Hierarchical Linear Models (HLM) have been used extensively for value-added analysis, adjusting for important student and school-level covariates such as socioeconomic status. A recently proposed alternative, the Layered Mixed Effects Model (LMEM) also analyzes learning gains, but ignores sociodemographic factors. Other features of LMEM, such as…

A growth curve approach to longitudinal profile analysis is presented by which univariate and multivariate descriptions of achievement and rate of growth are provided, and intraindividual strengths and weaknesses are computed for profiles of achievement and progress. Analyses are reported for simulated data conforming to a straight-line growth…

Analysis of multivariate aptitude-treatment-interaction data is discussed. Analysis procedures are illustrated using data from a science education study to describe the interaction between student reading ability and the time allowed in an individualized instructional approach for multiple achievement and attitudinal outcomes. (Author/RL)

To correct for the effects of measurement error on structural parameter estimates, many researchers are now estimating models of educational achievement with LISREL. In order to estimate such models it is desirable to obtain multiple manifest measures of the latent constructs. Many researchers restrict their models to two manifest measures per…