Estimating correlates of growth between mathematics and science achievement via a multivariate multilevel design with latent variables

The purpose of the present study was to improve a multivariate multilevel model in the research literature which estimates the consistency in the rates of growth between mathematics and science achievement among students and schools. We introduced a new multivariate multilevel model via a latent variable approach. Data from the Longitudinal Study of American Youth (LSAY) provided scores on basic skills, algebra, geometry, and quantitative literacy as indicators of the latent variable mathematics achievement, and scores on biology, physics, and environmental science as indicators of the latent variable science achievement. Using this multivariate multilevel model with latent variables, we examined the relationship between growth in mathematics and science achievement during middle and high school among students and schools, and we demonstrated that such a model was more sensitive to this relationship.