A Latent Profile Analysis of Internet Gaming Motivation and Internet Gaming Addiction
簡欣儀(Hsin-Yi Chien) ； 黃柏僩(Po-Hsien Huang) ； 許文耀(Wen-Yao Hsu) ； 林怡彤(Yi-Tung Lin)
關鍵詞 Key words : 網路遊戲成癮 ； 網路遊戲動機 ； 潛在剖面分析 ； internet gaming addiction ； internet gaming motivation ； latent profile analysis
DOI:
10.30074/FJMH.202112_34(4).0001
研究目的：過去探討網路遊戲動機與網路遊戲成癮的關係較不具全面性。於是本研究在網路遊戲成癮的測量中整合了「網路成癮測驗（Internet Addiction Test，簡稱IAT）」、「陳氏網路成癮量表（Chen Internet Addiction Scale，簡稱CIAS）」，與「網路遊戲疾患量表（Internet Gaming Disorder-20，簡稱IGD-20），重整一份新的量表。在測量網路遊戲動機方面，本研究以「網路遊戲動機量表（The Motivation to play Online Games Questionnaire，簡稱MPOGQ）」和「線上遊戲動機問卷（The Motives for Online Gaming Questionnaire，簡稱MOGQ）」，重整一份新的量表。本研究嘗試以潛在剖面分析，探討網路遊戲動機與網路遊戲成癮的組合型態可區分成幾種組別，以及這些組別是否可透過玩家人口學變項、遊戲相關變項，與憂鬱症狀進行預測。研究方法：本研究根據637份線上問卷，分別對兩份量表進行探索性與驗證性因素分析。接著使用因素分析的結果進行潛在剖面分析。研究結果：因素分析的結果驗證得到四個網路遊戲成癮面向（依賴、現實衝突、渴望、時間管理問題）與七個網路遊戲動機面向（探索設計機制、能力成就挑戰、社交人際關係、刺激破壞攻擊、思考策略應用、感官動作協調促進、樂趣）。潛在剖面分析界定出了不同成癮、動機與樂趣程度的五種組型。預測效度上，每週遊戲時間可區分「低成癮／低動機／低樂趣」組，與其他三個中至高成癮組別；憂鬱可區分出「高成癮／高動機／低樂趣」及其他四個組別的差異；男性落於中至高成癮的三個組別的機率顯著高於女性。另外，網路遊戲動機中，「能力成就挑戰」及「刺激破壞攻擊」可正向預測網路遊戲成癮，但「樂趣」會負向預測渴望的網路遊戲成癮面向。研究結論：本研究的結果指出即使是高網路遊戲動機不見得與網路成癮具有強的關聯性。依據本研究的結果發現不同向度的網路遊戲動機與網路遊戲成癮的組合時是具有異質性的，且在憂鬱的表現也有所不同。
Purpose: Previous studies regarding the relationship between internet gaming motivation and internet gaming addiction were not comprehensive enough. To measure internet gaming motivation, we developed the Internet Gaming Motivation Scale-Integrated Version (IGMS-IV) based on the Motivation to play Online Games Questionnaire and the Motives for Online Gaming Questionnaire. Based on the Internet Addiction Test, the Chen Internet Addiction Scale, and the Internet Gaming Disorder-20, we developed the Internet Gaming Addiction Scale-Integrated Version (IGAS-IV) to measure internet gaming addiction. We aimed to identify the different patterns of internet gaming motivation and addiction by conducting latent profile analysis (LPA). We then investigated whether these patterns can be predicted by demographic factors, internet gaming-related variables, and depressive symptoms. Methods: We collected 637 valid questionnaires online. We analyzed the items in each of our integrated version scales using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We then applied LPA based on the findings from the factor analyses. Results: The factor analyses identified 4 factors in the IGAS-IV (i.e., dependence, reality, craving, and time), and 7 factors in the IGMS-IV (i.e., discover, achieve, social, damage, thinking, skill, and fun). The LPA identified 5 latent profiles of individuals with different levels of addiction, motivation, and fun. Mean time spent on internet gaming per week corresponded with the low addiction/low motivation/low fun group in contrast to the other 3 groups with medium to high levels of addiction. Depressive symptoms corresponded with membership in the high addiction/ high motivation/low fun group but not with the other 4 groups. Significantly more men than women were in the medium to high level addiction groups. The internet gaming motivation subfactors of Achieve and Damage positively predicted the level of internet gaming addiction, and the motivation of Fun negatively predicted the level of Craving addiction. Conclusions: The results of the analyses highlighted that high internet gaming motivation is not necessarily associated with internet gaming addiction. The combinations of different subcomponents of internet gaming motivation and internet gaming addiction were heterogeneous, each latent profile had a different level of depressive symptoms.