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首页> 外文期刊>Journal of Applied Psychology >Decomposing Model Fit: Measurement vs. Theory in Organizational Research Using Latent Variables
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Decomposing Model Fit: Measurement vs. Theory in Organizational Research Using Latent Variables

机译：分解模型拟合：使用潜在变量的组织研究中的测量与理论

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摘要

Goodness-of-fit indices have an important role in structural equation model evaluation. However, some studies (e.g., McDonald & Ho, 2002; Mulaik et al., 1989) have raised concerns that overall fit values primarily reflect the fit of the measurement model, and this allows significant misspecification among the latent variables to be masked. Using an approach analogous to Anderson and Gerbing's (1988) 2-step approach that isolates the measurement component of a composite model, we present the rationale and evidence for the root mean square error of approximation of the path component (RMSEA-P), a relatively new fit index that isolates the path component. We reviewed 5 of the top organizational behavior/human resources journals from 2001 to 2008 and identified 43 studies using structural equation modeling in which the overall composite model could be decomposed into its measurement and path components. The RMSEA-P for these studies generally showed unfavorable results, with many values failing to meet commonly accepted standards. Incorporating the RMSEA-P and its confidence interval into James, Mulaik, and Brett's (1982) framework for model testing, we provide evidence that many of the conclusions based upon the goodness of fit of the overall model may be inaccurate. We conclude with recommendations for how researchers can focus more attention on path models and latent variable relations and improve their model evaluation process.
机译：拟合优度指标在结构方程模型评估中具有重要作用。但是，一些研究（例如，McDonald＆Ho，2002; Mulaik等，1989）引起了人们的担忧，即总体拟合值主要反映了测量模型的拟合度，这潜在地掩盖了潜在变量之间的明显错误。使用类似于Anderson和Gerbing（1988）的两步方法来分离复合模型的测量成分的方法，我们给出了路径成分近似值的均方根误差（RMSEA-P）的原理和证据。相对较新的适合指数，用于隔离路径成分。我们回顾了2001年至2008年间5篇顶级的组织行为/人力资源期刊，并使用结构方程模型确定了43项研究，其中整体组合模型可以分解为度量和路径成分。这些研究的RMSEA-P通常显示出不利的结果，许多值均未达到公认的标准。将RMSEA-P及其置信区间纳入James，Mulaik和Brett（1982）的模型测试框架中，我们提供证据表明，基于整体模型拟合优度的许多结论可能都不准确。最后，我们提出了一些建议，建议研究人员如何更加关注路径模型和潜在变量关系，并改善他们的模型评估过程。

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