The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification using modification indices and the Saris procedure, exploratory factor analysis (EFA) with different rotation procedures (Geomin, target, and objectively refined target matrix), Bayesian structural equation modeling (BSEM), and a new set of procedures that, after fitting an unrestrictive model (i.e., EFA, BSEM), identify and retain only the relevant loadings to provide a parsimonious CFA solution (ECFA, BCFA). By means of an exhaustive Monte Carlo simulation study and a real data illustration, it is shown that CFA and BSEM are overly stiff and, consequently, do not appropriately recover the structure of slightly misspecified models. EFA usually provides the most accurate parameter estimates, although the rotation procedure choice is of major importance, especially depending on whether the latent factors are correlated or not. Finally, ECFA might be a sound option whenever an a priori structure cannot be hypothesized and the latent factors are correlated. Moreover, it is shown that the pattern of the results of a factor analytic technique can be somehow predicted based on its positioning in the confirmatory-exploratory continuum. Applied recommendations are given for the selection of the most appropriate technique under different representative scenarios by means of a detailed flowchart. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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探索性因素分析总是首选吗？在整个验证-探索连续体中对因子分析技术进行系统比较。

在过去的几十年里，可用的因子分析技术的数量一直在增加。然而，这些技术之间缺乏明确的指导方针和详尽的比较研究可能会阻碍这些有价值的方法学进步进入应用研究。本文评估了验证性因素分析 (CFA)、使用修改指数和 Saris 程序修改顺序模型的 CFA、具有不同旋转程序（Geomin、目标和客观改进的目标矩阵）的探索性因素分析 (EFA)、贝叶斯结构方程模型 (BSEM)，以及一组新程序，在拟合非限制性模型（即 EFA、BSEM）后，识别并仅保留相关载荷以提供简约的 CFA 解决方案（ECFA、BCFA）。通过详尽的蒙特卡罗模拟研究和真实数据说明，表明 CFA 和 BSEM 过于僵硬，因此无法适当地恢复稍微错误指定的模型的结构。EFA 通常提供最准确的参数估计，尽管旋转过程的选择非常重要，尤其取决于潜在因素是否相关。最后，当无法假设先验结构且潜在因素相关时，ECFA 可能是一个不错的选择。此外，表明可以根据因子分析技术在验证-探索连续体中的定位以某种方式预测其结果的模式。通过详细的流程图，给出了在不同代表性场景下选择最合适技术的应用建议。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。