Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA
Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA
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DOI：
10.1080/10705511.2019.1611434
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发表时间：
2019-06-24
影响因子：
6
通讯作者：
Rosseel, Yves
中科院分区：
文献类型：
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作者：
Shi, Dexin;Maydeu-Olivares, Alberto;Rosseel, Yves
本研究介绍了序贯因子分析中用标准化均方根误差(SRMR)检验贴近度的统计理论。我们还比较了基于SRMR和基于逼近均方根误差(RMSEA)得到的可信区间(CI)和紧凑性检验的精度。RMSEA的当前(有偏见的)实现从未拒绝当数据是二进制时模型接近匹配，并且如果数据由五个类别组成，则几乎总是拒绝大样本中的模型。无偏RMSEA可以产生更好的拒绝率，但只有当变量数量较少且失配程度较小时，RMSEA才足够准确。相比之下，在所有模拟条件下，基于SRMR的紧密拟合测试产生可接受的I类错误率。紧密匹配的SRMR检验也比使用无偏RMSEA的检验更有效。
This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the SRMR with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almost invariably rejects the model in large samples if data consist of five categories. The unbiased RMSEA produces better rejection rates, but it is only accurate enough when the number of variables is small and the degree of misfit is small. In contrast, across all simulated conditions, the tests of close fit based on the SRMR yield acceptable type I error rates. SRMR tests of close fit are also more powerful than those using the unbiased RMSEA.