Inferences and conclusions drawn from model fitting analyses are commonly based on a single “best fitting” model. If model selection and inference are carried out using the same data model selection, uncertainty is ignored. We illustrate the Type I error inflation that can result from using the same data for model selection and inference, and we then propose a simple bootstrap-based approach to quantify model selection uncertainty in terms of model selection rates. A selection rate can be interpreted as an estimate of the replication probability of a fitted model. The benefits of bootstrapping model selection uncertainty are demonstrated in growth mixture analyses of data from the National Longitudinal Study of Youth, and a 2-group measurement invariance analysis of the Holzinger–Swineford data.

中文翻译：

---

基于最佳拟合模型的推理可能会导致复制危机：使用引导方法评估模型选择的不确定性


从模型拟合分析中得出的推论和结论通常基于单个“最佳拟合”模型。如果使用相同的数据模型选择来进行模型选择和推理，则可以忽略不确定性。我们说明了使用相同的数据进行模型选择和推理可能导致的 I 类错误膨胀，然后我们提出了一种简单的基于引导程序的方法来量化模型选择率方面的模型选择不确定性。选择率可以解释为拟合模型的复制概率的估计。自举模型选择不确定性的好处在国家青年纵向研究数据的增长混合分析以及 Holzinger-Swineford 数据的两组测量不变性分析中得到了证明。