论文标题
模型选择中的简约：评估拟合倾向的工具
Parsimony in Model Selection: Tools for Assessing Fit Propensity
论文作者
论文摘要
理论可以表示为经验测试的统计模型。有关于模型选择和多模型推理的大量文献，重点是如何评估哪种统计模型，因此哪种理论最适合可用数据。例如，给定一些数据，可以比较有关各种信息标准或其他拟合统计的模型。但是，这些指数未能捕获的是全部反事实。也就是说，某些模型可能会更好地拟合给定数据，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多的拟合倾向 - 一种倾向于拟合更广泛的数据范围的趋势，甚至是荒谬的数据。当前的方法在考虑parsimony的原理（OCCAM的剃须刀）时缺乏，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，以通过结构方程模型的拟合倾向更好地研究和理解简约。我们提供了基于流行的Lavaan包装的R包（Ockhamsem）。为了说明评估拟合倾向的重要性，我们使用Ockhamsem来研究Rosenberg自尊量表的因素结构。
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.
