preferable to an MNAR analysis, even if there is reason to believe the missingness is systematically related to the outcome variable,” in other words, “a good MAR model may be better than a bad MNAR model” (Enders 2010: 344). A conclusion echoed by Schafer (2003) who recommends the use of auxiliary variables to account for missingness under MAR assumptions.

This robust maximum likelihood estimator (MLR), not only takes missingness into account, but produces estimates that are relatively robust to violations of normality (Muthén and Muthén 2010).

Conventional criteria for establishing good model fit are: RMSEA ≤ .06, CFI ≥ .95, TLI ≥ .95, SRMR ≤ .05 (Byrne 2012).

Wave 1 is the only wave to include 18 year olds; the legal age to gamble in Manitoba.

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Title: Problem Gambling and the Youth-to-Adulthood Transition: Assessing Problem Gambling Severity Trajectories in a Sample of Young Adults
Authors: Jason D. Edgerton
Timothy S. Melnyk
Lance W. Roberts
Publication date: 01-12-2015
Publisher: Springer US
Published in: Journal of Gambling Studies / Issue 4/2015
Electronic ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-014-9501-2

Keynote webinar | Spotlight on medication adherence

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Problem Gambling and the Youth-to-Adulthood Transition: Assessing Problem Gambling Severity Trajectories in a Sample of Young Adults

Abstract

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