BOOTSTRAPPING GOODNESS-OF-FIT MEASURES IN STRUCTURAL EQUATION MODELS
BOOTSTRAPPING GOODNESS-OF-FIT MEASURES IN STRUCTURAL EQUATION MODELS
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DOI：
10.1177/0049124192021002004
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发表时间：
1992-11-01
影响因子：
6.3
通讯作者：
STINE, RA
中科院分区：
文献类型：
--
作者：
BOLLEN, KA;STINE, RA
结构方程模型师对整体适合度的评估非常感兴趣，然而，适合度的测量一直受到几个因素的阻碍。首先，模型拟合度的卡方检验所依据的假设经常被违反。其次，许多拟合度指标(如Bentler和Bonett[1980]的归一化拟合度指数)具有未知的统计分布，因此不可能进行假设检验、可信区间或比较这些拟合度指数中的显著差异。最后，建模师对错误指定的模型或非嵌套模型的适配度量的分布和行为知之甚少。在这种情况下，自举技术似乎是解决这些问题的理想方法。事实上，Bentler(1989)的EQS 3.0以及Joreskog和Sorbom(即将推出)的LISREL 8都有Bootstrap重采样选项来Bootstrap Fit指数。在本文中，作者(A)证明了通常的Bootstrap方法在应用于原始数据时将失败，(B)解释了为什么会发生这种情况，以及(C)提出了用于模型拟合的卡方检验统计量的改进Bootstrap方法。它们包括模拟和经验例子来说明他们的结果。
Assessing overall fit is a topic of keen interest to structural equation modelers, yet measuring goodness of fit has been hampered by several factors. First the assumptions that underlie the chi-square tests of model fit often are violated. Second, many fit measures (eg., Bentler and Bonett's [1980] normed fit index) have unknown statistical distributions so that hypothesis testing, confidence intervals, or comparisons of significant differences in these fit indices are not possible. Finally, modelers have little knowledge about the distribution and behavior of the fit measures for misspecified models or for nonnested models. Given this situation, bootstrapping techniques would appear to be an ideal means to tackle these problems. Indeed, Bentler's (1989) EQS 3.0 and Joreskog and Sorbom's (forthcoming) LISREL 8 have bootstrap resampling options to bootstrap fit indices. In this article the authors (a) demonstrate that the usual bootstrapping methods will fail when applied to the original data, (b) explain why this occurs, and (c) propose a modified bootstrap method for the chi-square test statistic for model fit. They include simulated and empirical examples to illustrate their results.