An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data
An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data
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DOI：
10.1037/1082-989x.9.4.466
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发表时间：
2004-12-01
影响因子：
7
通讯作者：
Curran, PJ
中科院分区：
文献类型：
--
作者：
Flora, DB;Curran, PJ
验证性因素分析（CFA）广泛用于检查序数变量之间的假设关系（例如，李克特型项目）。理论上适当的方法使用加权最小二乘（WLS）或健壮的WLS拟合了CFA模型。重要的是，这种方法假设一个连续的正常潜在过程决定了每个观察到的变量。对此假设的侵犯的程度破坏了CFA的估计，并不是众所周知。在本文中，作者使用计算机模拟研究从经验上研究了这个问题。结果表明，估计多对相关性是强大的，对于违反潜在正态性的适度侵犯。此外，WLS仅以最大的样本量进行足够的表现，但导致了较小的样品的实质性估计困难。最后，在所有条件下，强大的WLS表现良好。
Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions.