休閒滿意預測因子之迴歸與類神經網路分析

Analysis of the Predictors of Leisure Satisfaction by Regression and Artificial Neural Network

吳明蒼(Ming-Tsang Wu) ； 柳立偉(Li-Wei Liu)

《運動知識學報》 6期 (2009/10) Pp. 69-79

https://doi.org/10.29596/BGYY.200910.0007

摘要

目的：以某護理專科學校學生為研究對象，探討休閒態度、休閒動機、休閒阻礙對休閒滿意之預測程度。方法：利用問卷調查方式蒐集資料，並以迴歸與類神經網路進行分析，正式調查共發出943份問卷，回收有效問卷共870份，有效問卷回收率93%。結果：本研究發現預測變項之檢測效果以迴歸分析較佳，休閒阻礙對之休閒滿意預測力並未達顯著水準。若以休閒態度與休閒動機為預測變項建立複迴歸方程式，結果顯示，休閒態度、休閒動機共同與休閒滿意的相關係數為.997，休閒態度與休閒動機對休閒滿意的變異解釋高達99.1%。結論：由研究結果可推知，該校只要能積極協助學生建立正向的休閒態度，並激勵學生正向的發展其休閒動，應可提升學生的休閒滿意程度，亦有助於其增進生活品質，使求學生活更加順利。

關鍵字

休閒滿意 ； 預測變項 ； 休閒因子

並列摘要

Purpose: Using the students of a nursing college as subjects, the degree of predictability of leisure attitude, leisure motivation, leisure constraints to leisure satisfaction were explored. Method: Using surveys to collect data and multiple regression and artificial neural network to analyze data, 943 survey forms were sent, and 870 valid responses were received for a valid return rate of 93%. Results: This research discovered that multiple regression analysis worked better than artificial neural network in the testing of prediction variables. The predictability of leisure constraint on leisure satisfaction did not reach significant level. Using leisure attitude and leisure motivation as prediction variables to build a regression model, leisure attitude and leisure motivation both correlated with leisure satisfaction with multiple correlation coefficient of .997, and 99.1% of the variability in leisure satisfaction could be accounted for by the variability in leisure attitude and leisure motivation considered together. Conclusions: From this research we can infer that the school should actively help students build positive leisure attitude, and motivate students toward developing leisure behavior. This can promote students' leisure satisfaction, and increase quality of student life.

並列關鍵字

leisure satisfaction ； prediction variable ； leisure factor