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Validating gameplay activity inventory (GAIN) for modeling player profiles

机译：验证游戏活动清单（GAIN）以建模玩家资料

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摘要

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 ( $$N=879$$ 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 ( $$N=1322$$ N = 1322 ) and Japan ( $$N=1178$$ 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.
机译：在本研究中，我们验证了游戏活动活动清单（GAIN），这是一种简短的心理测验工具，用于测量玩家的游戏偏好和建模玩家资料。在研究1中，芬兰的参与者（$$ N = 879 $$ N = 879）对GAIN的52个项目做出了回应。探索性因素分析用于识别游戏活动欣赏的五个潜在因素：侵略性，管理性，探索性，协调性和谨慎性。在研究2中，加拿大（$$ N = 1322 $$ N = 1322）和日本（$$ N = 1178 $$ N = 1178）的受访者对GAIN做出了回应，并使用15个项目的因子结构进行了检验验证性因素分析。结果表明，短版的GAIN在日本和加拿大具有良好的结构效度，收敛效度和判别效度。我们通过进行聚类分析来识别人口统计和游戏选择不同的玩家类型，从而证明了GAIN的有用性。 GAIN可以在研究中用作调查玩家资料的工具。游戏公司，发行商和分析师可以利用GAIN进行以玩家为中心的游戏开发和有针对性的营销，并生成个性化的游戏推荐。

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