2022 Methods Workshop
Presented by the Learning Performance Research Center
Free to all attendees through sponsorship by the Berry Family Distinguished Professorship!
June 1st and 2nd – 9:00 a.m. to 1:00 p.m.
See below for information about a special pre-workshop event!
Virtual gathering on Zoom
About the Workshop
An Introduction to Machine Learning for the Social Sciences
What is machine learning and how does it compare to statistics and quantitative methods? Machine learning combines methods from computer science and statistics that allow researchers to interpret data. In particular, machine learning algorithms allow users to analyze datasets that would be difficult or impossible with standard statistical models. This course will introduce participants to the field of machine learning with specific focus several standard supervised machine learning algorithms although some unsupervised machine learning algorithms will be introduced as well. Participants are welcome to bring their own data for analysis, but sample datasets will be provided for demonstrations and practice during the workshop. This course will be taught in R. Although all code will be provided, some statistical modeling experience with R is recommended.
Presenter: Dr. Tracy Sweet
Tracy Sweet is an Associate Professor in the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology. She completed her Ph.D. in Statistics at Carnegie Mellon University and a M.A. in Mathematics at Morgan State University. Her research focuses on methods for social network analysis with particular focus on multilevel social network models. Recent projects include network interference, measurement error, and missing data. She serves as the Associate Director of Research for UMCP for the Maryland Longitudinal Data System Center and is currently overseeing projects applying data science and statistical methods to large-scale educational data. Finally, Dr. Sweet is committed to improving diversity in the fields of statistics and quantitative methodology.
Dr. Shenghai Dai from Educational Psychology will offer a brief session on using R via Zoom on May 31st from 10-11:30am and 12:30-2pm. Session 1 is an introduction to R, and Session 2 will cover basic analysis. Access to this event will be included in your registration.
Registration has closed for this event. We hope to see you next year!
Free to all attendees for 2022!
2021 – Tenko Raykov, Michigan State University – Longitudinal Modeling and Missing Data Analysis
2019 – Matthew McCrudden, Pennsylvania State University – Mixed Methods
2018 – Greg Hancock, University of Maryland – Latent Growth Curve Modeling
2017 – Roy Levy, Arizona State University – Foundations of Bayesian Statistical Modeling
2016 – Todd D. Little, Texas Tech University – Measurement, design, and analysis issues in longitudinal modeling with a particular focus on the longitudinal CFA model as the basis for both panel and latent growth curve modeling.
2015 – Greg Hancock, University of Maryland – Second Course in Structural Equation Modeling
2014 – Bethany Bray, Pennsylvania State University – An Introduction to Latent Class and Latent Profile Analysis
2013 – Greg Hancock, University of Maryland – A First Course in Structural Equation Modeling