메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
최영철 (한양대학교) 정우진 (동국대학교)
저널정보
한국체육과학회 한국체육과학회지 한국체육과학회지 제24권 제4호 (자연과학편)
발행연도
2015.8
수록면
1,573 - 1,583 (11page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
This research eventually aims at building up an academic foundation to provide proper information at a time when how to boost a screen golf course is approached from the viewpoint of selection attributes. In other words, this research checked out whether the yardstick for selection attributes can be commonly used to both male and female. In order to do so, this research analyzed that point by applying a statistics program to it, after collecting data by using the yardstick for selection attributes to a total of 391 individuals who visited a screen golf course.
As a result, the sameness of type-measurement-segment-factor dispersion were all established through collected data, and while differences of potential average analyses were drawn from the service of selection attributes and the program factors, it turned out that there were few meaningful effects of differences, which led us to conclude that information gained from all the users, male and female, is a suitable criterion to be commonly used to any customer. Namely, we came to a final conclusion that it is free from problems to provide information from the viewpoint of generalization after breaking down all the information by an identical method, regardless of the sex of each lower factor of the yardstick for selection attributes.
Nevertheless, more studies should be conducted which takes into consideration other individual characters such as age and career except for sex so that screen golf courses can be brisk, and vertical studies are also needed in order to exactly diagnose by means of changes in selection attributes.

목차

Abstract
Ⅰ. 서론
Ⅱ. 연구방법
Ⅲ. 결과
Ⅳ. 논의
Ⅴ. 결론 및 제언
참고문헌

참고문헌 (30)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2016-692-001868370