One approach for combining single-case data involves use of multilevel modeling. In this article, the authors use a Monte Carlo simulation study to inform applied researchers under which realistic conditions the three-level model is appropriate. The authors vary the value of the immediate treatment effect and the treatment's effect on the…

A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…

The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5…

The goal of this study was to validate measures and assess the effects of collaborative group-learning methods in real classrooms on 3 specific dependent variables: feelings of campus connectedness, academic classroom community, and effective group processing (2 factors). Confirmatory factor analysis were conducted to evaluate a 4-factor model.…

The authors investigated the influence of effect size and comment inclusion on readers' perceptions of research results. In three experiments, undergraduates, graduates, and faculty read a journal article that either included or did not include an effect size and commentary about the effect size. Contrary to a previous study by Robinson, Fouladi,…

Compared the Type I error rate and the power to detect differences in slopes and additive treatment effects of analysis of covariance (ANCOVA) and randomized block designs through a Monte Carlo simulation. Results show that the more powerful option in almost all simulations for tests of both slope and means was ANCOVA. (SLD)

Compared the power of analysis of covariance (ANCOVA) and two types of randomized block designs as a function of the correlation between the concomitant variable and the outcome measure, the number of groups, the number of participants, and nominal power. Discusses advantages of ANCOVA. (Author/SLD)