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      A Large-Scale Study of Changes to the Quantity, Quality, and Distribution of Video Game Play During a Global Health Pandemic

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          Abstract

          Video game play has been framed as both protective factor and risk to mental health during the Coronavirus disease (COVID-19) pandemic. We conducted a statistical analysis of changes to video game play during the pandemic to better understand gaming behavior and in doing so provide an empirical foundation to the fractured discourse surrounding play and mental health. Analyses of millions of players’ engagement with the 500 globally most popular games on the Steam platform indicated that the quantity of play had dramatically increased during key points of the pandemic; that those increases were more prominent for multiplayer games, suggesting that gamers were seeking out the social affordances of video game play; and that play had become more equally distributed across days of the week, suggesting increased merging of leisure activities with work and school activities. These results provide a starting point for empirically grounded discussions on video games during the pandemic, their uses, and potential effects.

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          Interrater reliability: the kappa statistic

          The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
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            Generalized Additive Models

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              The Motivational Pull of Video Games: A Self-Determination Theory Approach

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                Author and article information

                Journal
                Technology, Mind, and Behavior
                American Psychological Association
                2689-0208
                November 8, 2021
                : 2
                : 4
                Affiliations
                [1]Oxford Internet Institute, University of Oxford
                [2]Department of Computer Science, University of York
                [3]Game AI and Cognitive Sciences Groups, Queen Mary University of London
                [4]Department of Experimental Psychology, University of Oxford
                Author notes
                Action Editor: C. Shawn Green was the action editor for this article.
                Acknowledgements: We are grateful to Pavel Djundik (SteamDB) for assistance in collecting the data.
                Funding: This research was supported by the Huo Family Foundation.
                Disclosures: The authors declare no conflicts of interest.
                Author Contributions: Conceptualization: Matti Vuorre, David Zendle, Elena Petrovskaya, Nick Ballou and Andrew K. Przybylski. Data Curation: Matti Vuorre and David Zendle. Formal Analysis: Matti Vuorre. Funding Acquisition: Andrew K. Przybylski. Investigation: Matti Vuorre and David Zendle. Methodology: Matti Vuorre, David Zendle, Elena Petrovskaya, Nick Ballou and Andrew K. Przybylski. Project Administration: Matti Vuorre, David Zendle and Andrew K. Przybylski. Resources: Andrew K. Przybylski. Software: Matti Vuorre and David Zendle. Supervision: Andrew K. Przybylski. Validation: Matti Vuorre. Visualization: Matti Vuorre. Writing of Original Draft Preparation: Matti Vuorre, David Zendle and Andrew K. Przybylski. Writing of Review and Editing: Matti Vuorre, David Zendle, Elena Petrovskaya, Nick Ballou and Andrew K. Przybylski.
                Data Availability Statement: The raw data and annotated analysis code supporting this work are available at https://osf.io/ya9jt/. A public preprint of this work is available at https://psyarxiv.com/8me6p/
                Open Science Disclosures:

                The data are available at https://osf.io/n7mzv

                The experiment materials are available at https://osf.io/n7mzv

                [*] Andrew K. Przybylski, Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford OX1 3JS, United Kingdom andy.przybylski@oii.ox.ac.uk
                Author information
                https://orcid.org/0000-0001-5052-066X
                https://orcid.org/0000-0003-0279-6439
                https://orcid.org/0000-0003-4276-6154
                https://orcid.org/0000-0003-4126-0696
                https://orcid.org/0000-0001-5547-2185
                Article
                2022-01157-001
                10.1037/tmb0000048
                81a15d69-ef8e-406a-816e-df455a5379fd
                © 2021 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                History

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                COVID-19,video games,technology

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