Anomalous Results in G-Factor Models: Explanations and Alternatives
Anomalous Results in G-Factor Models: Explanations and Alternatives
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DOI：
10.1037/met0000083
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发表时间：
2017-09-01
影响因子：
7
通讯作者：
Heene, Moritz
中科院分区：
文献类型：
--
作者：
Eid, Michael;Geiser, Christian;Heene, Moritz
G因素模型，如双因素模型和层次G因素模型，在心理学中的应用越来越广泛。这些模型的许多应用都产生了异常和意想不到的结果，这些结果往往与这些应用所基于的理论假设不符。这种反常结果的例子是消失的特定因素和不规则的加载模式。在这篇文章中，作者表明，从随机测量理论的角度来看，当G因素模型应用于单水平(而不是两水平)抽样过程时，异常结果是必须预期的。作者认为，双因素模型和相关模型的应用需要两级抽样过程，而这在实证研究中通常是不存在的。我们演示了如何推导出具有G因子和特定因子的替代模型，以更好地定义作为大多数实证研究基础的实际单水平抽样设计。文中详细说明了如何定义两个备选模型，即双因素(S-1)模型和双因素(S.i-1)模型。对这些模型的性质进行了描述，并用一个实证实例进行了说明。最后，讨论了进一步分析多维模型的方法。
G-factor models such as the bifactor model and the hierarchical G-factor model are increasingly applied in psychology. Many applications of these models have produced anomalous and unexpected results that are often not in line with the theoretical assumptions on which these applications are based. Examples of such anomalous results are vanishing specific factors and irregular loading patterns. In this article, the authors show that from the perspective of stochastic measurement theory anomalous results have to be expected when G-factor models are applied to a single-level (rather than a 2-level) sampling process. The authors argue that the application of the bifactor model and related models require a 2-level sampling process that is usually not present in empirical studies. We demonstrate how alternative models with a G-factor and specific factors can be derived that are more well-defined for the actual single-level sampling design that underlies most empirical studies. It is shown in detail how 2 alternative models, the bifactor-(S - 1) model and the bifactor-(S. I - 1) model, can be defined. The properties of these models are described and illustrated with an empirical example. Finally, further alternatives for analyzing multidimensional models are discussed.