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題名 | 典型相關分析與結構方程模式關係之探究=The Relationship between Canonical Correlation Analysis and Structural Equation Modeling |
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作者 | 傅粹馨; Fu, Tsuey-shing; |
期刊 | 屏東師院學報 |
出版日期 | 20020300 |
卷期 | 16 2002.03[民91.03] |
頁次 | 頁231-261 |
分類號 | 520.3 |
語文 | chi |
關鍵詞 | 典型相關分析; 結構方程模式; 階層模式; Canonical correlation analysis; Structural equation modeling; MIMIC model; |
中文摘要 | 本研究之目的在於探究典型相關分析與結構方程模式二者之關係。分析資料包含584名國小四年級與六年級學生在十一個分量表上的分數,分別以上述二種方法加以分析,並比較其結果,從而得知其間之關係。 典型相關分析以X組之七個變項為預測變項和Y組之四個變項為效標變項,得到四個典型相關係數,唯第一個典型相關係數達.01之顯著水準,此外,有標準化與非標準化之加權係數與重疊係數,尚有結構係數、index係數等等。 結構方程模式之分析透過MIMIC模式,採用LISREL軟體,經過八次之分析,除了得到X組變項與Y組變項分別在四個典型變項上之標準化與非標準化之加權係數與index係數與顯著性檢定外,尚有標準化之加權係數、結構係數與重疊係數,結果與典型相關分析之數值是一致的。此外,再經由結構方程模式(階層模式)以達成對各個典型相關係數顯著性檢定之任務,其結果與典型相關分析之只有第一個典型相關係數達顯著是一致的。 綜而言之,典型相關分析是結構方程模式的一個特例,結構方程模式之執行較為複雜,而它提供了兩項特性,一為對加權係數與index係數之顯著性檢定,二為對各個典型相關係數作顯著性之檢定。 |
英文摘要 | The purpose of this study was to explore the relationship between canonical correlation analysis and structural equation modeling. Data were scores of eleven scales from 584 4th and 6th grade students. The study described canonical correlation analysis (CCA) in comparison with structural equation modeling (SEM) by analyzing the same data set. For canonical correlation analysis, the data contained seven x variables and four y variables. Because the number of canonical function was mathematically limited by the number of variables in the smaller set, the maximum number of canonical function for the data was four. Among the four canonical functions, only the 1st canonical function reached statistical significance. In addition, the analysis not only provided raw and standardized canonical coefficients and redundancy coefficients, but also provided structure coefficients and index coefficients. Canonical correlation analysis could be represented as a multiple indicator/multiple causes (MIMIC) model. Maximum-likelihood estimates of the model parameters could be generated by the LISREL computer program. The parameter estimates from SEM were the same as those from canonical correlation analysis. Actually, the representation of CCA as a SEM seemed to be a little complex, because several related models had to be analyzed, and some additional calculations were needed to obtain all the results from CCA. In other words, CCA could be considered as a special case of SEM. In sum, two advantages of the SEM approach to CCA were: (1) significance testing of canonical weights and index coefficients were possible; (2) significance testing for individual canonical correlation functions were possible. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。