Validating gameplay activity inventory (GAIN) for modeling player profiles

In the present study, we validated Gameplay Activity Inventory (GAIN), a short and psychometrically sound instrument for measuring players’ gameplay preferences and modeling player profiles. In Study 1, participants in Finland ( N = 879 ) responded to a 52-item version of GAIN. An exploratory factor...

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Bibliographic Details
Published in:User modeling and user-adapted interaction 2018-12, Vol.28 (4-5), p.425-453
Main Authors: Vahlo, Jukka, Smed, Jouni, Koponen, Aki
Format: Article
Language:English
Subjects:
Cluster analysis
Computer Science
Demographics
Discriminant analysis
Factor analysis
Games
Management of Computing and Information Systems
Measuring instruments
Modelling
Multimedia Information Systems
Questionnaires
User Interfaces and Human Computer Interaction
Validation studies
Validity
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Summary:In the present study, we validated Gameplay Activity Inventory (GAIN), a short and psychometrically sound instrument for measuring players’ gameplay preferences and modeling player profiles. In Study 1, participants in Finland ( N = 879 ) responded to a 52-item version of GAIN. An exploratory factor analysis was used to identify five latent factors of gameplay activity appreciation: Aggression , Management , Exploration , Coordination , and Caretaking . In Study 2, respondents in Canada ( N = 1322 ) and Japan ( N = 1178 ) responded to GAIN, and the factor structure of a 15-item version was examined using a Confirmatory Factor Analysis. The results showed that the short version of GAIN has good construct validity, convergent validity, and discriminant validity in Japan and in Canada. We demonstrated the usefulness of GAIN by conducting a cluster analysis to identify player types that differ in both demographics and game choice. GAIN can be used in research as a tool for investigating player profiles. Game companies, publishers and analysts can utilize GAIN in player-centric game development and targeted marketing and in generating personalized game recommendations.
ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-018-9212-y