Using latent class analysis to identify Finnish gambler types and potential risk

Abstract

The trend of increasing liberalization in gambling markets has been matched by a need for both effective approaches to promote responsible gambling practices and for improved prevention strategies. Given that the majority of players do not experience problematic gambling, it is in the public interest that knowledge is generated which helps identify activities or clusters of activities which are associated with at-risk behaviors. This study uses a representative sample of the Finnish population aged 15-74, to identify distinct types of gamblers based on their behavioral patterns and predictors of class membership via Latent Class Analysis. Cross-sectional random sample data were collected in 2019 (n = 3148). In addition to confirming existing knowledge for gamblers characterized by high engagement and high risk, it offered insights into three further classes: the largest (ME-HR, 45%), was characterized by moderate engagement, but participated in activities associated with higher levels of risk. Additionally, low-risk classes were differentiated by both gambling preferences and demographic characteristics. Given that the largest class was associated with significant potential for the development of problematic behaviors, this work makes several recommendations for preventative actions, including targeted awareness campaigns and psychoeducation addressing erroneous beliefs about gambling.