Model uncertainty is pervasive in real world analysis situations and is an often-neglected issue in applied statistics. However, standard approaches to the research process do not address the inherent uncertainty in model building and, thus, can lead to overconfident and misleading analysis interpretations. One strategy to incorporate more flexible models is to base inferences on predictive modeling. This approach provides an alternative to existing explanatory models, as inference is focused on the posterior predictive distribution of the response variable. Predictive modeling can advance explanatory ambitions in the social sciences and in addition enrich the understanding of social phenomena under investigation. Bayesian stacking is a methodological approach rooted in Bayesian predictive modeling. In this paper, we outline the method of Bayesian stacking but add to it the approach of posterior predictive checking (PPC) as a means of assessing the predictive quality of those elements of the stacking ensemble that are important to the research question. Thus, we introduce a viable workflow for incorporating PPC into predictive modeling using Bayesian stacking without presuming the existence of a true model. We apply these tools to the PISA 2018 data to investigate potential inequalities in reading competency with respect to gender and socio-economic background. Our empirical example serves as rough guideline for practitioners who want to implement the concepts of predictive modeling and model uncertainty in their work to similar research questions.

翻译：模型不确定性在实际数据分析场景中普遍存在，却常是应用统计领域被忽视的问题。然而，标准的研究流程并未解决模型构建中固有的不确定性，可能导致过度自信且具有误导性的分析结论。为纳入更灵活的模型，一种策略是基于预测建模进行推断。这种方法为现有解释性模型提供了替代方案，其推断聚焦于响应变量的后验预测分布。预测建模既能推动社会科学领域的解释性研究目标，也能丰富对受调查社会现象的理解。贝叶斯堆叠是一种根植于贝叶斯预测建模的方法论。本文在阐述贝叶斯堆叠方法的基础上，引入后验预测检验作为评估堆叠集成中与研究问题密切相关成分的预测质量的手段。由此，我们提出一个可行的工作流程，无需预设真实模型的存在，即可将后验预测检验融入基于贝叶斯堆叠的预测建模。我们将这些工具应用于PISA 2018数据，以探究性别与社会经济背景在阅读能力方面的潜在不平等。该实证案例可为实践者处理类似研究问题、在其工作中实施预测建模与模型不确定性概念提供粗略指南。