For a testing program with frequent administrations, it is important to understand and monitor the stability and fluctuation of test performance across administrations. Different methods have been proposed for this purpose. This study explored the potential of using multilevel analysis to understand and monitor examinees' test performance across…

Alternatives to vertical scales are compared for measuring longitudinal academic growth and for producing school-level growth measures. The alternatives examined were empirical cross-grade regression, ordinary least squares and logistic regression, and multilevel models. The student data used for the comparisons were Arabic Grades 4 to 10 in…

The hierarchical latent regression model (HLRM) is a flexible framework for estimating group-level proficiency while taking into account the complex sample designs often found in large-scale educational surveys. A complex assessment design in which information is collected at different levels (such as student, school, and district), the model also…

A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at improving both parameters and variance estimates via a two-level hierarchical linear model. This model falls naturally within the…