Exploring the Relationship Between Gaming Disorder and Player Motivation
Description
The dataset comprises responses from a study designed to explore the relationship between gaming addiction and motivational factors among Polish gamers. It includes various data points gathered from a diverse sample of participants. 1. Instruments and Validation: Gaming Addiction Test (GAT): This dataset includes scores from an adapted version of the Internet Addiction Test (IAT) by K. Young, tailored specifically for gaming addiction. The adaptation aligns with Griffiths' Gaming Addiction Criteria, ICD-11 Criteria, and DSM-V criteria. The reliability of this test was assessed using Cronbach's Alpha and McDonald's Omega indicators, ensuring its robustness and consistency in measuring gaming addiction. Motivation to Play Online Games Test: The dataset also contains scores from a modified version of Nick Yee's (2007) Motivation to Play Online Games test. This adaptation measures various motivational factors for gaming, such as escapism, social interaction, and immersion. The reliability of this test was similarly confirmed using Cronbach's Alpha and McDonald's Omega indicators. 2. Participants: Sample Size: The dataset includes data from 520 Polish participants. Data Collection Method: Data was collected using an online surveying tool. The survey was distributed through community channels of popular online games, including World of Warcraft, League of Legends, Dota 2, CS:GO, Apex Legends, and Diablo III and IV. 3. Data Points: Scores: The dataset includes the sums of raw scores and standardized scores (Standard Ten and Z-value) for both the Gaming Addiction Test and the Motivation to Play Online Games test. Descriptive Statistics: Required demographic information is provided, including participants' age, gender, and gaming habits (online vs. offline gaming). Gender was self-identified within four categories: man, woman, other (specify), and prefer not to tell. However, for this study, the descriptors were consolidated to Male and Female, as none of the 520 participants selected 'other' or 'prefer not to tell'. 4. Reliability Indicators: Cronbach's Alpha and McDonald's Omega: These indicators are included in the dataset to validate the internal consistency and reliability of the adapted psychometric tests. 5. Survey Distribution: Community Channels: The survey was disseminated through various online platforms and community channels associated with popular games.
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Steps to reproduce
Software Used: Statistica 13 Jamovi (for scores analysis, McDonald's Omega) MS Excel (for simple analysis) JASP (for confirmatory analysis of MPOG scores, item relevance, and factors - this was done prior to the study on a sample of 420 gamers) 1. Descriptive Statistics: Use Statistica 13 or Jamovi to calculate the mean, median, mode, range, and standard deviation for the primary variables: GAT (Gaming Addiction Test) scores and MPOG (Motivation to Play Online Games) scales. Generate demographic summaries, including age, gender distribution, and gaming habits (online vs. offline). 2. Deviation: Compute the standard deviation for the GAT scores and MPOG scales to assess the variability within the data using Statistica 13 or Jamovi. 3. Normality Tests: Conduct normality tests (e.g., Shapiro-Wilk test) for GAT scores and MPOG scales to determine if the data follows a normal distribution. This can be performed in Statistica 13 or Jamovi. 4. Non-Parametric Gender Differences in Supposed Gaming Disorder: Define the threshold for supposed Gaming Disorder as a GAT score of 50 or higher (Standard Ten score of 6+ and above). Use the Mann-Whitney U test in Statistica 13 or Jamovi to compare GAT scores between genders (Male and Female). 5. Pearson and Spearman Correlation Tests: Calculate Pearson correlation coefficients to measure the linear relationship between GAT scores and each MPOG scale. Calculate Spearman rank correlation coefficients to measure the monotonic relationship between GAT scores and MPOG scales. Both tests can be performed using Statistica 13 or Jamovi. 6. Linear Regression Analysis: Conduct linear regression analysis to investigate the predictive relationship between escapism (dependent variable) and GAT scores (independent variable). Conduct a separate linear regression analysis to examine the relationship between immersion (dependent variable) and GAT scores. Perform these analyses using Statistica 13.