Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
ICPSR: Machine Learning for the Analysis of Text as Data Instructor(s): Brice Acree, Ohio State University Quantitative analysis of digitized text represents an exciting and challenging frontier of data science across a broad spectrum of disciplines. From the … Read more
Multi-Level Modeling Instructor(s): Michelle Dion, McMaster University Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories … Read more
ICPSR: Growth Mixture Models (GMM): A Structural Equation Modeling Approach - Class Delayed to Start Time of 12:30pm Monday Instructor(s): Shawn Bauldry, Purdue University The Growth Mixture Model (GMM) is an extension of the Latent Growth Curve Model … Read more
ICPSR: Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach Instructor(s): Kenneth Bollen, University of North Carolina at Chapel Hill A powerful method for analyzing longitudinal data is Latent Growth Curve Models (LGCM). LGCM allow each case … Read more
Network Analysis: Statistical Approaches Instructor(s): John Skvoretz, University of South Florida This workshop covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties … Read more
