4.0

credits

Average Course Rating

Reviews concepts, key assumptions, and published applications of measurement theory, including true scores and counterfactual outcomes, latent variables, and validity. Explores novel applications of item response theory to refinement of measures, assessment of differential item functioning, and calibration of metrics across diverse samples. Considers analysis of novel types of data such as biomarkers, latent class analysis, item response theory, latent growth curve models for longitudinal data and their extensions (e.g., growth mixture modeling, piecewise growth modeling, parallel process growth modeling), bivariate dual change score models, and cross-study statistical harmonization. Draws examples from epidemiologic applications in the behavioral and social sciences. Offers students opportunities for applying lessons from didactic lectures in a laboratory setting using prepared examples.