Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we…

Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…

Collapsing categories is a commonly used data reduction technique; however, to date there do not exist principled methods to determine whether collapsing categories is appropriate in practice. With ordinal responses under the partial credit model, when collapsing categories, the true model for the collapsed data is no longer a partial credit…

This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…

In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…

An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis,…

The use of ex post (historical) simulation statistics as means of evaluating latent growth models is considered, and a variety of simulation quality statistics are applied to such models. Results illustrate the importance of using these measures as adjuncts to more traditional forms of model evaluation. (SLD)

The authors examine the asymptotic effect of omitting a random coefficient in the multilevel model and derive expressions for the change in (a) the variance components estimator and (b) the estimated variance of the fixed effects estimator. They apply the method of moments, which yields a closed form expression for the omission effect. In…

The concomitant-variable latent-class model is described for situations with continuous explanatory variables, and an EM estimation procedure to estimate the model is presented. The model is applied to the study of crime among ethnic groups in the Netherlands, and its utility is demonstrated. (SLD)

Demonstrates Markov chain Monte Carlo (MCMC) techniques that are well-suited to complex models with Item Response Theory (IRT) assumptions. Develops an MCMC methodology that can be routinely implemented to fit normal IRT models, and compares the approach to approaches based on Gibbs sampling. Contains 64 references. (SLD)

Presents a model-based approach to rater reliability for essays read by multiple raters. The approach is motivated by generalizability theory, and variation of rater severity and rater inconsistency is considered in the presence of between-examinee variations. Illustrates methods with data from standardized educational tests. (Author/SLD)

Considers an approach to validating the selection of education indicators by incorporating them into a multilevel structural model and using the estimates from that model in policy-relevant simulations. The potential of this approach is demonstrated with data from the National Education Longitudinal Study of 1988. (SLD)