The last decade has witnessed a resurgence of interest in exploratory bifactor analysis models and the concomitant development of new methods to estimate these models. Understandably, due to the rapid pace of developments in this area, existing Monte Carlo comparisons of bifactor analysis have not included the newest methods. To address this issue, we compared the model recovery capabilities of 5 existing methods and 2 newer methods (Waller, 2018a) for exploratory bifactor analysis. Our study expands upon previous work in this area by comparing (a) a greater number of estimation algorithms and (b) by including both nonhierarchical and hierarchical bifactor models in our study design. In aggregate, we conducted almost 3 million exploratory bifactor analyses to identify the most accurate methods. Our results showed that, when compared with the alternatives, the rank-deficient Schmid-Leiman and Direct Schmid-Leiman methods were better able to recover both nonhierarchical and hierarchical bifactor structures. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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恢复双因素模型：七个方法的比较。

在过去的十年中，人们对探索性双因素分析模型的兴趣重新兴起，并且随之出现了估算这些模型的新方法。可以理解的是，由于该领域的快速发展，现有的双因素分析的蒙特卡洛比较并未包括最新的方法。为了解决这个问题，我们比较了5种现有方法和2种较新方法的模型恢复能力（Waller，2018a），用于探索性双因素分析。通过比较（a）大量的估计算法和（b）通过在研究设计中同时包括非分层和分层双因素模型，我们的研究扩展了该领域以前的工作。总计，我们进行了将近300万次探索性双因素分析，以找出最准确的方法。我们的结果表明，与其他方法相比，秩不足的Schmid-Leiman和Direct Schmid-Leiman方法能够更好地恢复非分层和分层双因子结构。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。