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      Development of retirement age prediction model for athletes = Development of retirement age prediction model for athletes

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      https://www.riss.kr/link?id=A106533081

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      다국어 초록 (Multilingual Abstract)

      The purpose of this study is to present retirement age predictive functions of athletes that can be utilized as data to reduce psychological shock of athletes from retirement and prepare for the future. To accomplish such purpose, athletes who retired as an undergraduate, unemployed or professional registered on the athlete registration system of the Korean Sport & Olympic Committee for three years were selected as the population. Stratified sampling was used for convenience sampling. Content validity of a retirement factor questionnaire was examined by consulting with experts. Opinions of 72 retirees were collected through an open questionnaire, and samples of 260 persons were used in the first and second parts after consulting with experts based on first sample data. The stepwise regression analysis method was applied to develop reliable and valid retirement predictive regression function. The degree of relevance was shown by multiple correlation coefficient between predicted age calculated by the predictive function and actual retirement age. Significance level was .05 for all tests. The 8 predictive function are presented according to the procedure above. Y<sub>1</sub>(retirement age of female athlete)= 24.097+1.778*(physical limitation 11)-1.142*(job plan29), Y<sub>2</sub>(retirement age of male athlete)= 23.498+1.334*(popularity 2)-1.126*(exercise attitude 20)+1.021*(competitiveness 7)-1.020* (job plan 29)+0.871*(economy 18)-1.905*(administration 22)-1.024*(administration 23)+.778*(interpersonal relationship 13), Y<sub>3</sub>(retirement age of combat sports)=23.158+.688*(physical limitation 11)-1.790*(job plan 29)+0.960*(popularity 1)-0.656*(exercise attitude 16)+0.747*(job plan 33)+0.643*(economy 18)-0.461*(administration 23)+.606*(injury 46), Y<sub>4</sub>(retirement age of non-combat sports)=20.741+ 1.637*(popularity 2)-1.270*(exercise attitude 20)+0.942*(competitiveness 7)+2.061*(family 5)-3.291*(administration 21)+1.082*(administration 25)+1.192(interpersonal relationship 8), Y<sub>5</sub>(retirement age of individual sports)=27.414-1.295*(job plan 29)+1.463*(physical limitation 15)+0.972*(popularity 1)-0.639* (exercise 16), Y<sub>6</sub>(retirement age of group sports)= 21.950+1.950*(popularity 2)-1.318*(exercise attitude 20)+4.635*(interpersonal relationship 6)-3.337*(addiction 41), Y<sub>7</sub>(retirement age of undergraduate athlete)= 21.950+1.950*(popularity 2)-1.318*(exercise attitude 20)+4.635*(interpersonal relationship 6) -3.337*(addiction 41), Y<sub>8</sub>(retirement age of unemployed and professional athlete)= 27.808-0.874* (exercise attitude 19)+1.287*(competitiveness 8)-1.402*(administration 21)+0.757*(popularity 2).
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      The purpose of this study is to present retirement age predictive functions of athletes that can be utilized as data to reduce psychological shock of athletes from retirement and prepare for the future. To accomplish such purpose, athletes who retired...

      The purpose of this study is to present retirement age predictive functions of athletes that can be utilized as data to reduce psychological shock of athletes from retirement and prepare for the future. To accomplish such purpose, athletes who retired as an undergraduate, unemployed or professional registered on the athlete registration system of the Korean Sport & Olympic Committee for three years were selected as the population. Stratified sampling was used for convenience sampling. Content validity of a retirement factor questionnaire was examined by consulting with experts. Opinions of 72 retirees were collected through an open questionnaire, and samples of 260 persons were used in the first and second parts after consulting with experts based on first sample data. The stepwise regression analysis method was applied to develop reliable and valid retirement predictive regression function. The degree of relevance was shown by multiple correlation coefficient between predicted age calculated by the predictive function and actual retirement age. Significance level was .05 for all tests. The 8 predictive function are presented according to the procedure above. Y<sub>1</sub>(retirement age of female athlete)= 24.097+1.778*(physical limitation 11)-1.142*(job plan29), Y<sub>2</sub>(retirement age of male athlete)= 23.498+1.334*(popularity 2)-1.126*(exercise attitude 20)+1.021*(competitiveness 7)-1.020* (job plan 29)+0.871*(economy 18)-1.905*(administration 22)-1.024*(administration 23)+.778*(interpersonal relationship 13), Y<sub>3</sub>(retirement age of combat sports)=23.158+.688*(physical limitation 11)-1.790*(job plan 29)+0.960*(popularity 1)-0.656*(exercise attitude 16)+0.747*(job plan 33)+0.643*(economy 18)-0.461*(administration 23)+.606*(injury 46), Y<sub>4</sub>(retirement age of non-combat sports)=20.741+ 1.637*(popularity 2)-1.270*(exercise attitude 20)+0.942*(competitiveness 7)+2.061*(family 5)-3.291*(administration 21)+1.082*(administration 25)+1.192(interpersonal relationship 8), Y<sub>5</sub>(retirement age of individual sports)=27.414-1.295*(job plan 29)+1.463*(physical limitation 15)+0.972*(popularity 1)-0.639* (exercise 16), Y<sub>6</sub>(retirement age of group sports)= 21.950+1.950*(popularity 2)-1.318*(exercise attitude 20)+4.635*(interpersonal relationship 6)-3.337*(addiction 41), Y<sub>7</sub>(retirement age of undergraduate athlete)= 21.950+1.950*(popularity 2)-1.318*(exercise attitude 20)+4.635*(interpersonal relationship 6) -3.337*(addiction 41), Y<sub>8</sub>(retirement age of unemployed and professional athlete)= 27.808-0.874* (exercise attitude 19)+1.287*(competitiveness 8)-1.402*(administration 21)+0.757*(popularity 2).

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