Redefining Populations of Inference for Generalizations from Small Studies

TL;DR

This paper investigates how redefining the inference population can enhance the validity of generalizations from small educational studies, addressing issues of bias and precision in treatment impact estimates.

Contribution

It introduces and compares two frameworks for redefining populations to improve generalizations from small samples in educational research.

Findings

Redefining populations can reduce bias in small sample studies.

Different methods of population redefinition have varying impacts on estimate accuracy.

Guidelines are provided for practitioners to apply these methods effectively.

Abstract

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.