论文标题
重新定义小型研究推断的推断人群
Redefining Populations of Inference for Generalizations from Small Studies
论文作者
论文摘要
随着教育实验研究的增长，决策者和从业人员不仅有兴趣了解什么有效，而且对干预措施有效。对研究发现的普遍性的这种兴趣受益于旨在改善概括的统计方法的进步，尤其是在未随机选择原始研究样本的情况下。但是，一个挑战是概括通常基于小型研究样本。有限的数据会影响治疗影响估计的精度和偏见，据此质疑概括的有效性。这项研究探讨了重新定义推理人群是改善小型研究概括的有用工具的程度。我们讨论了重新定义人群的两个主要框架，并将方法应用于基于教育中完整的群集随机试验的经验示例。我们讨论了各种方法重新定义人口的含义，并根据有兴趣使用重新定义的从业者的指导和一些建议。
With the growth in experimental studies in education, policymakers and practitioners are interested in understanding not only what works, but for whom an intervention works. This interest in the generalizability of a study's findings has benefited from advances in statistical methods that aim to improve generalizations, particularly when the original study sample is not randomly selected. A challenge, however, is that generalizations are frequently based on small study samples. Limited data affects both the precision and bias of treatment impact estimates, calling into question the validity of generalizations. This study explores the extent to which redefining the inference population is a useful tool to improve generalizations from small studies. We discuss two main frameworks for redefining populations and apply the methods to an empirical example based on a completed cluster randomized trial in education. We discuss the implications of various methods to redefine the population and conclude with guidance and some recommendations for practitioners interested in using redefinition.
