Doubly robust (DR) estimators that combine regression adjustments and inverse probability weighting (IPW) are widely used in causal inference with observational data because they are claimed to be consistent when either the outcome or the treatment selection model is correctly specified (Scharfstein et al., 1999). This property of "double…

Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these "knowns" about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and…

BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…

In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their…

To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…

We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…

Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead…

Evaluations of vocational education and training (VET) programs play a key role in informing training policy in Australia and elsewhere. Increasingly, such evaluations use observational data from surveys or administrative collections to assess the effectiveness of VET programs and interventions. The difficulty associated with using observational…

This paper is based on previous studies in applying propensity score methods to study multiple treatment variables to examine the causal moderator effect. The propensity score methods will be demonstrated in a case study to examine the causal moderator effect, where the moderators are categorical and continuous variables. Moderation analysis is an…

The central role of the propensity score analysis (PSA) in observational studies is for causal inference; as such, PSA is often used for making causal claims in research articles. However, there are still some issues for researchers to consider when making claims of causality using PSA results. This summary first briefly reviews PSA, followed by…

In this paper the authors present a new method for sample selection for scale-up experiments. This method uses propensity score matching methods to create a sample that is similar in composition to a well-defined generalization population. The method they present is flexible and practical in the sense that it identifies units to be targeted for…

Survey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least…

In this article, a Bayesian model selection approach is introduced that can select the best of a set of inequality and equality constrained hypotheses for contingency tables. The hypotheses are presented in terms of cell probabilities allowing researchers to test (in)equality constrained hypotheses in a format that is directly related to the data.…

This article discusses comparisons of means using exploratory and confirmatory approaches. Three methods are discussed: hypothesis testing, model selection based on information criteria, and Bayesian model selection. Throughout the article, an example is used to illustrate and evaluate the two approaches and the three methods. We demonstrate that…