Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results.…

In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…

Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…

Despite having drawn from empirical evidence and cumulative prior expertise in the formulation of research questions as well as study design, each study is treated as a stand-alone product rather than positioned within a sequence of cumulative evidence. While results of prior studies are typically cited within the body of prior literature review,…

This article explores the potential of Bayesian inference as a theoretical framework for describing how people evaluate hypotheses. First, it identifies a set of logically possible forms of non-Bayesian behavior. Second, it reviews existing research in a variety of areas to see whether these possibilities are ever realized. (Author/BW)

Statistical significance is not a sufficient condition for claiming a hypothesis has been supported. Constructive replications are more important. Statistically significant results may be meaningless while a sequence of nonsignificant results may be quite important. Gives advice on how to overcome some limitations of classifical statistical…

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)

Bayesian statistical methodology and its possible uses in the behavioral sciences are discussed in relation to the solution of problems in both the use and teaching of fundamental statistical methods, including confidence intervals, significance tests, and sampling. The Bayesian model explains these statistical methods and offers a consistent…

In this article, a new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores…

Four researchers at the Educational Testing Service describe what they consider some of the most vexing research problems they face. While these problems are not completely statistical, they all have major statistical components. Following the introduction (section 1), in section 2, "Problems with the Simultaneous Estimation of Many True…