Dr. Marc Scott

Professor

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Biography

Marc Scott is a Professor and Co-Department Chair in the Department of Applied Statistics at NYU Steinhardt. He is also the Co-Director of the Center for Practice Research Intersection Information, Society, Methodology (PRIISM). His research focuses on the development of statistical models for longitudinal data, with an emphasis on latent variable approaches. Scott has contributed to examining trends in wage inequality and exploring applications of statistical models in medical histories and psychological profiles. His recent work involves multi-channel model-based sequence analysis, sensitivity analysis, multilevel models, and Bayesian computational methods. Scott teaches various courses in applied statistics, including Multi-Level Models: Growth Curves, Practicum in Statistical Computing, and Supervised & Unsupervised Machine Learning. He plays a pivotal role in guiding research practices at the university and has been involved in the launch of new programs in applied statistics.

Research Interests

Bayesian Models Categorical Sequence Analysis Causal Inference Model-Based Clustering Multilevel Models Single Subject Designs Applied Statistics Computational Social Science

Courses

Multi-Level Models: Growth Curves Practicum in Statistical Computing Supervised & Unsupervised Machine Learning Applied Spatial Statistics Generalized Linear Models Multi-Level Modeling: Nested Data/Longitudinal Data

Requirements for New York University Steinhardt

Doctorate Program
Requirements
TOEFL
Total
Required:100
Prerequisites
Master's degree preferred but not required for 54-credit track
Application Checklist
  • Résumé/CV
  • MCC Essay (Research Area/Topic)
  • Writing Sample (Master's thesis or equivalent)
  • Three Letters of Recommendation
  • Official Transcripts
  • English Proficiency Exam (if applicable)
Specialization Notes

Department of Media, Culture, and Communication - PhD Program focusing on Global and Transcultural Studies, Technology and Society, and Visual Culture.