A Functional Analytic Approach to Understanding Disordered Gambling

By Dixon, Mark R.; Wilson, Alyssa N. et al. | The Psychological Record, June 2018 | Go to article overview

A Functional Analytic Approach to Understanding Disordered Gambling


Dixon, Mark R., Wilson, Alyssa N., Belisle, Jordan, Schreiber, James B., The Psychological Record


Attempts to understand why people gamble when the odds of winning are against them have included a myriad of self-report assessments that hope to gain insight into the minds of gamblers (e.g., Johnson, Hamer, & Nora, 1998; Kim, Grant, Adson, & Young, 2001). Hypotheses suggest gambling addiction is a function of biological, psychological, ontological, and socio-cultural responses (see also Blaszcznski & Nower, 2002; Shaffer & Kidman, 2003). Assessments range from targeting gambling proclivity (i.e., The South Oaks Gambling Screen, SOGS; Lesieur & Blume, 1987) and gambling severity (i.e., The This paper is based on a portion of the dissertation of the second author, completed while affiliated with Southern Illinois University.

Problem Gambling Severity Index, PGSI; Ferris & Wynne, 2001) to identifying the functions sustaining the behavior (i.e., Gambling Functional Assessment, GFA; Dixon & Johnson, 2007; Gambling Functional Assessment--Revised; GFA-R; Weatherly, Miller, & Terrell, 2011). Function-based assessments are rather unique in their quest to discover what types of consequences sustain gambling (e.g., social, escape, neurological), instead of relying solely on corollary environmental events that may occasion gambling (e.g., advertisements, access to games, prior losing/winning history). The GFA (Dixon & Johnson, 2007) was the first functional behavioral assessment developed to determine which of four possible consequences maintains a person's gambling behaviors. The GFA was similar to other behavioral assessments designed to measure aberrant behaviors, particularly self-injurious behaviors (Durand & Crimmins, 1988), which have been used in clinical psychology and by behavior analysts. The 20-item GFA included questions regarding the contributions of social attention (e.g., enjoyment of interacting with peers), psychological/physical escape (e.g., ability to forget about stress at home, leave a troubled work environment), access to tangible rewards (e.g., money, comps, vouchers), and sensory (e.g., feeling a rush or buzz). For example, a question targeting an escape function stated, "I often gamble when I feel stressed or anxious," while a question targeting a sensory function stated, "I like the sounds, the lights, and the excitement that often go along with gambling." Questions are rated on a 7-point Likert scale, from never (0) to always (6). Afterward, each function is summed, and the highest category is identified as the most likely maintaining function of gambling behavior.

In research that followed, the utility of the GFA appeared statistically promising. For example, Miller, Meier, Muehlenkamp, and Weatherly (2009) tested the construct validity of the GFA with 949 undergraduate university students, all considered to be non-disordered gamblers. A factor analysis yielded two factor loadings consistently across participants, which reported to be representative of positive and negative reinforcement (Miller et al., 2009). Escape items were reported to be representative of negative reinforcement, while items related to tangible, sensory, and attention were reported as positive reinforcement. Subsequently, Weatherly et al., (2011) revised the items on the GFA to construct the Gambling Functional Assessment--Revised (GFA-R) that included only negative and positive functional categories.

To test the factor structure of the GFA-R (Weatherly et al., 2011), 728 undergraduate university students completed the 22-item assessment, and responses were subjected to explanatory and confirmatory factor analyses. A principal component analysis suggested a two-factor solution accounting for 53.86% of the variance in participant responses. A total of six items were removed due to inaccurate loading, leaving eight items representing positive reinforcement and eight items representing negative reinforcement. A confirmatory structural equation model was used to asses multiple fit indices and suggested a moderate correlation (r = 0. …

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