I have three principal research interests: (1) multivariate methods for the analysis of change, (2) using multiple group and latent class models to understand divergent developmental processes, and (3) achievement and intellectual development. In the following paragraphs I provide detailed accounts of my work in these areas.
My research in this area focuses on methods to analyze repeated measures data to evaluate long-term systematic trends and interindividual differences therein. Such data are typical in the study of developmental changes in academic endeavors, such as mathematics and reading, as well as behavior and depression. These sorts of data often show systematic patterns of change; however the pattern and amount of change often vary over people making modeling of these types of data more complex. My research in this area has focused on the match between theory and model specification/selection (Grimm, 2007a; Grimm, Ram, & Hamagami, in press; Ram & Grimm, 2007), nonlinear forms of change (Grimm & Ram, 2009; Grimm, Ram, & Estabrook, in press; Grimm, Ram, & Hamagami, in press), separation of method-related variance and change (Grimm, Pianta, & Konold, 2009), and model specification (Grimm & Widaman, 2010).
My research in this area focuses on models for examining heterogeneity in development. The basic growth curve models allows for a specific type of heterogeneity as the variability in latent intercepts and slopes is normally distributed. Growth mixture models, a combination of the finite mixture model and latent growth curve, allow for heterogeneity to be examined in terms of latent classes with divergent developmental trajectories. My work in this area has focused on model specification (Grimm, McArdle, & Hamagami, 2007; Ram & Grimm, 2009), the incorporation of measurement models to aid in the determination of latent classes (Grimm & Ram, 2009), and modeling nonlinear trajectories with multiple latent classes (Grimm, Ram, & Estabrook, 2010).
My work in this area has focused primarily on changes in reading and mathematics skills during elementary school. I have published papers on the associations between changes in reading and mathematics and school-readiness predictors of reading and mathematics changes. This work showed that reading achievement was an important predictor of problem solving - often thought to require higher-level thinking. More recently, I’ve examined the influence of early cognitive and non-cognitive skills in predicting changes in reading and mathematics (Grimm, Steele, Mashburn, Burchinal, & Pianta, 2010; Grissmer, Grimm, Aiyer, Murrah, & Steele, 2010). The focus in Grimm et al. (2010) was on early behavior problems as previous work by Duncan and colleagues (2007) indicated that early behavioral measures showed little, if any, predictive power to later achievement. In a re-analysis we fit more sophisticated longitudinal models and accounted for nonlinear associations between behavior problems and academic trajectories. We found that early reading and mathematics skills remained the strongest predictor of changes in reading and mathematics, early attention was a strong and consistent predictor, and early behavior problems were inconsistent predictors of change in achievement. In each dataset there were moderate associations between behavior problems and changes in achievement; however, the aspect of behavior problems (externalizing, social skills, & internalizing) and achievement (reading & mathematics) varied.