對於一組來自於例行性健康檢查之橫斷性資料，本論文目的在於建立一個結構方程模型，作為後續疾病診斷追蹤及遺傳分析之長期性研究的先期分析基礎。由於本資料（包含問卷之設計）缺乏一個事先架構之生理學與病理學理論機制，故而所欲建構之結構方程模型的量測模型的部分，無法給予一確證型因素分析，所以我們必須暫時借用探索型因素分析以建立量測模型。基於此，對於整個結構方程模型建立之過程，我們混和了所謂全局估計與分段估計的想法，在整個結構方程模型路徑的建立方面，採用分段法；而在估計方面，仍採全局估計法。並依照多變數迴歸分析的結果，設立幾種顯著程度之判則，得到一個配適程度較佳的結構模型；且由於此結構模型之建立終將破壞由探索型因素分析所得到之量測模型擾動項的獨立性，我們最後乃借用最簡單之變項間邊際相關，逐次地加入一些共變性結構路徑。最後，我們並簡單探討不同的統計估計軟體在估計設限上所得到之差異。; The data analyzed in this thesis was collected from a part of a community-based screening program as a cross-sectional study. It offered a premise of a longitudinal study in constructing a structural equation model (SEM), and served as a building block of a future analysis for disease diagnosis, perhaps with a consideration of genetic confounding. Since there was no complete physiological and pathological model a priori, it is not possible to obtain a measurement model, as a part of the entire SEM, from the confirmatory factor analysis. Instead, we borrowed the strength of exploratory factor analysis (EFA) and adopted a ‘two-stage’ algorithm as a hybrid method. That is, to set-up the constructs of structural and measurement components in a two-stage manner, and to estimate the parameters of the entire SEM simultaneously. The paths of structural component were selected according to several rules of significance via ordinary multiple linear regressions. After an acceptable construct was obtained, in terms of goodness-of-fit indices, the inter-disturbance correlations re-induced by the structural equation modeling after EFA, which only permitted independent disturbances, were considered in a stepwise order based on absolute ranks of marginal correlations. Finally, results of different statistical packages with different constraint conditions and estimates were briefly discussed.
