This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model’s posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

中文翻译：

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使用贝叶斯模型平均优化预测：使用大规模教育评估的示例。

本文概述了贝叶斯模型平均，将其作为优化用于大规模教育评估的通用统计模型的预测性能的一种方法。贝叶斯框架认识到，除了参数不确定性之外，模型本身的选择还存在不确定性。解决模型不确定性问题的贝叶斯方法是贝叶斯模型平均的方法。贝叶斯模型平均在可能的模型空间中搜索满足某些科学原理的一组子模型，然后对这些子模型的系数进行平均，并按每个模型的后验模型概率（PMP）加权。根据某些评分规则，使用加权系数进行预测已显示出最佳的预测性能。我们通过三个示例展示贝叶斯平均模型在教育研究中的预测效用：贝叶斯回归分析，贝叶斯逻辑回归以及贝叶斯结构方程建模的最新方法。在每种情况下，模型平均估计均显示出比基于预测覆盖率和对数得分规则的任何子模型更好的目标结果预测。讨论了当目标是策略上下文中的最佳预测时对大规模评估设计的影响。与基于预测覆盖率和对数得分规则的任何子模型相比，模型平均估计值显示出对目标结果的更好预测。讨论了当目标是策略上下文中的最佳预测时对大规模评估设计的影响。与基于预测覆盖率和对数得分规则的任何子模型相比，模型平均估计值显示出对目标结果的更好预测。讨论了当目标是策略上下文中的最佳预测时对大规模评估设计的影响。