The standard methodology for many large-scale assessments in education involves regressing latent variables on numerous contextual variables to estimate proficiency distributions. To reduce the number of contextual variables used in the regression and improve estimation, we propose and evaluate principal component analysis on the covariance matrix…

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

This paper evaluated multidimensional linking procedures with which multidimensional test data from two separate calibrations were put on a common scale. Data were simulated with known ability distributions varying on two factors which made linking necessary: mean vector differences and variance-covariance (v-c) matrix differences. After the…