Marc A. Scott
Professor of Applied Statistics
Dr. Scott's research involves the development of statistical models for longitudinal data. Using a latent variable approach, he developed a new class of covariance models for such data, and has used them to examine trends in wage inequality. More recent applications include medical histories. He also works on models for longitudinal sequence analysis. In the educational setting, such models examine the influence of the entire pathway (e.g., the timing of educational and employment spells and interruptions to these) on an outcome measure such as wages or degree completion. In current work on low-wage labor markets, such models seek to find similar structure in the career histories and education of workers to identify more and less successful career ladders. More recent work involves sensitivity analysis for multilevel models and Bayesian computational methods.
Dr. Scott teaches Multi-Level Models: Growth Curves / Nested Data (APSTA-GE.2040/41/42), and Classification and Clustering (APSTA-GE.2011), He serves as co-director with Lisa Stulberg of the Interdepartmental Research Studies (IDRS) program; follow this link to website for descriptions of this program and links to course schedules and syllabi. In Fall 2008, Steinhardt launched a new Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) , for which he and Jennifer Hill serve as co-directors. In 2014, Steinhardt launched a new Masters of Science in Applied Statistics for Social Research (MS-A3SR), for which he and Jennifer Hill serve as co-directors.