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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/99703


    Title: RMSEA與卡方差異法於檢定模式差異之正確性：測量不變性之檢定
    Other Titles: Evaluating RMSEA and Chi-square Difference Tests in Testing Model Differences: An Application of Testing easurement Invariance
    Authors: 楊志堅;楊志強;蔡良庭;呂淑妤
    Keywords: 卡方差異檢定;RMSEA 差異檢定;多組群驗證性因素分析;測量不變性
    Chi-square difference test;RMSEA difference test;multiple group CFA;measurement invariance
    Date: 2009-12
    Issue Date: 2016-08-08 10:47:31 (UTC+8)
    Abstract: 本研究主要探討在不同自由度、樣本數及因素負荷量之設計下，使用卡方差異檢定及RMSEA差異檢定於多組群驗證性因素分析檢測不同組群因素負荷量相同時之正確性；這個檢定也可應用於量表的不變性檢測。本研究利用模擬實驗，檢驗一般咸信的「卡方差異性檢定對於大樣本數的敏感程度會影響檢定模式適配的正確性」之信念及RMSEA不易受到模式複雜度及樣本數影響之特性。研究結果發現，該信念至少還需考慮一些特定因素，例如：在本研究所設定的情況下，以卡方差異檢定及RMSEA差異檢定於檢測不同組群間因素負荷量相同時，檢定正確率除了會隨因素負荷量差距、樣本數及自由度差距增加而提高，且存在交互作用的影響，因此，當研究者在進行相關研究時，需同時考慮到多種因素對於檢測效果的影響。
    This study is to investigate the screening accuracies between competing models by using RMSEA methods and Chi-square difference tests. The experimental design to demonstrate the accuracies contains various magnitudes of sample sizes， factor loadings， and degrees of freedom when multiple group confirmatory factor analysis is used to test measurement invariance. Some conventional practices believed that Chi-square difference test could be sensitive to sample sizes in selecting competing models while RMSEA may not have the problems. Results of simulation studies in this paper show that the beliefs have to be adjusted at least for some specified conditions， for example， some magnitudes of sample sizes， model differences， and model complexity (i.e.， degrees of freedom). Discussions and implications on the interesting results are provided.
    Relation: 教育與心理研究, 32(4),53-72
    Journal of Education & Psychology
    Data Type: article
    Appears in Collections:[教育與心理研究 TSSCI] 期刊論文

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