Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions—either before or after the estimation of more restrictive CFA models—to (a) identify model misspecifications that may drive anomalous estimates and (b) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All CFA models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p-factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models.

中文翻译：

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Tom Swift 的电动因子分析机中错误的方法和被遗忘的教训：具有竞争性精神病理学结构模型的演示。

验证性因子分析（CFA）及其双因子模型在心理结构的因子结构的实证研究中很受欢迎。CFA 提供直接的假设检验，但也存在明显的缺陷，例如强加严格的假设（即简单的结构）会掩盖未建模的复杂性。由于双因子 CFA 的局限性，它们在样本和研究中产生了异常结果，表明模型错误指定（例如，蒸发特定因子和意外加载）。我们建议使用探索性因子分析 (EFA) 来评估 CFA 解决方案的结构有效性——无论是在估计更具限制性的 CFA 模型之前还是之后——以 (a) 识别可能导致异常估计的模型错误规范和 (b) 确认 CFA通过检查假设的结构是否在有限的研究人员投入下出现来建立模型。我们评估了主要因素结构在探索性-确认性连续统一体中跨环境不变的程度，并展示了糟糕的方法选择如何扭曲结果并阻碍理论发展。所有 CFA 模型都非常适合，但在可复制性和实质性可解释性方面存在许多差异。双因子 CFA 和 EFA 模型之间出现了一些相似之处，包括过度提取的证据，特定因素对一般因素的崩溃，以及随后如何定义一般因素的转变。我们将这些方法论的缺陷置于关于精神病理学结构模型的更广泛文献中，阐明对理论的影响（例如p因子），概述了在执行探索性双因子分析时遇到的问题的几种补救措施，并提出了验证双因子模型的替代规范。