Journal of the Korean Data Analysis Society

Korean Data Analysis Society (한국자료분석학회)
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A Study on Determinant Factor for Number of Stop-out

휴학학기 수에 대한 결정요인 분석

  • Cho, Jang Sik (Department of Informational Statistics, Kyungsung University)
  • 조장식 (경성대학교 정보통계학과)
  • Received : 2014.01.07
  • Accepted : 2014.02.08
  • Published : 2014.02.28
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Abstract

The fundamental concerns of this paper are to analyze the effects for number of stop-out of an university students using characteristic variables. We use Poisson regression and negative binomial regression models since the number of stop-out is count data. The results of the analysis were given as follows. First, sex, department category, GPA and screening time had statistically significant effect on number of stop-out. Second, male had statistically significant effect on number of stop-out larger than female. Third, humanity and regular screening had statistically significant effect larger than art and nonscheduled screening, respectively. Finally, GPA had statistically significant effect on number of stop-out had statistically significant. These results are used as basic information to manage stop-out students.

본 연구에서는 대학 졸업생들에 대해 재학기간 중 휴학한 총 학기 수에 미치는 영향력을 분석하였다. 이를 위해 먼저 다중대응분석과 의사결정나무분석을 활용하여 총 휴학학기 수와 관련성 있는 독립변수를 탐색하였다. 그리고 포아송 회귀모형과 음이항 회귀모형을 적용하여 총 휴학학기 수에 미치는 영향력을 분석하여 다음과 같은 결과를 얻었다. 여학생에 비해서 남학생의 총 휴학학기 수가 많았으며, 예체능 계열에 비해서 인문계열이 총 휴학학기 수가 많았다. 또한 수시모집으로 입학한 학생에 비해서 정시모집으로 입학한 학생들의 총 휴학학기 수가 작았으며, 평균평점이 높을수록 총 휴학학기 수가 통계적으로 유의하게 작음을 알 수 있다. 이러한 연구결과는 휴학횟수에 미치는 요인에 대한 분석을 통해서 휴학횟수를 줄이기 위해서는 어떤 프로그램과 지원체제를 갖추어야 하는지에 대한 기초자료를 제공할 수 있을 것이다.

Keywords

Acknowledgement

Supported by : 경성대학교

References

  1. Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. (1984). Classification and regression trees, Belmont : Wadsworth.
  2. Chae, J. E. (2011). A study on the stopout of Korean college students, The Journal of Educational Administration, 29(1), 295-317. (in Korean).
  3. Cho, J. S. (2011). Determination Analysis for Students of University Dropout, Journal of the Korean Data Analysis Society, 13(4), 1873-1880. (in Korean).
  4. Cho, J. S. (2012). Inflow and outflow analysis of double majors using social network analysis, Journal of the Korean Data & Information Science Society, 23(4), 693-701. (in Korean). https://doi.org/10.7465/jkdi.2012.23.4.693
  5. Chu, H. J., Cha, S. H. (2011). An analysis of factors affecting four-year college students' stop-out, The Journal of Educational Administration, 29(1), 277-293. (in Korean).
  6. Chun, H. J., Kim, H. I. (2008). A Study on SMS Expansion Using Negative Binomial Regression, Journal of the Korean Data Analysis Society, 10(4), 2065-2074. (in Korean).
  7. Kim, K. M., Eum, H. J. (2010). Comparisons of the estimators for the zero-inflated Poisson distribution, Journal of the Korean Data Analysis Society, 12(2), 1113-1124. (in Korean).
  8. Kim, J. D., Kim, J. H. (2013). Korean college student stopout experiences and employment outcomes, 2013 Employment Panel Conference, 557-572. (in Korean).
  9. Lee, D. H. (2013). Simulated maximum likelihood estimator for the zero inflated Poisson lognormal regression model, Journal of the Korean Data Analysis Society, 15(3), 1347-1360. (in Korean).
  10. Lee, J. K. (2011). The Effect of the Economic Background Risk on Fertility Decision : Using Censored and Zero Inflated Count Data Regression, Journal of the Korean Data Analysis Society, 13(3), 1521-1532. (in Korean).
  11. Rhee, B. S. (2003). An exploratory analysis of institutional influences on college student withdrawal, Korean Journal of Educational Research, 41(2), 135-154. (in Korean).
  12. News 21 homepage : http://www.krnews21.co.kr/sub_read.html?uid=108211. (in Korean).
  13. Veritas alpha : http://www.veritas-a.com/news/articleView.html?idxno=17589. (in Korean).
  14. Voice of the people : http://www.vop.co.kr/A00000296570.html. (in Korean).