IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0278572.html
   My bibliography  Save this article

A cluster analysis of high-performance female team players’ perceived motivational climate: Implications on perceived motor competence and autonomous behaviour

Author

Listed:
  • J Arturo Abraldes
  • Luis Conte Marín
  • David Manzano-Sánchez
  • Manuel Gómez-López
  • Bernardino J Sánchez-Alcaraz

Abstract

High performance sport for females is an area which is gaining more and more relevance today, but which hasn’t received the same research interest as sport for males. The aim of the present study was to analyse the motivational climate perceived by high performance female athletes and the implications on perceived motor competence and autonomous behaviour and check the differences according category, sport experience and training hours in performance and master climate. The participants were 615 female athletes who practice top level team sports, with ages comprised of 16 to 38 (M = 22,10; SD = 4,91). The sample consisted of two different categories: junior (n = 242) and senior (n = 373). These players participated in different team sports, specifically football, handball, basketball and volleyball, training between 6 and 24 hours a week (M = 8,34; DT = 4,33). The variables measured were: perceived motivational climate in sport, autonomous behaviour and perceived motor competence. A cluster analysis was carried out using, as a variable, the perceived motivational climate, and showing the existence of two profiles, one related to ego and the other to task. The multivariate analysis showed that the profile orientated towards the task had significant differences compared to the autonomous behaviour (M = 4.66 vs M = 3.41). At the same time an analysis was carried out looking at different social demographic variables, revealing how there were differences in the sports experience (those participants with more than ten years’ experience were more orientated towards ego, compared to those with less than ten years’ experience) and the category (junior athletes were more orientated towards the task, compared to senior athletes). It was concluded that a greater orientation towards the task can lead to an improvement in the perception of motor competence, with the youngest and least experienced athletes being the most prominent in this category.

Suggested Citation

  • J Arturo Abraldes & Luis Conte Marín & David Manzano-Sánchez & Manuel Gómez-López & Bernardino J Sánchez-Alcaraz, 2022. "A cluster analysis of high-performance female team players’ perceived motivational climate: Implications on perceived motor competence and autonomous behaviour," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-12, December.
  • Handle: RePEc:plo:pone00:0278572
    DOI: 10.1371/journal.pone.0278572
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278572
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0278572&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0278572?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    2. David Manzano-Sánchez & Lucas Postigo-Pérez & Manuel Gómez-López & Alfonso Valero-Valenzuela, 2020. "Study of the Motivation of Spanish Amateur Runners Based on Training Patterns and Gender," IJERPH, MDPI, vol. 17(21), pages 1-12, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Bank business models, size, and profitability," Finance Research Letters, Elsevier, vol. 53(C).
    2. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    3. Marcin Gąsior, 2021. "Environmental Attitudes and Willingness to Purchase Online—Classification Approach," Sustainability, MDPI, vol. 13(15), pages 1-17, August.
    4. Roopam Shukla & Ankit Agarwal & Kamna Sachdeva & Juergen Kurths & P. K. Joshi, 2019. "Climate change perception: an analysis of climate change and risk perceptions among farmer types of Indian Western Himalayas," Climatic Change, Springer, vol. 152(1), pages 103-119, January.
    5. Saemi Shin & Won Suck Yoon & Sang-Hoon Byeon, 2022. "Trends in Occupational Infectious Diseases in South Korea and Classification of Industries According to the Risk of Biological Hazards Using K-Means Clustering," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    6. Igor Kravchuk & Viktoriia Stoika, 2021. "Business Μodels of Βanks for the Financial Markets in the EU," European Research Studies Journal, European Research Studies Journal, vol. 0(2 - Part ), pages 371-382.
    7. Song He & Xinyu Song & Xiaoxi Yang & Jijun Yu & Yuqi Wen & Lianlian Wu & Bowei Yan & Jiannan Feng & Xiaochen Bo, 2021. "COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-10, March.
    8. Jihane El Ouadi & Hanae Errousso & Nicolas Malhene & Siham Benhadou & Hicham Medromi, 2022. "A machine-learning based hybrid algorithm for strategic location of urban bundling hubs to support shared public transport," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3215-3258, October.
    9. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    10. Chuyuan Lin & Ying Yu & Lucas Y. Wu & Jiguo Cao, 2023. "Unsupervised learning on U.S. weather forecast performance," Computational Statistics, Springer, vol. 38(3), pages 1193-1213, September.
    11. Fabián Humberto Marín-González & Iago Portela-Pino & Juan Pedro Fuentes-García & María José Martínez-Patiño, 2024. "Analysis of Socio-Emotional Competencies as a Key Dimension for Sustainability in Colombian Elite Athletes," Sustainability, MDPI, vol. 16(5), pages 1-18, March.
    12. Kreitmair, Ursula & Bower-Bir, Jacob, 2021. "Too different to solve climate change? Experimental evidence on the effects of production and benefit heterogeneity on collective action," Ecological Economics, Elsevier, vol. 184(C).
    13. Getaneh Addis Tessema & Jan van der Borg & Anton Van Rompaey & Steven Van Passel & Enyew Adgo & Amare Sewnet Minale & Kerebih Asrese & Amaury Frankl & Jean Poesen, 2022. "Benefit Segmentation of Tourists to Geosites and Its Implications for Sustainable Development of Geotourism in the Southern Lake Tana Region, Ethiopia," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    14. Manuel Mosqueira-Ourens & José M. Sánchez-Sáez & Aitor Pérez-Morcillo & Laura Ramos-Petersen & Andrés López-Del-Amo & José L. Tuimil & Adrián Varela-Sanz, 2021. "Effects of a 48-Day Home Quarantine during the Covid-19 Pandemic on the First Outdoor Running Session among Recreational Runners in Spain," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
    15. Wu, Tong & Rocha, Juan C. & Berry, Kevin & Chaigneau, Tomas & Hamann, Maike & Lindkvist, Emilie & Qiu, Jiangxiao & Schill, Caroline & Shepon, Alon & Crépin, Anne-Sophie & Folke, Carl, 2024. "Triple Bottom Line or Trilemma? Global Tradeoffs Between Prosperity, Inequality, and the Environment," World Development, Elsevier, vol. 178(C).
    16. Bita Mashayekhi & Kaveh Asiaei & Zabihollah Rezaee & Amin Jahangard & Milad Samavat & Saeid Homayoun, 2024. "The relative importance of ESG pillars: A two‐step machine learning and analytical framework," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 5404-5420, October.
    17. Petricli, Gulcan & Inkaya, Tulin & Gokay Emel, Gul, 2024. "Identifying green citizen typologies by mining household-level survey data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    18. Young Hyun Kim & Kug Jin Jeon & Chena Lee & Yoon Joo Choi & Hoi-In Jung & Sang-Sun Han, 2021. "Analysis of the mandibular canal course using unsupervised machine learning algorithm," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-13, November.
    19. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    20. Ben Beck & Meghan Winters & Trisalyn Nelson & Chris Pettit & Simone Z Leao & Meead Saberi & Jason Thompson & Sachith Seneviratne & Kerry Nice & Mark Stevenson, 2023. "Developing urban biking typologies: Quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics," Environment and Planning B, , vol. 50(1), pages 7-23, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0278572. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.