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| 題名: | 我國公立大學校院校務基金財務執行績效之分析與評估研究 |
| 其他題名: | A study to analyze and evaluate the performance of public colleges and universities school fund |
| 作者: | 范東生 |
| 貢獻者: | 郭秋勳
范東生 |
| 關鍵詞: | 校務基金;多變量分析;典型相關;集群分析;區別分析
Colleges and Universities School Funds;Multivariate Analysis;Canonical Coorelation Analysis;Cluster Analysis;Discriminant Analysis |
| 日期: | 2001
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| 上傳時間: | 2010-12-03
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| 描述: | 碩士
教育研究所 |
| 摘要: | 本研究透過財務績效的觀念，運用多變量統計分析方法，設計出一套有效的校務基金財務執行績效的評估模式，以瞭解各公立大學校院的財務收支重點與差異，評估其財務執行績效，並做出預測模型。本研究兼將最早試辦校務基金新制的五所公立大學，進行時間縱向的比較分析，以瞭解其實施五年來的實際執行趨勢。另探究一些相關的區別變數與校務基金自籌項目問的關聯性。綜合本研究之主要結果與發現如下：
一、從時間縱向的趨勢追蹤與比較分析發現，只有學生公費與獎學金的費用項目平均每年減少幅度為-1.5%，在其他收支項目的平均年增幅度均為正向增加。
二、學校規模、學校所在縣市的商業登記總家數、學校所在縣市廠商投入研發之總經費以及學校所在縣市的人口密度等四個變項，分別與校務基金之建教合作毛收入、推廣教育毛收入與捐贈收入三個自籌項目，具有正相關。
三、根據本研究建立之公立大學校院財務績效評估模式，依九項財務收支比率表現之不同，透過集群分析，可分為四個不同財務執行績效的群體。
四、本研究執行同步區別分析的結果，各項財務比率對於區別各校的財務執行績效之重要性依次為：1.推廣教育淨收入 2.其他作業外收入 3.作業外費用 4.學校教學研究補助收入 5.建教合作淨收入 6.作業成本與資用 7.財務收入 8.學雜費收入 9.受贈收入。
五、本研究根據實證結果，建立四個預測模型，其正確率達100%，具有很高的預測能力。根據本研究建立的執行績效評估模型，可以比較各集群間的差異，並據以解釋及評估其財務執行績效，依其優劣順序為「集群二」、「集群三」、「集群一」與「集群四」。其中「集群二」在開源方面表現突出，「集群三」在節流方面表現較佳，「集群一」則屬平平，而「集群四」則在各方面表現均不理想，有待進一步之加強。
本研究根據上述研究結果，另行對各公立校院、教育部與相關監督單位，以及未來進一步之研究，提出若干相關的建議。
This study is using multivariate analysis through the idea of finance performance to design an effective pattern evaluating public college fund. By this analysis pattern, we can understand the emphases and the differences among the public college funds; thereby, we can make predict models to discriminate and evaluate them. Through this study, we've chosen the earlist five universities to analysis their trends of school funds. Also exploring some segmentation variables to see if they are correlated with some self-raised fund items.
The obtained results indicated:
1. During the analysis of five years trend. We found that only the government scholarship, the public expense item, has an average decrement rate about -1.5% per year. Other income and expenditure items are all presents positive increase rate.
2. School extent, business registration of located county, R&D input appropriation of located county, and located county population density are positive correlated with university-industry cooperation gross income, promotive education gross income and donation.
3. Through this financial performance analysis pattern. In this study, based on the nine income and expenditure items, we can divide these samples into four different financial performance groups by Cluster Analysis.
4. The importance of each financial ratio to discriminate different college funds according the results of Discriminant Analysis in this study are ranking as followed: (1) promotive education net income (2) other non-operate income (3) non-operate expenditure (4) government subsidy (5) university-industry cooperation net income (6) operation cost and expenditure (7) financial income (8) tuition income (9) donative income.
5. According to this analysis pattern, we build four predict models. The hit ratio of these four models are 100% correct. Showing a very good prediction ability of these prediction models. Base on this analysis pattern, we can explain and evaluate the financial performance of these college funds ranking from Cluster 2, Cluster 3, Cluster I and Cluster 4. Cluster 2 presents to open more sources of income. Cluster 3 presents to cut down more expenditure. Cluster I presents mediocrity and Cluster 4 presents insufficient in most items.
A number of suggestions were made for further study and educational guidance purpose. |
| 顯示於類別: | [教育研究所] 博碩士論文
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