Lubke Gitta H, Campbell Ian
University of Notre Dame.
VU Amsterdam.
Struct Equ Modeling. 2016;23(4):479-490. doi: 10.1080/10705511.2016.1141355. Epub 2016 Apr 7.
Inference 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 is demonstrated in a growth mixture analyses of data from the National Longitudinal Study of Youth, and a 2-group measurement invariance analysis of the Holzinger-Swineford data.
从模型拟合分析得出的推断和结论通常基于单一的“最佳拟合”模型。如果使用相同的数据进行模型选择和推断，模型选择的不确定性就会被忽略。我们阐述了使用相同数据进行模型选择和推断可能导致的第一类错误膨胀，然后提出一种基于简单自抽样法的方法，以模型选择率来量化模型选择的不确定性。选择率可以解释为拟合模型重复概率的估计值。在对国家青少年纵向研究数据进行的生长混合分析以及对霍尔津格 - 斯温福德数据进行的两组测量不变性分析中，展示了自抽样法处理模型选择不确定性的益处。
