Free Access
Mov Sport Sci/Sci Mot
Number 105, 2019
Emotions et régulation émotionnelle en contexte sportif interpersonnel ou intergroupe
Page(s) 79 - 88
Published online 03 May 2019
  • Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural Equation Modeling: A Multidisciplinary Journal , 21(3), 329–341. doi: 10.1080/10705511.2014.915181. [CrossRef] [Google Scholar]
  • Beedie, C.J., Terry, P.C., & Lane, A.M. (2000). The profile of mood states and athletic performance: Two meta-analyses. Journal of Applied Sport Psychology , 12(1), 49–68. doi: 10.1080/10413200008404213. [Google Scholar]
  • Campo, M., Laborde, S., & Weckemann, S. (2015). Emotional intelligence training: Implications for performance and health. In A.M. Colombus (Ed.), Advances in psychology research (Vol. 101, pp. 75–92). Hauppauge, NY: Nova Science Publishers. [Google Scholar]
  • Cece, V., Lienhart, N., Nicaise, V., Guillet-Descas, E., & Martinent, G. (2018). The role of emotional intelligence and emotional regulation on the emotional trajectories of young athletes involving in intense training centers. Journal of Sport and Exercise Psychology , in revision. [Google Scholar]
  • Cerin, E., Szabo, A., Hunt, N., & Williams, C. (2000). Temporal patterning of competitive emotions: A critical review. Journal of Sports Sciences , 18(8), 605–626. doi: 10.1080/02640410050082314. [CrossRef] [PubMed] [Google Scholar]
  • Collins, L.M., & Wugalter, S.E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research , 27, 131–157. doi: 10.1207/s15327906mbr2701_8. [Google Scholar]
  • Diggle, P.J., Liang, K.L., & Zeger, S.L. (1994). Analysis of longitudinal data. Oxford, England: Clarendon Press. [Google Scholar]
  • Doron, J., & Martinent, G. (2016). Trajectories of psychological states of women elite fencers during the final stages of international matches. Journal of Sports Sciences , 34(9), 836–842. doi: 10.1080/02640414.2015.1075056. [CrossRef] [PubMed] [Google Scholar]
  • Doron, J., & Martinent, G. (2017). Appraisal, coping, emotion, and performance during elite fencing matches: A random coefficient regression model approach. Scandinavian Journal of Medicine & Science in Sports , 27(9), 1015–1025. doi: 10.1111/sms.12711. [CrossRef] [PubMed] [Google Scholar]
  • Fernando, J.W., Kashima, Y., & Laham, S.M. (2014). Multiple emotions: A person-centered approach to the relationship between intergroup emotion and action orientation. Emotion , 14(4), 722. doi: 10.1037/a0036103. [CrossRef] [PubMed] [Google Scholar]
  • Folkman, S. (2013). Stress: Appraisal and coping. In M.D. Gellman & J.R. Turner (Eds.), Encyclopedia of behavioral medicine (pp. 1913–1915). NY: Springer. [CrossRef] [Google Scholar]
  • Gaudreau, P., Amiot, C.E., & Vallerand, R.J. (2009). Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents. Developmental Psychology , 45(2), 307. doi: 10.1037/a0014134. [CrossRef] [PubMed] [Google Scholar]
  • Golaz, V., & Bringé, A. (2009). Apports et enjeux de l’analyse multiniveau en démographie. Actes des Journées de Méthodologie Statistique. INSEE (3–5 juin 2009). [Google Scholar]
  • Gross, J.J. (1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology , 2(3), 271. doi: 10.1037/1089-2680.2.3.271. [CrossRef] [Google Scholar]
  • Hanin, Y.L. (2007). Emotions in sport: Current issues and perspectives. In G. Tenenbaum & R.C. Eklund (Eds.), Handbook of sport psychology (Vol. 3, pp. 31–58). Hoboken: NJ: John Wiley & Sons. [Google Scholar]
  • Hox, J. (2002). Multilevel analyses: Techniques and applications. Mahwah, NJ: Erlbaum. [CrossRef] [Google Scholar]
  • Jeu, B. (1987). Analysis of sport. Paris : Presses universitaires de France. [Google Scholar]
  • Jung, T., & Wickrama, K.A.S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass , 2, 302–317. doi: 10.1111/j.1751-9004.2007.00054.x. [Google Scholar]
  • Lane, A., Thelwell, R., & Devonport, T. (2009). Emotional intelligence and mood states associated with optimal performance. E-journal of Applied Psychology , 5(1), 67–73. doi: 10.7790/ejap.v5i1.123. [CrossRef] [Google Scholar]
  • Lanza, S.T., Patrick, M.E., & Maggs, J.L. (2010). Latent transition analysis: Benefits of a latent variable approach to modeling transitions in substance use. Journal of Drug Issues , 40, 93–120. doi: 10.1177/002204261004000106. [CrossRef] [PubMed] [Google Scholar]
  • Laursen, B.P., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly , 52(3), 377–389. doi: 10.1353/mpq.2006.0029. [Google Scholar]
  • Lazarus, R.S. (2000). How emotions influence performance in competitive sports. Sport Psychologist , 14(3), 229–252. doi: 10.1123/tsp.14.3.229. [CrossRef] [Google Scholar]
  • Louvet, B., Gaudreau, P., Menaut, A., Genty, J., & Deneuve, P. (2007). Longitudinal patterns of stability and change in coping across three competitions: A latent class growth analysis. Journal of Sport and Exercise Psychology , 29(1), 100–117. doi: 10.1123/jsep.29.1.100. [CrossRef] [Google Scholar]
  • Louvet, B., Gaudreau, P., Menaut, A., Genty, J., & Deneuve, P. (2009). Revisiting the changing and stable properties of coping utilization using latent class growth analysis: A longitudinal investigation with soccer referees. Psychology of Sport and Exercise , 10(1), 124–135. doi: 10.1016/j.psychsport.2008.02.002. [Google Scholar]
  • Magidson, J., & Vermunt, J.K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing , 20, 37–44. [Google Scholar]
  • Marsh, H.W., & O’Mara, A. (2008). Reciprocal effects between academic self-concept, self-esteem, achievement, and attainment over seven adolescent years: Unidimensional and multidimensional perspectives of self-concept. Personality and Social Psychology Bulletin , 34, 542–552. doi: 10.1177/0146167207312313. [CrossRef] [Google Scholar]
  • Martinent, G., Campo, M., & Ferrand, C. (2012). A descriptive study of emotional process during competition: Nature, frequency, direction, duration and co-occurrence of discrete emotions. Psychology of Sport and Exercise , 13(2), 142–151. doi: 10.1016/j.psychsport.2011.10.006. [Google Scholar]
  • Martinent, G., Ledos, S., & Nicolas, M. (2014). Méthodologies à la 1e et à la 3e personnes. Quelles articulations possibles dans le champ de la psychologie des émotions en contexte compétitif ? Dans M. Quidu (Ed.), Les Sciences du sport en mouvement. Tome II : innovations théoriques en STAPS et implications pratiques en EPS (pp. 291–306). Paris : L’Harmattan. [Google Scholar]
  • Martinent, G., Ledos, S., Ferrand, C., Campo, M., & Nicolas, M. (2015). Athletes’ regulation of emotions experienced during competition: A naturalistic video-assisted study. Sport, Exercise, and Performance Psychology , 4(3), 188–205. doi: 10.1037/spy0000037. [Google Scholar]
  • Martinent, G., & Nicolas, M. (2017). Athletes’ affective profiles within competition situations: A two-wave study. Sport, Exercise, and Performance Psychology , 6(2), 143. doi: 10.1037/spy0000085. [Google Scholar]
  • Martinent, G., Gareau, A., Lienhart, N., Nicaise, V., & Guillet-Descas, E. (2018). Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings. Psychology of Sport and Exercise , 35, 198–206. doi: 10.1016/j.psychsport.2018.01.001. [Google Scholar]
  • Mayer, J.D. (2009). Personal intelligence expressed: A theoretical analysis. Review of General Psychology , 13(1), 46–58. doi: 10.1037/a0014229. [CrossRef] [Google Scholar]
  • 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. doi: 10.1111/j.1530-0277.2000.tb02070.x. [CrossRef] [Google Scholar]
  • Nicholls, A.R., Holt, N.L., Polman, R.C., & Bloomfield, J. (2006). Stressors, coping, and coping effectiveness among professional rugby union players. The Sport Psychologist , 20(3), 314–329. doi: 10.1123/tsp.20.3.314. [Google Scholar]
  • Nylund, K. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. Thèse de doctorat, University of California, Los Angeles. [Google Scholar]
  • Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal , 21(3), 439–454. doi: 10.1080/10705511.2014.915375. [CrossRef] [Google Scholar]
  • Reinboth, M., Duda, J.L., & Ntoumanis, N. (2004). Dimensions of coaching behavior, need satisfaction, and the psychological and physical welfare of young athletes. Motivation and Emotion , 28(3), 297–313. doi: 10.1023/B:MOEM.0000040156.81924.b8. [Google Scholar]
  • Riemer, H.A., & Chelladurai, P. (1998). Development of the athlete satisfaction questionnaire (ASQ). Journal of Sport and Exercise Psychology , 20(2), 127–156. doi: 10.1123/jsep.20.2.127. [CrossRef] [Google Scholar]
  • Rindfleisch, A., Malter, A.J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research , 45(3), 261–279. doi: 10.1509/jmkr.45.3.261. [CrossRef] [Google Scholar]
  • Schutte, N.S., Malouff, J.M., Simunek, M., McKenley, J., & Hollander, S. (2002). Characteristic emotional intelligence and emotional well-being. Cognition & Emotion , 16(6), 769–785. doi: 10.1080/02699930143000482. [Google Scholar]
  • Singer, J.D., & Willett, J.B. (2003). Doing data analysis with the multilevel model for change. In Applied longitudinal data analysis: Modeling change and event occurrence (pp. 96–97). London: Oxford University. [Google Scholar]
  • Vacher, P., Nicolas, M., Martinent, G., & Mourot, L. (2017). Changes of swimmers’ emotional states during the preparation of national championship: Do recovery-stress states matter ? Frontiers in Psychology , 8, 1043. doi: 10.3389/fpsyg.2017.01043/full. [CrossRef] [PubMed] [Google Scholar]
  • Wright, D.B., & London, K. (2009). Multilevel modelling: Beyond the basic applications. British Journal of Mathematical and Statistical Psychology , 62, 439–456. doi: 10.1348/000711008X327632. [CrossRef] [Google Scholar]

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