comparison of three growth modeling techniques in the multilevel analysis of longitudinal academic achievement scores: Latent growth modeling, hierarchical linear modeling, and longitudinal profile analysis via multidimensional scaling

This study introduces three growth modeling techniques: latent growth modeling (LGM), hierarchical linear modeling (HLM), and longitudinal profile analysis via multidimensional scaling (LPAMS). It compares the multilevel growth parameter estimates and potential predictor effects obtained using LGM, HLM, and LPAMS. The purpose of this multilevel growth analysis is to alert applied researchers to selected analytical issues that are required for consideration in decisions to apply one of these three approaches to longitudinal academic achievement studies. The results indicated that there were no significant distinctions on either mean growth parameter estimates or on the effects of potential predictors to growth factors at both the student and school levels. However, the study also produced equivocal findings on the statistical testing of variance and covariance growth parameter estimates. Other practical issues pertaining to the three growth modeling methods are also discussed.     

Observed lesson structure during the first year of secondary education: Exploration of change and link with academic engagement

This study investigates whether lesson structure (LS) matters and which components are important for academic engagement during the first grade of secondary education. Data from videoed lessons of 10 Dutch and 12 Indonesian teachers analyzed using an observation protocol show that six LS components are found, that between class and over measurement variability in LS is evident, and that on average LS change is not a linear function of time. Class differences over time do exist and several personal and contextual characteristics affect LS. Links between LS (student work time, reviewing and introducing new content) and academic engagement are evident.