Decomposing variables into between and within components are often required in multilevel analysis. This method of decomposition should not ignore possible unreliability of an observed group mean (i.e., arithmetic mean) that is due to small cluster sizes and can lead to substantially biased estimates. Adjustment procedures that allow unbiased…

Variance heterogeneity is a common feature of educational data when treatment differences expressed through means are present, and often reflects a treatment by subject interaction with respect to an outcome variable. Identifying variables that account for this interaction can enhance understanding of whom a treatment does and does not benefit in…

Discipline-based education researchers have a natural laboratory--classrooms, programs, colleges, and universities. Studies that administer treatments to multiple sections, in multiple years, or at multiple institutions are particularly compelling for two reasons: first, the sample sizes increase, and second, the implementation of the treatments…

Measures of variability are successfully used in predictive modeling in research areas outside of education. This study examined how standard deviations can be used to address research questions not easily addressed using traditional measures such as group means based on index variables. Student survey data were obtained from the Organisation for…

Two previous studies examining 5 empirical educational psychology research journals (Hsieh et al., 2005; Robinson, Levin, Thomas, Pituch, & Vaughn, 2007) found that in the 21-year period from 1983 to 2004, there was a decrease in intervention and randomized experimental research, whereas in the 10-year period from 1994 to 2004, there was an…

Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…

The purpose of this project is to demonstrate the practical methods developed to utilize a dataset consisting of both multivariate and multilevel effect size data. The context for this project is a large-scale meta-analytic review of the predictors of academic achievement. This project is guided by three primary research questions: (1) How do we…

This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…

Closing the achievement gap in public education is a worthy goal that has been included as a top priority in the No Child Left Behind Act of 2001 (2002). This study analyzed the most salient predictors at the student and school levels to identify their long-term impact on mathematics achievement from the elementary grades to high school. The…

The current teacher workforce is younger, less experienced, more likely to turnover, and more diverse in preparation experiences than the workforce of two decades ago. Research shows that inexperienced teachers are less effective, but we know little about the effectiveness of teachers with different types of preparation. In this study, we classify…