Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity—a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of structural equation models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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模型选择中的简约性：评估拟合倾向的工具。

理论可以表示为用于实证检验的统计模型。有大量关于模型选择和多模型推理的文献，重点关注如何评估哪种统计模型，以及哪种理论最适合可用数据。例如，给定一些数据，可以根据各种信息标准或其他拟合统计来比较模型。然而，这些指数未能捕捉到的是全方位的反事实。也就是说，某些模型可能更好地拟合给定数据，并不是因为它们代表了更正确的理论，而仅仅是因为这些模型具有更高的拟合倾向- 倾向于更好地适应更广泛的数据，甚至是无意义的数据。当前的方法在考虑简约原则（奥卡姆剃刀）方面存在不足，通常将其等同于模型参数的数量。在这里，我们为研究人员提供了一个工具包，可以通过结构方程模型的拟合倾向更好地研究和理解简约性。我们提供了一个基于流行的lavaan包构建的R包 ( ockhamSEM ) 。为了说明评估适合倾向的重要性，我们使用ockhamSEM来研究 Rosenberg 自尊量表的因子结构。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）