Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population. A previous study illustrated a bootstrap approach to gauge this model selection uncertainty using 2 empirical examples. This study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multigroup and mixture model comparisons. These simulations show that bootstrap selection rates can provide additional information over and above simply relying on the size of AIC and BIC differences in a given sample.

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使用 Bootstrap 方法评估模型选择的不确定性：更新

行为科学中的模型比较通常旨在选择最能描述总体结构的模型。模型选择通常基于Akaike的信息准则（AIC）或贝叶斯信息准则（BIC）等拟合指标，并根据选择的最佳拟合模型进行推断。这种做法没有考虑到由于抽样可变性，可能会在来自同一总体的新样本中选择不同的模型作为首选模型的可能性。之前的一项研究说明了使用 2 个经验示例来衡量这种模型选择不确定性的引导方法。本研究包括一系列模拟，以评估所提出的引导方法在多组和混合模型比较中的效用。