Marc A. Scott

Professor of Applied Statistics

Marc A. Scott

Phone: 212-992-9407

Dr. Scott's research involves the development of statistical models for longitudinal data. Using latent variable approaches, he develops new classes of covariance models for such data. He has used them to examine trends in wage inequality, with more recent applications including medical histories and psychological profiles. 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 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 multi-channel and model-based sequence analysis, sensitivity analysis for multilevel models and Bayesian computational methods.

Dr. Scott teaches Multi-Level Models: Growth Curves / Nested Data (APSTA-GE.2040/41/42), Practicum in Statistical Computing (APSTA-GE.2352), Supervised and Unsupervised Machine Learning (APSTA-GE.2011), and Spatial Statistics (APSTA-GE.2015). He serves as co-director with Lisa Stulberg of the Interdepartmental Research Studies (IDRS) program. In Fall 2008, Steinhardt launched a new applied statistics center, PRIISM (Center for Practice and Research at the Intersection of Information, Society, and Methodology), 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.