第三波革命到底稱得上是革命嗎?我在學時代南加大另一個心理計量領域的卓越教授Linda Collins[2]或許會同意我的話。因為在縱貫資料的計量領域中,SEM已經成為一個指標性與領導性的分析技術。2001年所APA所出版的New Methods for the Analysis of Change?一書中,至少有一半的章節直接以SEM為題,間接與SEM有關的論點則散見於各章節中。
另外,在學術活動方面,根據Hershberger(2003)檢閱1994至2001年間的相關文獻發現,到了2003年的今天,不論在刊登結構方程模式相關論文的期刊數、期刊論文的數量、結構方程模式所延伸出來的多變量分析技術等各方面,均有大幅度的成長,顯示結構方程模式已經是一門發展成熟且高度受到重視的學問與技術。結構方程模式除了擁有專屬期刊《結構方程模式》(Structural Equation Modeling)[4],專門刊登與結構方程模式有關的論文與實證研究,心理學界的重要典籍《心理學年度評論》(Annual Review of Psychology)也於1996年(Bentler & Dudgeon, 1996)與2000年(MacCallum & Austin, 2000)兩度刊登了介紹結構方程模式相關文獻的專文。美國社會學會出版的《社會方法學》(Sociological Methodology)與《社會學方法與研究》(Sociological Methods and Research),以及美國心理學會的《心理學方法》(Psychological Methods)期刊,每一卷也都有相當篇幅有關結構方程模式的應用的論文。在《心理學方法》第七卷第一期中,McDonald與Ho(2002)發表了一篇結構方程模式整理原則與寫作規範(Principles and Practice in Reporting Structural Equation Modeling),作為結構方程模式相關學術文獻的寫作、投稿與編輯的準則,其他在管理學、傳播學、教育學等領域的重要期刊,也有越來越多的相關討論與應用論文。這些學術上的發展趨勢,再再說明結構方程模式在相關領域的重要地位。
綜觀統計分析技術的內容,可以概略分為平均數檢定的變異數分析與探討線性關係的迴歸分析兩大範疇。事實上,這兩者並無本質上的差異,前者可以被歸為一般線性模型(general linear model)分析技術,後者則是以變項間的線性關係為分析的內容。隨著電腦科技的發展,分析軟體功能的提昇,使得兩種統計模式可以互通,合而為一。
一般線性模型的優點是可以數學方式來整合不同型態的變異來源,可以不斷擴充研究者所欲探討的變項的數目與影響方式,因此一般線性模型逐漸發展出多種多變量統計的概念,例如多變量變異數分析(multivariate analysis of variance)。而迴歸分析在處理變項的彈性與複雜度的優勢似乎有凌駕變異數分析之勢,但是變異數分析由於簡單清楚的數學原理與容易解釋分析的特性,也一直受到研究者的青睞[8]。在SEM當中,雖然是以變項的共變關係為主要內容,但由於SEM模型往往牽涉到大量變項的分析,因此常借用一般線性模式分析技術來整合變項,故SEM分析可以說是多種不同統計分析程序的集合體。
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