Burcu Kaniskan’s research while affiliated with Pearson Inc and other places

Given that the root mean square error of approximation (RMSEA) is currently one of the most popular measures of goodness-of-model fit within structural equation modeling (SEM), it is important to know how well the RMSEA performs in models with small degrees of freedom (df). Unfortunately, most previous work on the RMSEA and its confidence interval has focused on models with a large df. Building on the work of Chen et al. to examine the impact of small df on the RMSEA, we conducted a theoretical analysis and a Monte Carlo simulation using correctly specified models with varying df and sample size. The results of our investigation indicate that when the cutoff values are used to assess the fit of the properly specified models with small df and small sample size, the RMSEA too often falsely indicates a poor fitting model. We recommend not computing the RMSEA for small df models, especially those with small sample sizes, but rather estimating parameters that were not originally specified in the model.

Standards-based progress reports (SBPRs) require teachers to grade students using the performance levels reported by state tests and are an increasingly popular report card format. They may help to increase teacher familiarity with state standards, encourage teachers to exclude nonacademic factors from grades, and/or improve communication with parents. The current study examines the SBPR grade-state test score correspondence observed across 2 years in 125 third and fifth grade classrooms located in one school district to examine the degree of consistency between grades and state test results. It also examines the grading practices of a subset of 37 teachers to determine whether there is an association between teacher appraisal style and convergence rates. A moderate degree of grade-test score convergence was observed using three agreement estimates (coefficient kappa, tau-b correlations, and classroom-level mean differences between grades and test scores). In addition, only small amounts of grade-test score convergence were observed between teachers; a much greater proportion of variance lay within classrooms and subjects. Appraisal style correlated weakly with convergence rates, but was most strongly related to assigning students to the same performance level as the test. Therefore using recommended grading practices may improve the quality of SBPR grades to some extent.

This article provides an illustration of growth curve modeling within a multilevel framework. Specifically, we demonstrate coding schemes that allow the researcher to model discontinuous longitudinal data using a linear growth model in conjunction with time-varying covariates. Our focus is on developing a level-1 model that accurately reflects the shape of the growth trajectory. We demonstrate the importance of adequately modeling the shape of the level-1 growth trajectory in order to make inferences about the importance of both level-1 and level-2 predictors.