In this response, we first show that Simpson's proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.

A recent paper uses Bayes factors to argue a large minority of rigorous, large-scale education RCTs are "uninformative." The definition of "uninformative" depends on the authors' hypothesis choices for calculating Bayes factors. These arguably overadjust for effect size inflation and involve a fixed prior distribution,…

The present article considers a fundamental question in evaluation research: "By how much do program effects vary across sites?" The article first presents a theoretical model of cross-site impact variation and a related estimation model with a random treatment coefficient and fixed site-specific intercepts. This approach eliminates…

Evaluating and monitoring large-scale distance learning programs require different techniques, systems, and analysis methods. This work presents challenges in evaluating and monitoring digital inclusion training programs, considering the aspects inherent in large-scale distance training, and reports an approach based on network and distance…

The Multiattribute Utility Model combines subjective goal definition with objective data analysis. Goals are defined, ranked, and weighted. Subjective opinions about their attainment are assigned against decision alternatives considered by the school. Bayesian analysis of data enables revision of prior opinions about the realization of goals…

Trade-offs are implicit in choosing a subjective or objective method for evaluating social programs. The differences between Bayesian and traditional statistics, decision and cost-benefit analysis, and anthropological and traditional case systems illustrate trade-offs in choosing methods because of limited resources. (SLD)

The Bayesian inferential process is modified for use in an aggregate meta-analytic evaluation. Compared with the average effect size meta-analytic approach, the Bayesian approach was more sensitive, more consistent and more powerful. This approach is recommended when primary data are not available and when all evaluations involve comparisons of…

Evaluation is discussed as an information-gathering process. Currently popular evaluation programs are reviewed in relation to decision making and various approaches that seem to contribute to the decision utility of evaluation (e.g. classical approaches, Bayesian approaches, adaptive designs, and model-based evaluations) are described. (Author/AL)

In the late 1990s, the Korea Ministry of Education and Human Resources, in response to concern over the relatively low standing of the nation's universities and researchers, launched the Brain Korea 21 program BK21). BK21 seeks to make Korean research universities globally competitive and to produce more high-quality researchers in Korea. It…

This study examined the appropriateness of the use of criterion referenced tests as a means of controlling an individual student's advancement to the next level of instruction or retention in the current unit in the PLATO Air Force Base Computer-Based Education project at Chanute. The study was also concerned with program evaluation, which…

The philosophy and assumptions of the Multi-Attribute Utility-Bayesian Decision Theoretic model (MAUT-Bayesian model) are presented. The evaluator uses the MAUT-Bayesian model along with the knowledge of the decision-maker's, and perhaps the evaluator's own values to decide what data should be collected. Appropriate data are presented to the…

The Multi-Attribute Utilities Theory (MAUT), a model for the evaluation of public postsecondary programs, is presented and is applied to a study of WINNERS (Women's Inner-City Educational Resources Service) with an analysis of the effects of the model on the program. The first chapter provides an overview of the MAUT approach, outlining 16 steps…