The Psychometric Properties of Dispositional Flow Scale-2 in Video Games

Authors

DOI:

https://doi.org/10.35670/1667-4545.v21.n3.36307

Keywords:

exploratory factor analysis, confirmatory factor analysis, convergent validity, discriminant validity, Mexican students, flow state, video games

Abstract

The state of flow is an important psychological characteristic of educational video games design and evaluation. This study analyzed a Mexican adaptation of the Dispositional Flow Scale-2 psychometric properties in the use of video games. Based on the information provided by a sample of 312 students, aged 16 to 34 years (M = 19.90, SD = 2.73), from a university in northeastern Mexico a confirmatory factor analysis that suggested an acceptable fit of the factorial structure, adequate convergent validity but poor discriminant validity was performed. Based on an exploratory factor analysis a re-specified model was identified, grouping 33 of the 36 items of the scale. This factorial structure, which showed an acceptable fit, adequate convergent validity and discriminant validity, suggests that scale dimensions can be grouped into antecedents and consequences of flow.

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Author Biographies

Raúl Rodríguez-Antonio, Universidad de Montemorelos

Trabaja como investigador y catedrático en la Facultad de Educación, de la Universidad de Montemorelos, México. Posee el grado de Maestría en Estadística Aplicada por el Instituto Tecnológico y de Estudios Superiores de Monterrey, México, y es candidato a obtener el grado de Doctor en Educación, por la Universidad de Montemorelos.

Jair Arody del Valle López, Universidad de Montemorelos

Coordinador para la Calidad Académica de Posgrado; Catedrático para la Dirección de Posgrado e Investigación y para la Facultad de Ingeniería y Tecnología en la Universidad de Montemorelos, Montemorelos, N. L., México.

References

Bittencourt, I. I., Freires, L., Lu, Y., Challco, G. C., Fernandes, S., Coelho, J., ... & Isotani, S. (2021). Validation and psychometric properties of the Brazilian-Portuguese dispositional Flow Scale 2 (DFS-BR). PLoS ONE 16(7), e0253044. doi: 10.1371/journal.pone.0253044

Brockmyer, J. H., Fox, C. M., Curtiss, K. A., McBroom, E., Burkhart, K. M., & Pidruzny, J. N. (2009). The development of the Game Engagement Questionnaire: A measure of engagement in video game-playing. Journal of Experimental Social Psychology, 45(4), 624-634. doi: 10.1016/j.jesp.2009.02.016

Calero, A., & Injoque-Ricle, I. (2013). Propiedades psicométricas del Inventario Breve de Experiencias Óptimas (Flow). Revista Evaluar, 13(1), 40-55. doi: 10.35670/1667-4545.v13.n1.6796

Chung, C. H., Shen, C., & Qiu, Y. Z. (2019). Students’ acceptance of gamification in Higher Education. International Journal of Game-Based Learning, 9(2), 1-19. doi: 10.4018/IJGBL.2019040101

Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10, Article 7. doi: 10.7275/jyj1-4868

Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass.

Csikszentmihalyi, M. (2014). Play and intrinsic rewards. En M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology. The collected works of Mihaly Csikszentmihalyi (pp. 135-154). Nueva York, NY: Springer. doi: 10.1007/978-94-017-9088-8

Domínguez, A., Saenz de Navarrete, J., de Marcos, L., Fernández-Sanz, L., Pagés, C., & Martínez-Herráiz, J. J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380-392. doi: 10.1016/j.compedu.2012.12.020

Erhel, S., & Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91, 106-114. doi: 10.1016/j.chb.2018.09.020

Farrel, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324-327. doi: 10.1016/j.jbusres.2009.05.003

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi: 10.1177/002224378101800104

Fu, F. -L., Su, R. -C., & Yu, S. -C. (2009). EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101-112. doi: 10.1016/j.compedu.2008.07.004

García-Calvo, T., Jiménez-Castuera, R., Santos-Rosa-Ruano, F. J., Reina-Vaíllo, R., & Cervelló-Gimeno, E. (2008). Psychometric properties of the Spanish version of the Flow State Scale. The Spanish Journal of Psychology, 11(2), 660-669. doi: 10.1017/s1138741600004662

Giasiranis, S., & Sofos, L. (2017). Flow experience and educational effectiveness of teaching informatics using AR. Educational Technology & Society, 20(4), 78-88. Recuperado de http://www.jstor.org

Gonzalez, C. (Ed.). (2013). Student usability in educational software and games: Improving experiences. Hershey, PA: IGI Global. doi: 10.4018/978-1-4666-1987-6

Gutierrez, J. P. (2021). Do game transfer phenomena lead to flow? An investigation of in-game and out-game immersion among MOBA gamers. Computers in Human Behavior Reports, 3, Article 100079. doi: 10.1016/j.chbr.2021.100079

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7a ed.). Harlow, Reino Unido: Pearson Education Limited.

Hamari, J., & Koivisto, J. (2014). Measuring flow in gamification: Dispositional Flow Scale-2. Computers in Human Behavior, 40, 133-143. doi: 10.1016/j.chb.2014.07.048

Hassan, L., Jylhä, H., Sjöblom, M., & Hamari, J. (2020). Flow in VR: A study on the relationships between preconditions, experience and continued use. Proceedings of the 53rd Hawaii International Conference on System Sciences (pp. 1196-1205). doi: 10.24251/HICSS.2020.149

Hu, L. -T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. doi: 10.1080/10705519909540118

Hwang, G. -J., Chiu, L. -Y., & Chen, C. -H. (2015). A contextual game-based learning approach to improving students´ inquiry-based learning performance in social studies courses. Computers & Education, 81, 13-25. doi: 10.1016/j.compedu.2014.09.006

Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The Flow State Scale-2 and Dispositional Flow Scale-2. Journal of Sport & Exercise Psychology, 24(2), 133-150. doi: 10.1123/jsep.24.2.133

Jackson, S. A., Eklund R. C., & Martin, A. J. (23 de junio de 2020). Flow Scales. Mind Garden. Recuperado de https://www.mindgarden.com/100-flow-scales

Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport & Exercise Psychology, 18(1), 17-35. doi: 10.1123/jsep.18.1.17

Joo, Y. J., Oh, E., & Kim, S. M. (2015). Motivation, instructional design, flow, and academic achievement at a Korean online university: A structural equation modeling study. Journal of Computing in Higher Education, 27(1), 28-46. doi: 10.1007/s12528-015-9090-9

Khoshnoud, S., Alvarez-Igarzábal, F., & Wittmann, M. (2020). Peripheral-physiological and neural correlates of the flow experience while playing video games: A comprehensive review. PeerJ, 8, e10520. doi: 10.7717/peerj.10520

Kiili, K., de Freitas, S., Arnab, S., & Lainema, T. (2012). The design principles for flow experience in educational games. Procedia Computer Science, 15, 78-91. doi: 10.1016/j.procs.2012.10.060

Kiili, K., & Lainema, T. (2008). Foundation for measuring engagement in educational games. Journal of Interactive Learning Research, 19(3), 469-488. Recuperado de https://www.learntechlib.org

Kline, R. B. (2011). Principles and practice of structural equation modeling (3a ed.). New York, NY: The Guilford Press.

Marinho, A., Oliveira, W., Bittencourt, I. I., & Dermeval, D. (2019). Does gamification improve flow experience in classroom? An analysis of gamer types in collaborative and competitive settings. Brazilian Journal of Computers in Education (Revista Brasileira de Informática na Educação - RBIE), 27(2), 40-68. doi: 10.5753/RBIE.2019.27.02.40

Mesurado, B. (2010). La experiencia de flow o experiencia óptima en el ámbito educativo. Revista Latinoamericana de Psicología, 42(2), 183-192. Recuperado de http://revistalatinoamericanadepsicologia.konradlorenz.edu.co

Montes-González, J. A., Ochoa-Angrino, S., Baldeón-Padilla, D. S., & Bonilla-Sáenz, M. (2018). Videojuegos educativos y pensamiento científico: Análisis a partir de los componentes cognitivos, metacognitivos y motivacionales. Educación y Educadores, 21(3), 388-408. doi: 10.5294/edu.2018.21.3.2

Moral de la Rubia, J. (2019). Revisión de los criterios para validez convergente estimada a través de la Varianza Media Extraída. Psychologia, 13(2), 25-41. doi: 10.21500/19002386.4119

Prensky, M. (2001). Digital game-based learning. New York, NY: McGraw-Hill.

Procci, K., Singer, A. R., Levy, K. R., & Bowers, C. (2012). Measuring the flow experience of gamers: An evaluation of the DFS-2. Computers in Human Behavior, 28(6), 2306-2312. doi: 10.1016/j.chb.2012.06.039

Revelle, W. (2021). psych: Procedures for psychological, psychometric, and personality research (R package 2.1.9). [Software de cómputo]. Recuperado de https://CRAN.R-project.org/package=psych

Rijavec, M., Ljubin-Golub, T., Jurčec, L., & Olčar, D. (2017). Working part-time during studies: The role of flow in students’ well-being and academic achievement. Croatian Journal of Education, 19. doi: 10.15516/cje.v19i0.2724

Riva, E. F. M., Riva, G., Talò, C., Boffi, M., Rainisio, N., Pola, L., ... & Inghilleri, P. (2017). Measuring dispositional flow: Validity and reliability of the Dispositional Flow State Scale 2, Italian version. PLoS ONE, 12(9), e0182201. doi: 10.1371/journal.pone.0182201

Rodríguez-Ardura, I., & Meseguer-Artola, A. (2017). Flow in e-learning: What drives it and why it matters. British Journal of Educational Technology, 48(4), 899-915. doi: 10.1111/bjet.12480

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. doi: 10.18637/jss.v048.i02

Satorra, A., & Bentler, P. M. (1994). Correction to test statistics and standard errors in covariance structure analysis. En A. von Eye & C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research (pp. 399-419). Thousand Oaks, CA: Sage.

Shu-Hui, C., Wann-Yih, W., & Dennison, J. (2018). Validation of EGameFlow: A self-report scale for measuring user experience in video game play. Computers in Entertainment, 16(3), 1-15. doi: 10.1145/3238249

Stavrou, N. A., & Zervas, Y. (2004). Confirmatory factor analysis of the flow state scale in sports. International Journal of Sport and Exercise Psychology, 2(2), 161-181. doi: 10.1080/1612197X.2004.9671739

Swann, C., Crust, L., & Vella, S. A. (2017). New directions in the psychology of optimal performance in sport:Flow and clutch states. Current Opinion in Psychology, 16, 48-53. doi: 10.1016/j.copsyc.2017.03.032

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6a ed.). Upper Saddle River, NJ: Pearson. Taber, K. S. (2018). The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. doi: 10.1007/s11165-016-9602-2

Wang, C. K. J., Liu, W. C., & Khoo, A. (2009). The psychometric properties of Dispositional Flow Scale-2 in internet gaming. Current Psychology, 28(3), 194-201. doi: 10.1007/s12144-009-9058-x

Published

2021-12-24

How to Cite

Rodríguez-Antonio, R., & del Valle López, J. A. (2021). The Psychometric Properties of Dispositional Flow Scale-2 in Video Games. Revista Evaluar, 21(3), 63–80. https://doi.org/10.35670/1667-4545.v21.n3.36307

Issue

Section

Investigaciones originales