| 摘要: | 台灣雖然累積不少有關背景對教育取得的影響，以及教育擴充對教育機會不均等性之影響的研究，但是探討高等教育擴充對教育機會不均等性的研究，卻做的不夠。本文著重於台灣地區民眾「高等教育取得」機會不均等分析。欲探究何種背景者較有機會取得高等教育？不同背景者之間的差異有多大？並希望進一步瞭解在高等教育擴充的過程中，背景與子女高等教育取得之間的關聯是否產生了變化。本文主要運用社會變遷基本調查第三期第三次資料，選取受測當時年齡在25-64歲受訪者，分為四個年輪進行分析。以父親教育、母親教育、父親職業、出生地、性別、籍貫與出生年次為自變項，本人高等教育取得（包括大專、大學、研究所的教育年數、就學率與升學率）為依變項。首先，作雙變項關聯分析。其次，當依變項為大專以上、大學以上、研究所以上教育年數時，進行OLS迴歸分析；在探討就學率與升學率時，則以是否上「大專」、「大學」、「研究所」為依變項做邏輯迴歸。 研究結果顯示：1、在最年輕的25-33年齡組與34-41歲組相比，除研究所就學率外，不管在教育年數、就學率、升學率，母親教育的影響增強。2、在研究所教育取得，男性的顯著優勢情形卻一直存在，甚至在最年輕的25-34年齡組中也是如此。3、外省人、客家人比閩南人取得高等教育機會之優勢，而原住民最居弱勢。4、出生地都市化程度越高者，越有機會取得高等教育。5、年齡越輕，各項教育年數、就學率、升學率也越高。6、最年輕的25-33歲組高教、大學與研究所就學率都接近0.5。可是其b值與R square都比34-41歲組都來的低，背景的影響並不是最大。這是因為25-33歲組與34-41歲組樣本比較，許多背景變項對各項就學率的邏輯迴歸b值均下降，特別是性別在最年輕的年齡層中，所反映的教育資源分佈機會不均等性下降，違反「教育資源分配機會不均等性不變」的假設之預設所致。
There are many studies about how the background affects on educational attainment and the educational expansion influences inequity of higher education opportunity. But there are not enough studies on the educational expansion influence inequity of higher education opportunity. The study focuses finding “the Inequity of Higher Education Opportunity ” in Taiwan. Our major is what kinds of background have more opportunity to attain on higher education. We’d like to ask,” how much difference was resulted from the different backgrounds?” Furthermore, within the period of “expansion on higher education”, we’d also like to find out if there is any change between before and after the period. We used the national sample data from 1997 Social Change Survey. And we divided the data into 4 age cohorts, ranging from 25-64 years old. For the variable design, we take father’s education level, mother’s education level, father’s career category, place of birth, gender, ethnicity, and year of birth as independent variables. On the other hand, we take “respondent’s higher education” (including college-undergraduate-graduate-levels) as dependent variables. For the model design, firstly Chi-square test was undertaken as analysis of bivariate correlation. Secondly, we do OLS regression analysis was undertaken when take educational levels was taken as dependent variables. Thirdly, to find the effect on schooling rate and upgrading rate, logistic regression model was undertaken as dependent variables. The findings are as follows: 1.compare 25-33 cohort with34-41 cohorts, the effects of mother’s educational level on educational years, upgrading rate, schooling rate increase, except schooling rate in graduate schooling. 2. In graduate schooling attainment, male takes more advantage than female, which is also applied to the youngest 25-34 cohorts. 3. Compared to Hokkien group, Mainlander and Hakkas Group take more advantage. The most disadvantages go to the Aborigine. 4. The more metropolitan in place of birth the better the opportunity to attain higher education. 5. Younger cohorts take better advantage on acquiring higher education. 6. Schooling rate of the youngest 25-33 cohorts in higher education, undergraduate, and graduate school near 0.5. However, its b value and R-Square value much lower than the 34-41 cohort does. Therefore, the background effect seems not to be the most significant. In comparison with 25-33 cohort and 34-41 cohort, the logistic regression coefficients b’s of background variables on schooling rate decrease. Especially, we find” the inequality of educational resource distribution” decreased, even in different genders of youngest cohorts. So, it mismatches the assumption “ inequality of educational resource distribution keeps unchanged”. |
