Research into problematic video gaming has increased greatly over the last decade and many screening instruments have been developed to identify such behaviour. This study re-examined the Problematic Videogame Playing [PVP] Scale. The objectives of the study were to (i) examine its psychometric properties in two European countries, (ii) estimate the prevalence of potential pathological gaming among adolescents in both countries, and (iii) assess the classification accuracy of the PVP Scale based on its symptomatology as a way of exploring its relationship with both the behavioural component model of addiction and the proposed Internet Gaming Disorder. The data were collected via a survey administered to 2,356 adolescents aged between 11 and 18 years from Spain (n=1,132) and Great Britain (n=1,224). Results indicated that the reliability of both versions was adequate, and the factorial and construct validity were good. Findings also showed that the prevalence of pathological gamers estimated with a rigorous cut-off point was 7.7% for Spanish and 14.6% for British adolescents. The scale showed adequate sensitivity, specificity and classification accuracy in both countries, and was able to differentiate between social and potential pathological 2 2 gamers, and from their addictive symptomatology. The implications of these findings are discussed.
This study uses an innovative statistical strategy to test the role of certain variables as predictors of problematic Internet and mobile phone usage among adolescents in Spain and in the United Kingdom . A paper-and-pencil questionnaire was used, with socio-demographics and patterns of technology usage as variables, and two tests were administered: the Problematic Internet Entertainment Use Scale for Adolescents (PIEUSA) and the Mobile Phone Problem Use Scale for Adolescents (MPPUSA). The overall sample size was 2228 high school students aged between 11 and 18 from Barcelona and London. PIEUSA and MPPUSA scores were transformed into normed scores, and both were then dichotomized according to three statistical criteria as cut-off points (i.e., median, 80th percentile, and extreme scores below the 25th percentile and above the 75th percentile) in order to establish the relationship between the variables above and the excessive use of the Internet or mobile phones, using a binary logistic regression. The results show that the best predictive model for both technologies includes socio-demographic variables as predictors of extreme scores for excessive Internet and mobile phone usage, with good sensitivity, specificity and classification accuracy, as well as a notable capacity for discrimination according to the receiver-operating characteristic curve. Implications of these findings are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.