Are Fit Indices Really Fit to Estimate the Number of Factors With Categorical Variables? Some Cautionary Findings via Monte Carlo Simulation
Are Fit Indices Really Fit to Estimate the Number of Factors With Categorical Variables? Some Cautionary Findings via Monte Carlo Simulation
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DOI：
10.1037/met0000064
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发表时间：
2016-03-01
影响因子：
7
通讯作者：
Ponsoda, Vicente
中科院分区：
文献类型：
--
作者：
Garrido, Luis Eduardo;Abad, Francisco Jose;Ponsoda, Vicente
结构验证过程的早期步骤包括为预定数量的公共因子建立无限制的“探索性”析因模型的拟合。对于这个初始的无限制模型，研究人员经常推荐并使用拟合指数来估计要保留的因子数量。尽管这种方法在逻辑上很有吸引力，但人们对拟合指数在估计数据维度时的实际准确性知之甚少。本研究旨在通过系统地评估4种常用拟合指数--比较拟合指数（CFI）、Tucker-Lewis指数（TLI）、近似均方根误差（RMSEA）和标准化均方根残差（SRMR）--在估计具有分类变量的因子数量时的性能，并将其与当前可以说是黄金法则的方法进行比较，Horn（1965）的平行分析。结果表明，CFI和TLI提供了几乎相同的估计，是最准确的拟合指数，其次是RMSEA，然后是SRMR，它给出了显着较差的维数估计。然而，困难的拟合指数和平行分析的一般优势，建立最佳截止值，建议应用研究人员更好地服务于补充他们的理论考虑与后一种方法提供的估计维度。
An early step in the process of construct validation consists of establishing the fit of an unrestricted "exploratory" factorial model for a prespecified number of common factors. For this initial unrestricted model, researchers have often recommended and used fit indices to estimate the number of factors to retain. Despite the logical appeal of this approach, little is known about the actual accuracy of fit indices in the estimation of data dimensionality. The present study aimed to reduce this gap by systematically evaluating the performance of 4 commonly used fit indices-the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR)-in the estimation of the number of factors with categorical variables, and comparing it with what is arguably the current golden rule, Horn's (1965) parallel analysis. The results indicate that the CFI and TLI provide nearly identical estimations and are the most accurate fit indices, followed at a step below by the RMSEA, and then by the SRMR, which gives notably poor dimensionality estimates. Difficulties in establishing optimal cutoff values for the fit indices and the general superiority of parallel analysis, however, suggest that applied researchers are better served by complementing their theoretical considerations regarding dimensionality with the estimates provided by the latter method.