應用決策樹探索大學以上畢業生薪資之影響因素 Decision Tree for Investigating the Factors Affecting Graduate Salaries - 鄭永福 Yung-Fu Cheng; 許瑛玿 Ying-Shao Hsu (National Taiwan Normal University, 2017)
近年由於高等教育的快速擴充，導致教育市場的競爭壓力激增，大學以上畢業生在勞動 市場上的供需失衡，其薪資的變化逐漸成為社會大眾關注的議題。近 20 年，已有諸多文獻探 討大學畢業生薪資的影響因素，也建立相當完整的薪資理論架構。本研究目的在探究大學畢 業生薪資的影響因素，研究以台灣教育長期追蹤資料庫（TEPS）以及台灣教育長期追蹤資料 來源後續調查：教育與勞動市場的連結（TEPS-B）為資料，採用資料庫的變項共 1,303 個， 設定研究三種薪資模型，分別是畢業初薪資、入職場後薪資以及薪資變動等模型，研究採用 大數據資料探勘作法，分別對三個模型進行決策樹資料探勘，因薪資為連續變項，故採用決 策樹中的迴歸樹方法，研究結果顯示：一、畢業初薪資模型篩選出教育程度、公司組織規模 以及高中職二年級時的綜合分析能力。二、入職場後薪資模型篩選出公司組織規模、工作職 務、工作時數以及高中職二年級時的綜合分析能力。三、薪資變動模型篩選出工作職務。本 研究結果亦顯示，決策樹分析能夠有效地發掘出既有薪資理論之外的薪資影響因素，即高中 職二年級時的綜合分析能力及答對題數。 In recent years, the rapid expansion of higher education has led to a surge in competitive pressure in the education market and has increased the excess in university-educated labor supply. Thus, the salary of graduates is a major societal issue. Over the past two decades, a number of studies have explored the factors that positively affect graduate salaries, and have constructed a complete theoretical scheme. Our studies explored these factors by analyzing data from the Taiwan Education Panel Survey and Taiwan Education Panel Survey and Beyond, which together comprise 1,303 variables. The following three types of model were investigated: the initial model, workplace model, and change model. Data mining for the three models was conducted using a regression tree, a type of decision tree method for analyzing big data. The major findings of this research are described as follows: (1) Three variables, educational level, firm size, and academic achievement, were selected in the initial model. (2) Four variables, firm size, job title, working hours, and academic achievement, were selected in the workplace model. (3) One variable, job title, was selected in the change model. In summary, the decision tree effectively determined that academic achievement positively affects graduate salaries.