Assessing the factor structure of the Problem and Pathological Gambling Measure (PPGM)

Background and aims: The Problem and Pathological Gambling Measure (PPGM) is increasingly used to assess gambling problems, exhibiting excellent correspondence between population surveys and clinical interviews. Despite its increasing use as a sum-scoring measurement for gambling problem severity, the internal structure of this survey has not been rigorously tested. The aim of this study is to analyze the factor structure of the PPGM scale and determine the best structural model that accounts for a population-representative sample of gamblers.Design and setting: Secondary data analysis from the Finnish Gambling Harm Survey on gambling-related behaviors in Finland in 2016 and 2017.Participants: A total of 3,218 participants in 2016 and 1,250 in 2017.Measurements: Responses from PPGM were utilized for confirmatory factor analysis, with gambling frequency, diversity, expenses, and harms as criterion variables. All measures were self-reported.Findings: While the separation between the impaired control and other addictive issues in the PPGM design was unnecessary for model fit, two stable common sources of variance in PPGM have been identified, namely gambling harm and behavioral dependence. The two-factor model, with a correlated residual error between two self-denial items, better fit the data than a more complex bifactor model. The reliability analysis supported the appropriateness of using PPGM as a continuous scoring scale. Furthermore, PPGM exhibited stronger associations with gambling harm than excessive gambling, as measured by gambling engagement intensity and diversity.Conclusions: Combining factor-item assignment permutation and traditional model fit approaches, the correlated two-factor model with a correlated residual error between two self-denial items was the best-fitting model. It exhibited adequate model fit, a parsimonious structure, and excellent factor validities and reliabilities.