The effectiveness of weighted least squares means and variance adjusted based fit indices in assessing local dependence of the rasch model: Comparison with principal component analysis of residuals.

Local independence is a principal assumption of applying latent variable models. Violations of this assumption might be stemmed from dimensionality (trait dependence) and statistical independence of item responses (response dependence). The purpose of this study is to evaluate the sensitivity of weighted least squares means and variance adjusted (WLSMV) based global fit indices to violations of local independence in Rasch models, and compare those indices to principal component analysis of residuals (PCAR) that is widely used for Rasch models.

Teacher and student race as a predictor for negative feedback during instruction.

There is a long and persistent gap between the academic achievement of White and Black students in America's schools. Further, Black students are suspended from school at a rate that is more than three times greater than White students. While there has been some suggestion that perhaps teacher-student racial matching may be part of a solution, the research does not currently provide adequate support for teacher race alone to be sufficiently effective.

Application of the Korean Version of the Modified Barthel Index: Development of a keyform for use in Clinical Practice.

Objective: To demonstrate the clinical application of the Korean version of the Modified Barthel Index (K-MBI) using Rasch analysis.

Methods: A total of 276 patients with neurological disorders were assessed with the K-MBI in outpatient clinics. The Rasch partial-credit model was used to generate a keyform based on investigating the psychometric properties of the K-MBI, including dimensionality, precision (person strata and reliability), and hierarchical item difficulty.