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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/97620


    Title: 驗證性因素模式加權最小平方法之小樣本估計特性分析
    Authors: 蔡坤宏
    Keywords: 驗證性因素分析 ; 加權最小平方法 ; 小樣本 ; 估計 CFA
    Date: 1995-09
    Issue Date: 2016-06-04 14:00:33 (UTC+8)
    Abstract: 以往研究樣本大小對驗證性因素模式估計的影響皆集中於最大概似估計法（ML） ，而對加權最小平方法（ WLS ）的影響並不清楚。實證上亦見到小樣本時 WLS 的使用，但 卻缺乏明確的參考依據。是以，本文主要目的即在探討驗證性因素分析模式中，WLS 在小樣 本下的估計特性。因解析上的困難，本文以 Monte Carlo 模擬的方式進行探討。 根據模擬結果發現： 雖然 WLS 並不會因其估計的有效特性而在參數上產生偏高的錯誤評估 ，但是 WLS 的估計並不精確，且隨樣本數的減少更為明顯。 這種現象對兩個不同的模式設 定而言，皆一致。而且，在模式評估上，WLS 理論上，亦顯然地較易產生較高的錯誤評估。 對實證研究而言，這些發現有著重要的涵意； 理論上，儘管 WLS 估計有著分佈自由的優點 。但實務上，樣本大小在 200 （含）以下時，WLS 並不是一個良好的估計方法。
    The purpose of the paper is to investigate the performance of method of estimation, WLS, under small sample sizes. In this investigation we use Monte Carlo simulation to generate pseudo data under factorial design given different specified CFA models. Results show that the parameter estimation of WLS is not precise enough. The smaller the sample sizes are, the less precise the estimation is. Moreover, in the model evaluation WLS is prone to resulting in type I error. These findings enrich the understanding of the small sample properties of WLS on CFA model. Furthermore, for the empirical study, they imply that researchers had better not choose WLS to estimate CFA model when the sample sizes are fewer than 200.
    Relation: 教育與心理研究, 18,1-17
    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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