Suchergebnisse

Background: An important question in the design of experiments is how to ensure that the findings from the experiment are generalizable to a larger population. This concern with generalizability is particularly important when treatment effects are heterogeneous and when selecting units into the experiment using random sampling is not possible—two conditions commonly met in large-scale educational experiments. Method: This article introduces a model-based balanced-sampling framework for improving generalizations, with a focus on developing methods that are robust to model misspecification. Additionally, the article provides a new method for sample selection within this framework: First units in an inference population are divided into relatively homogenous strata using cluster analysis, and then the sample is selected using distance rankings. Result: In order to demonstrate and evaluate the method, a reanalysis of a completed experiment is conducted. This example compares samples selected using the new method with the actual sample used in the experiment. Results indicate that even under high nonresponse, balance is better on most covariates and that fewer coverage errors result. Conclusion: The article concludes with a discussion of additional benefits and limitations of the method.

Background: Large-scale randomized experiments are important for determining how policy interventions change average outcomes. Researchers have begun developing methods to improve the external validity of these experiments. One new approach is a balanced sampling method for site selection, which does not require random sampling and takes into account the practicalities of site recruitment including high nonresponse. Method: The goal of balanced sampling is to develop a strategic sample selection plan that results in a sample that is compositionally similar to a well-defined inference population. To do so, a population frame is created and then divided into strata, which "focuses" recruiters on specific subpopulations. Units within these strata are then ranked, thus identifying "replacements" similar to sites that can be recruited when the ideal site refuses to participate in the experiment. Result: In this article, we consider how a balanced sample strategic site selection method might be implemented in a welfare policy evaluation. Conclusion: We find that simply developing a population frame can be challenging, with three possible and reasonable options arising in the welfare policy arena. Using relevant study-specific contextual variables, we craft a recruitment plan that considers nonresponse.

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.