Gamer profiles; Gaming motivation; Scale development; Systematic literature review; Video games; Psychology (all)
Résumé :
[en] With billions of players, gaming is ubiquitous in everyday life. The growing interest in games has also sparked scientific interest in why people play. Existing measures, however, are focused on specific theories, genres, or games, lack validation, or highlight disordered gaming. Therefore, the Motivation to Play Scale (MOPS) provides a comprehensive scale assessing general gaming motivation. After an item pool was based on a systematic literature review, we evaluated the dimensionality of items and used exploratory factor analysis (N1 = 562) resulting in 58 items and a 10-factor structure (i.e., creativity/exploration, escapism, competition, prestige, enjoyment, achievement, socializing, boredom, aggression, and skill). Lastly, we cross-validated the structure using confirmatory factor analysis and exploratory structural equation modeling (N2 = 732). Latent profile analysis identified four gamer types (i.e., casual players, high performers, crafters, and highly involved players). Overall, results suggest that the MOPS is reliable and valid to assess general gaming motivation.
Disciplines :
Communication & médias
Auteur, co-auteur :
HOLL, Elisabeth ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences > Department of Behavioural and Cognitive Sciences > Team André MELZER ; Media and Business Communication, Human Computer Media Institute, University of Würzburg, Würzburg, Germany
SISCHKA, Philipp ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Health and Behaviour
WAGENER, Gary Lee ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Health and Behaviour
Melzer, André ; Media and Experimental Laboratory, Department of Behavioural and Cognitive Science, University of Luxembourg, Esch-sur-Alzette, Luxembourg ; Unilu - University of Luxembourg > Department of Behavioural and Cognitive Sciences
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
The Motivation to Play Scale (MOPS) - introducing a validated measure of gaming motivation
C.A. Anderson A. Shibuya N. Ihori E.L. Swing B.J. Bushman A. Sakamoto H.R. Rothstein M. Saleem Violent video game effects on aggression, empathy, and prosocial behavior in Eastern and western countries: A meta-analytic review Psychological Bulletin 2010 136 2 151 173 10.1037/a0018251 20192553
Asparouhov, T., & Muthén, B. (2022). First-order derivative warning message, condition number, and non-identification. https://statmodel.com/download/ConditionNumber.pdf
Auerswald, M., & Moshagen, M. (2019). How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions. Psychological Methods, 24(4), 468–491. https://doi.org/10.1037/met0000200
Bányai, F., Griffiths, M. D., Demetrovics, Z., & Király, O. (2019a). The mediating effect of motivations between psychiatric distress and gaming disorder among Esport gamers and recreational gamers. Comprehensive Psychiatry, 94, 152117. https://doi.org/10.1016/j.comppsych.2019.152117
Bányai, F., Griffiths, M. D., Király, O., & Demetrovics, Z. (2019b). The psychology of esports: A systematic literature review. Journal of Gambling Studies, 35(2), 351–365. https://doi.org/10.1007/s10899-018-9763-1
M. Barr A. Copeland-Stewart Playing video games during the COVID-19 pandemic and effects on players’ well-being Games and Culture 2022 17 1 122 139 10.1177/15554120211017036
Bartle, R. (1996). Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD research. http://www.arise.mae.usp.br/wp-content/uploads/2018/03/Bartle-player-types.pdf
P.M. Bentler K.H. Yuan Positive definiteness via off-diagonal scaling of a symmetric indefinite matrix Psychometrika 2011 76 1 119 123 10.1007/s11336-010-9191-3 21566679 3091008
E.A. Boyle T. Hainey T.M. Connolly G. Gray J. Earp M. Ott T. Lim M. Ninaus C. Ribeiro J. Pereira An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games Computers & Education 2016 94 178 192 10.1016/j.compedu.2015.11.003
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150. https://doi.org/10.1207/S15327906MBR3601_05
G. Celeux G. Soromenho An entropy criterion for assessing the number of clusters in a mixture model Journal of Classification 1996 13 2 195 212 10.1007/BF01246098
Y. Cheng The Mediating effects of Motivation for playing Pokémon go on Internet Gaming Disorder and Well–Being The American Journal of Family Therapy 2019 47 1 19 36 10.1080/01926187.2019.1583614
Clark, S. L., & Muthen, B. (2009). Relating latent class analysis results to variables not included in the analysis. https://www.statmodel.com/download/relatinglca.pdf
T.M. Connolly E.A. Boyle E. MacArthur T. Hainey J.M. Boyle A systematic literature review of empirical evidence on computer games and serious games Computers & Education 2012 59 2 661 686 10.1016/j.compedu.2012.03.004
R. Daneels N.D. Bowman D. Possler E.D. Mekler The ‘eudaimonic experience’: A scoping review of the concept in digital games research Media and Communication 2021 9 2 Article 2 10.17645/mac.v9i2.3824
F. De Grove V. Cauberghe J. Van Looy Development and validation of an instrument for measuring individual motives for playing Digital games Media Psychology 2016 19 1 101 125 10.1080/15213269.2014.902318
De Grove, F., Breuer, J., Chen, H. H., Quandt, V., Ratan, T., R., & Van Looy, J. (2017). Validating the digital games motivation scale for comparative research between countries. Communication Research Reports, 34 (1), 37–47. https://doi.org/10.1080/08824096.2016.1250070
R. Debelak U.S. Tran Comparing the effects of different smoothing algorithms on the assessment of dimensionality of ordered categorical items with parallel analysis PLOS ONE 2016 11 2 e0148143 10.1371/journal.pone.0148143 26845032 4742070
Z. Demetrovics R. Urbán K. Nagygyörgy J. Farkas D. Zilahy B. Mervó A. Reindl C. Ágoston A. Kertész E. Harmath Why do you play? The development of the motives for online gaming questionnaire (MOGQ) Behavior Research Methods 2011 43 3 814 825 10.3758/s13428-011-0091-y 21487899
A.M. Dunn E.D. Heggestad L.R. Shanock N. Theilgard Intra-individual response variability as an indicator of insufficient effort responding: Comparison to other indicators and relationships with individual differences Journal of Business and Psychology 2018 33 1 105 121 10.1007/s10869-016-9479-0
W. F Velicer Determining the number of components from the matrix of partial correlations Psychometrika 1976 41 3 321 327 10.1007/BF02293557
J.K. Flake J. Pek E. Hehman Construct validation in Social and Personality Research: Current practice and recommendations Social Psychological and Personality Science 2017 8 4 370 378 10.1177/1948550617693063
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71(3), 551–570. https://doi.org/10.1177/0013164410389489
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2013). A new look at Horn’s parallel analysis with ordinal variables. Psychological Methods, 18(4), 454–474. https://doi.org/10.1037/a0030005
D. Gentile Pathological video-game Use among Youth ages 8 to 18: A National Study Psychological Science 2009 20 5 594 602 10.1111/j.1467-9280.2009.02340.x 19476590
Goretzko, D., Pham, T. T. H., & Bühner, M. (2019). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology. https://doi.org/10.1007/s12144-019-00300-2
M. Hattori G. Zhang K.J. Preacher Multiple local solutions and geomin rotation Multivariate Behavioral Research 2017 52 6 720 731 10.1080/00273171.2017.1361312 28952786
Hilgard, J., Engelhardt, C. R., & Bartholow, B. D. (2013). Individual differences in motives, preferences, and pathology in video games: The gaming attitudes, motives, and experiences scales (GAMES). Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00608
J.R. Hipp D.J. Bauer Local solutions in the estimation of growth mixture models Psychological Methods 2006 11 1 36 53 10.1037/1082-989X.11.1.36 16594766
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447
Jansz, J., Avis, C., & Vosmeer, M. (2010). Playing the Sims2: An exploration of gender differences in players’ motivations and patterns of play. New Media & Society, 12(2), 235–251. https://doi.org/10.1177/1461444809342267
Jonason, P. K., & Webster, G. D. (2010). The dirty dozen: A concise measure of the dark triad. Psychological Assessment, 22(2), 420–432. https://doi.org/10.1037/a0019265
Jones, C., Scholes, L., Johnson, D., Katsikitis, M., & Carras, M. (2014). Gaming well: Links between videogames and flourishing mental health. Frontiers in Psychology, 5. https://www.frontiersin.org/article/. https://doi.org/10.3389/fpsyg.2014.00260
Kelley, K., & Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21(1), 69–92. https://doi.org/10.1037/a0040086
O. Király J. Billieux D.L. King R. Urbán P. Koncz E. Polgár Z. Demetrovics A comprehensive model to understand and assess the motivational background of video game use: The Gaming Motivation Inventory (GMI) Journal of Behavioral Addictions 2022 11 3 796 819 10.1556/2006.2022.00048 35939353 9872527
R. Kowert R. Festl T. Quandt Unpopular, overweight, and socially inept: Reconsidering the stereotype of online gamers Cyberpsychology Behavior and Social Networking 2014 17 3 141 146 10.1089/cyber.2013.0118 24053382
Leiner, D. J. (2019). Too fast, too straight, too Weird: Non-reactive indicators for meaningless data in internet surveys. Survey Research Methods, 229–248. https://doi.org/10.18148/SRM/2019.V13I3.7403
F.J. López-Fernández L. Mezquita M.D. Griffiths G. Ortet M.I. Ibáñez The development and validation of the Videogaming motives Questionnaire (VMQ) PLOS ONE 2020 15 10 e0240726 10.1371/journal.pone.0240726 33095762 7584249
Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Hrsg.), The Sage handbook of quantitative methodology for the social sciences (S. 175–198).
Z. Marjanovic R. Holden W. Struthers R. Cribbie E. Greenglass The inter-item standard deviation (ISD): An index that discriminates between conscientious and random responders Personality and Individual Differences 2015 84 79 83 10.1016/j.paid.2014.08.021
H.W. Marsh A.J.S. Morin P.D. Parker G. Kaur Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis Annual Review of Clinical Psychology 2014 10 1 85 110 10.1146/annurev-clinpsy-032813-153700 24313568
K.E. Masyn T.D. Little Latent class analysis and finite mixture modeling The Oxford handbook of quantitative methods 2013 Oxford University Press 551 611
J.P. Meyer A.J.S. Morin A person-centered approach to commitment research: Theory, research, and methodology Journal of Organizational Behavior 2016 37 4 584 612 10.1002/job.2085
D. Moher L. Shamseer M. Clarke D. Ghersi A. Liberati M. Petticrew P. Shekelle L.A. Stewart Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement Systematic Reviews 2015 4 1 1 10.1186/2046-4053-4-1 25554246 4320440
Morin, A. J. S., Myers, N. D., & Lee, S. (2020). Modern factor analytic techniques. Bifactor models, exploratory structural equation modeling (ESEM), and bifactor-ESEM. In G. Tenenbaum & R. C. Eklund (Hrsg.), Handbook of sport psychology (Fourth edition, S. 1044–1073). Wiley.
H. Myrseth G. Notelaers L. Å Strand E. K Borud O. K Olsen Introduction of a new instrument to measure motivation for gaming: The electronic gaming motives questionnaire: The electronic gaming motives questionnaire Addiction 2017 112 9 1658 1668 10.1111/add.13874 28543718
Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440–461. https://doi.org/10.1037/tps0000176
M.B. Oliver A.A. Raney Entertainment as pleasurable and meaningful: Identifying hedonic and eudaimonic motivations for entertainment consumption Journal of Communication 2011 61 5 984 1004 10.1111/j.1460-2466.2011.01585.x
Oliver, M. B., Bowman, N. D., Woolley, J. K., Rogers, R., Sherrick, B. I., & Chung, M. Y. (2016). Video games as meaningful entertainment experiences. Psychology of Popular Media Culture, 5(4), 390–405. https://doi.org/10.1037/ppm0000066
F. Pallavicini A. Ferrari F. Mantovani Video games for well-being: A systematic review on the application of computer games for cognitive and emotional training in the adult population Frontiers in Psychology 2018 9 2127 10.3389/fpsyg.2018.02127 30464753 6234876
F. Pallavicini A. Pepe F. Mantovani Commercial off-the-shelf video games for reducing stress and anxiety: Systematic review JMIR Mental Health 2021 8 8 e28150 10.2196/28150 34398795 8406113
Park, J., Song, Y., & Teng, C. I. (2011). Exploring the links between personality traits and motivations to play online games. Cyberpsychology Behavior and Social Networking, 14 (12), 747–751. https://doi.org/10.1089/cyber.2010.0502
Y. Poels J.H. Annema M. Verstraete B. Zaman D. De Grooff Are you a gamer? A qualititive study on the parameters for categorizing casual and hardcore gamers Iadis International Journal on www/internet 2012 1 1 16
Possler, D., Kümpel, A. S., & Unkel, J. (2020). Entertainment motivations and gaming-specific gratifications as antecedents of digital game enjoyment and appreciation. Psychology of Popular Media, 9(4), 541–552. https://doi.org/10.1037/ppm0000248
Predescu, A., & Mocanu, M. (2020). A data driven survey of video games. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–6. https://doi.org/10.1109/ECAI50035.2020.9223203
Ratan, R. A., Chen, V. H. H., Degrove, F., Breuer, J., Quandt, T., & Williams, P. (2021). Gender, gaming motives, and genre: Comparing Singaporean, German, and American players. IEEE Transactions on Games, 1–1. https://doi.org/10.1109/TG.2021.3116077
G. Reid Motivation in video games: A literature review The Computer Games Journal 2012 1 2 70 81 10.1007/BF03395967
Reinecke, L. (2009). Games and recovery: The use of video and computer games to recuperate from stress and strain. Journal of Media Psychology: Theories Methods and Applications, 21 (3), 126–142. https://doi.org/10.1027/1864-1105.21.3.126
Rigby, S., & Ryan, R. (2007). The Player Experience of Need Satisfaction (PENS): An applied model and methodology for understanding key components of the player experience. Retrieved from immersyve. com/PENS_Sept07. pdf.
Ryan, R., Rigby, C., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation & Emotion, 30(4), 344–360. https://doi.org/10.1007/s11031-006-9051-8
Scharkow, M., Festl, R., Vogelgesang, J., & Quandt, T. (2015). Beyond the core-gamer: Genre preferences and gratifications in computer games. Computers in Human Behavior, 44, 293–298. https://doi.org/10.1016/j.chb.2014.11.020
Schonlau, M., & Toepoel, V. (2015). Straightlining in web survey panels over time. Survey Research Methods, 9, 125–137. https://doi.org/10.18148/SRM/2015.V9I2.6128
Simms, L. J., Zelazny, K., Williams, T. F., & Bernstein, L. (2019). Does the number of response options matter? Psychometric perspectives using personality questionnaire data. Psychological Assessment, 31(4), 557–566. https://doi.org/10.1037/pas0000648
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and how to guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445
Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. https://doi.org/10.1080/10705511.2013.824781
L.E. van Zyl P.M. ten Klooster Exploratory structural equation modeling: Practical guidelines and tutorial with a convenient online tool for Mplus Frontiers in Psychiatry 2022 12 795672 10.3389/fpsyt.2021.795672 35069293 8779472
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Watkins, M. W. (2020). A step-by-step guide to exploratory factor analysis with R and Rstudio (1. Aufl.). Routledge. https://doi.org/10.4324/9781003120001
Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. State University of New York.
N. Yee Motivations for play in Online games CyberPsychology & Behavior 2006 9 6 772 775 10.1089/cpb.2006.9.772
Yee, N., Ducheneaut, N., & Nelson, L. (2012). Online gaming motivations scale: Development and validation. Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI ’12, 2803. https://doi.org/10.1145/2207676.2208681
Yentes, R. D., & Wilhelm, F. (2021). careless: Procedures for computing indices of careless responding [Software].