The field of machine learning is most commonly associated with “big data”: how we can use massive datasets to make better predictions about things like credit card fraud, Netflix recommendations, and the like. Though machine learning has been most influential … Read more

This workshop introduces the Bayesian multilevel model framework. Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. The Bayesian approach simplifies several of the assumptions of the classical techniques … 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

Mixed methods research typically refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis strategies. This course examines best practices, through the review of exemplars and discussion of theoretical approaches, in “mixing” methods and integrating … Read more

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 based on attributes (e.g. homophily), and network effects on individual attributes (social influence … Read more

This class has been CANCELLED.   This workshop will cover strategies for obtaining visual displays of quantitative information. We will discuss ways to, quite literally, look at data and statistical models in pictorial form. This is important because graphical representations … Read more

ICPSR – Bayesian Latent Variable Analysis   This workshop will focus on a variety of commonly used latent variable techniques from the Bayesian perspective. The Bayesian paradigm is, in many ways, superior to classic techniques for estimating models with latent … Read more

ICPSR – Machine Learning for the Analysis of Text as Data   Quantitative analysis of digitized text represents an exciting and challenging frontier of data science across a broad spectrum of disciplines. From the analysis of physicians’ notes to identify … Read more

This Course has been CANCELLED   The Growth Mixture Model (GMM) is an extension of the Latent Growth Curve Model (LGCM) that identifies distinct subgroups of growth trajectories and allows individuals to vary around subgroup-specific mean trajectories. Conventional growth modeling … Read more

ICPSR – Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach   A powerful method for analyzing longitudinal data is Latent Growth Curve Models (LGCM). LGCM allow each case in a sample to have individual trajectories (“latent curves” or … Read more