Testing the construct validity of Dixon and Johnson's (2007) Gambling Functional Assessment.
The GFA checklist sorts gambling motives into two tidy boxes, but those boxes still need real-world proof.
01Research in Context
What this study did
Bigby et al. (2009) checked if the Gambling Functional Assessment really measures two kinds of reinforcement.
They gave the 20-question self-report to college students who gamble.
Factor analysis asked: do the items split into positive and negative reinforcement groups?
What they found
The math produced two factors, but the fit was shaky.
The authors say the GFA structure looks okay on paper, yet it still needs testing with real clinical gamblers.
How this fits with other research
Katz et al. (2003) did the same factor-analytic dance earlier with the FACT tool for multiply-controlled problem behavior.
Their work set the template: write brief items, run factors, claim a quick checklist.
Fahmie et al. (2013) later showed a screener can work; their FAST predicted the best FA condition about two-thirds of the time.
Together these papers say brief checklists can help, but each new tool must prove it predicts actual behavior.
Why it matters
If you assess adults who gamble, do not treat GFA scores as fact. Use them as a talking point, then watch what happens before and after the person plays. Track wins, losses, escape, and thrills directly. Only real-time data will tell you which reinforcers keep the habit alive.
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02At a glance
03Original abstract
The Gambling Functional Assessment (GFA; Dixon & Johnson, 2007) is a 20-item self-report inventory identifying four potential consequences maintaining gambling behavior. Exploratory and confirmatory factor analyses are performed for two large, nonclinical samples of university undergraduates. For the exploratory analysis, the optimal model yields two factors: Positive Reinforcement (correlated with GFA Sensory, Attention, and Tangible scores) and Negative Reinforcement (correlated with GFA Escape scores). One GFA item fails to load on either factor adequately. Factor loadings are confirmed using structural equation modeling for the second sample. The resulting model yields a mix of adequate and suboptimal fit indicators. Although the 2-factor model of the GFA has great theoretical utility and shows significant promise, confirmation within clinical samples of gamblers will be necessary to further validate the model. GFA Escape scores are uniquely distributed in the two samples and may represent functions most likely to maintain pathological gambling.
Behavior modification, 2009 · doi:10.1177/0145445508320927