Ali Zarei Beidsorkhi; Loghman Keshavarz; Abolfazl Farahani
Abstract
parameters. The research method was exploratory mixed (qualitative-quantitative). Statistical sample of research in qualitative part (purposive sampling) to theoretical saturation of data consisted of 21 top level coaches, basketball experts and talent specialists and in quantitative section (random) ...
Read More
parameters. The research method was exploratory mixed (qualitative-quantitative). Statistical sample of research in qualitative part (purposive sampling) to theoretical saturation of data consisted of 21 top level coaches, basketball experts and talent specialists and in quantitative section (random) 242 international basketball coaches ( FIBA (n = 7), Grade 1 (n = 60) and Grade 2 (n = 175), respectively. In-depth interviews and researcher-made questionnaires were used for data collection. The validity and reliability of the interviews and questionnaires were evaluated and verified using expert opinion, two coders' agreement, convergent validity, Cronbach's alpha, and hybrid reliability. Coding and categorization were used to draw conclusions about the content of the interviews. As a result of the initial analysis of the interviews, 10 categories, 30 subcategories and 101 key concepts were extracted from the transcripts through open coding of the interviews and finally resulted in a conceptual model with 9 categories and 33 subcategories. Testing of talent parameters in basketball derived from interview analysis using second-order confirmatory factor analysis showed physiological (β = 0.79) and psychological (β = 0.49) parameters. Also, the conceptual model test using structural equation modeling showed that among the constructs derived from the qualitative sector in terms of culture (β = 0.35, P = 0.01), talent centers (β = 0.23). , P = 0.02, economic status (β = 0.22, P = 0.02), and management (P = 0.03, β = 0.13) had the greatest role in talent scoring in basketball.
