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dc.contributor.author鄭天德en_US
dc.contributor.authorJane, Ten-Deren_US
dc.contributor.author丁承en_US
dc.contributor.authorDing, Cherng G.en_US
dc.date.accessioned2014-12-12T01:21:56Z-
dc.date.available2014-12-12T01:21:56Z-
dc.date.issued2010en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079137804en_US
dc.identifier.urihttp://hdl.handle.net/11536/40340-
dc.description.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。zh_TW
dc.description.abstractIn 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.en_US
dc.language.isozh_TWen_US
dc.subject誤差共變異結構zh_TW
dc.subject潛在成長模型zh_TW
dc.subject穩態性zh_TW
dc.subject結構方程式模型zh_TW
dc.subjecterror covariance structureen_US
dc.subjectlatent growth modelen_US
dc.subjectstationarityen_US
dc.subjectstructural equation modelingen_US
dc.title成長模型第一層次誤差共變異結構之鑑定準則zh_TW
dc.titleGuidelines for identifying level-1 error covariance structures in latent growth modelingen_US
dc.typeThesisen_US
dc.contributor.department經營管理研究所zh_TW
Appears in Collections:Thesis


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