Profiles of Flow Experience Among Chinese College Basketball Players: A Latent Profile Analysis
DOI:
https://doi.org/10.12775/QS.2026.77.76231Keywords
flow experience, college basketball players, latent profile analysis, skill-challenge balance, coach leadershipAbstract
Background. Flow is an optimal psychological state that facilitates athletic performance and engagement. Prior research has mainly used variable-centered approaches to examine flow antecedents, leaving unclear whether athletes form distinct subgroups in flow patterns.
Aim. This study identified latent flow profiles among Chinese college basketball players and examined whether these profiles differ in key situational and cognitive antecedents.
Material and methods. In total, 100 college basketball players from Chongqing, Sichuan, and Beijing completed measures of flow, coach leadership and support, environmental and resource guarantee, training task design, team interaction (harmonious relationship, cooperative spirit), and skill-challenge balance. Latent profile analysis used mclust in R; t-tests and one-way ANOVAs compared profiles on flow and antecedent variables.
Results. Two distinct profiles were identified: a low-flow group (n = 70, 70%) and a high-flow group (n = 30, 30%). The high-flow group scored higher on all four flow items (all p < 0.001; Cohen's d = 0.72–1.06) and reported higher coach leadership and support, skill-challenge balance, team interaction, training task design, and environmental and resource guarantee (all p < 0.001; η² = 0.30–0.59).
Conclusions. College basketball players can be classified into two distinct flow profiles. Coaches should adopt differentiated interventions, particularly for low-flow athletes.
References
Aydın, M., & Sarı, İ. (2021). The relationship between coach-created motivational climate and athletes' challenge and threat perceptions. Quality in Sport, 7(2), 24-37. https://doi.org/10.12775/qs.2021.008
Bartlett, M. S. (1954). A note on the multiplying factors for various χ2 approximations. Journal of the Royal Statistical Society: Series B, 16(2), 296-298. https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
Bergman, L. R., & Trost, K. (2006). The person-oriented versus the variable-oriented approach: Are they complementary, opposites, or exploring different worlds? Merrill-Palmer Quarterly, 52(3), 601-632. https://doi.org/10.1353/mpq.2006.0023
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
de Sampaio Barros, M. F., Stefano Filho, C. A., Menezes, L. T. de, Araújo-Moreira, F. M., Trevelin, L. C., Pimentel Maia, R., Radel, R., & Castellano, G. (2024). Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: Low- and high-altitude slackline walking. PeerJ, 12, e17743. https://doi.org/10.7717/peerj.17743
Fong, C. J., Zaleski, D. J., & Leach, J. K. (2015). The challenge-skill balance and antecedents of flow: A meta-analytic investigation. The Journal of Positive Psychology, 10(5), 425-446. https://doi.org/10.1080/17439760.2014.967799
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192-3198. https://doi.org/10.1016/j.jbusres.2015.12.008
Huang, H., Sha, X., Zhong, C., Ma, N., & Shui, Y. (2025). Association of football athlete engagement profiles with adolescent mental health: A latent profile analysis. BMC Public Health, 25(1), 2045. https://doi.org/10.1186/s12889-025-23239-5
Huang, X., & Lang, J. (2026). Understanding flow experience among college basketball players: The mediating role of perceived skill-challenge balance. Frontiers in Humanities and Social Sciences, 6(7), 51-61. https://doi.org/10.54691/e6mt6j95
Jackman, P. C., Swann, C., & Crust, L. (2016). Exploring athletes' perceptions of the relationship between mental toughness and dispositional flow in sport. Psychology of Sport and Exercise, 27, 56-65. https://doi.org/10.1016/j.psychsport.2016.07.007
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 and Exercise Psychology, 24(2), 133-150. https://doi.org/10.1123/jsep.24.2.133
Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport and Exercise Psychology, 18(1), 17-35. https://doi.org/10.1123/jsep.18.1.17
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. https://doi.org/10.1016/j.tourman.2021.104330
Liu, R., Wang, S., & Li, J. (2025). How coach leadership behavior influences athletes' performance: The chain-mediated role of the coach-athlete relationship. Frontiers in Psychology, 15, 1500867. https://doi.org/10.3389/fpsyg.2024.1500867
Mageau, G. A., & Vallerand, R. J. (2003). The coach-athlete relationship: A motivational model. Journal of Sports Sciences, 21(11), 883-904. https://doi.org/10.1080/0264041031000140374
Martinent, G., & Nicolas, M. (2017). Athletes' affective profiles within competition situations: A two-wave study. Sport, Exercise, and Performance Psychology, 6(2), 143-157. https://doi.org/10.1037/spy0000085
Martiny, L. E., Theil, L. Z., Maciel Neto, E., Dias, G., Ferreira, J. P., & Mendes, R. (2023). Effects of flow states on elite athletes in team sports: A systematic review. Revista Foco, 16(8), e2910. https://doi.org/10.54751/revistafoco.v16n8-118
Muthén, B., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24(6), 882-891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569. https://doi.org/10.1080/10705510701575396
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 289-317. https://doi.org/10.32614/RJ-2016-021
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
Swann, C., Keegan, R. J., Piggott, D., & Crust, L. (2012). A systematic review of the experience, occurrence, and controllability of flow states in elite sport. Psychology of Sport and Exercise, 13(6), 807-819. https://doi.org/10.1016/j.psychsport.2012.05.006
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
World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191-2194. https://doi.org/10.1001/jama.2013.281053
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Xiaoqing Huang, Jun Lang
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Stats
Number of views and downloads: 10
Number of citations: 0
