成長模型第一層次誤差共變異結構之鑑定準則

Abstract

本研究提出潛在成長模型(LGM)之第一層次誤差共變異結構的鑑定準則，並假設第二層次誤差共變異沒有特定結構，該準則係基於改編自Anderson 與 Gerbing (1988)之循序卡方差異檢定法；此外，本研究也特別說明如何檢定誤差過程的穩態性。無論是就外顯變數或潛在構念之成長建模，所提供之鑑定準則皆適用，有助於正確設定成長模型之誤差共變異結構。針對模擬以及實際資料，我們利用SAS 軟體之PROC CALIS，具體示範說明所提準則在鑑定誤差共變異結構上的操作程序，以供實證研究者參考引用。

In this study, guidelines for identifying the first-level error covariance structures in latent growth modeling (LGM) are proposed, assuming that the second-level error covariances are unstructured. The guidelines are based on the sequential chi-square difference test, adapted from Anderson and Gerbing (1988). Moreover, how to test for stationarity of an error process is specifically addressed. The guidelines are useful for correctly specifying the first-level error covariance structures, regardless of modeling growth curves for manifest variables or latent constructs. Simulated and real data were used to demonstrate identifying the first-level error covariance structures with the guidelines by using SAS PROC CALIS.