Focusing Narrowly on Model Fit in Factor Analysis Can Mask Construct Heterogeneity and Model Misspecification: Applied Demonstrations across Sample and Assessment Types.
Focusing Narrowly on Model Fit in Factor Analysis Can Mask Construct Heterogeneity and Model Misspecification: Applied Demonstrations across Sample and Assessment Types.
复制标题
狭隘地关注因子分析中的模型拟合可以掩盖构造异质性和模型错误指定：跨样本和评估类型的应用演示。
DOI：
10.1080/00223891.2022.2047060
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发表时间：
2023
影响因子：
3.4
通讯作者：
Zimmerman,Mark
中科院分区：
文献类型：
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作者：
Stanton,Kasey;Watts,AshleyL;Levin-Aspenson,HollyF;Carpenter,RyanW;Emery,NoahN;Zimmerman,Mark
这项研究建立在研究的基础上，该研究表明，在评估因子分析模型时狭隘地关注模型拟合可能会导致有关项目集性质以及如何应用模型来为度量开发和验证提供信息的有问题的推论。为了推进这一领域的研究，我们提出了与临床、人格和相关子领域的研究人员相关的具体例子，强调了过度依赖模型拟合可能出现问题时的两种特定场景。具体来说，我们提供的数据分析示例表明，狭隘地关注模型拟合可能会导致（a）错误的结论，即异质项目集反映了较窄的同质结构，以及（b）在制定评估措施时保留潜在问题的项目。我们使用成人门诊患者的访谈数据 (N= 2,149) 和在线招募的成人的自我报告数据 (N= 547) 来证明这些问题在样本类型和评估方法中的重要性。在使用这些数据进行演示之后，我们提出建议，重点关注在评估因子分析模型时，除了模型拟合指数提供的信息之外，还应如何考虑其他模型特征（例如因子加载模式；仔细考虑因子指标的内容和性质）。
This study builds upon research indicating that focusing narrowly on model fit when evaluating factor analytic models can lead to problematic inferences regarding the nature of item sets, as well as how models should be applied to inform measure development and validation. To advance research in this area, we present concrete examples relevant to researchers in clinical, personality, and related subfields highlighting two specific scenarios when an overreliance on model fit may be problematic. Specifically, we present data analytic examples showing that focusing narrowly on model fit may lead to (a) incorrect conclusions that heterogeneous item sets reflect narrower homogeneous constructs and (b) the retention of potentially problematic items when developing assessment measures. We use both interview data from adult outpatients (N= 2,149) and self-report data from adults recruited online (N= 547) to demonstrate the importance of these issues across sample types and assessment methods. Following demonstrations with these data, we make recommendations focusing on how other model characteristics (e.g., factor loading patterns; carefully considering the content and nature of factor indicators) should be considered in addition to information provided by model fit indices when evaluating factor analytic models.