Causal Inference, Group-based Trajectory Modeling, and more!
Today we have six more short workshops to feature, all starting the week of May 18. If you are reading perhaps tomorrow or even later, don’t worry. This email is not time sensitive nor self-destructive. As part of a data archive we are strongly against any Mission: Impossible-style archiving protocols. See what we have to offer for the second week of our 2020 program:
Introduction to Causal Inference (May 18-22)
Instructor: Stephen Jesse, University of Texas
This course will cover methods for making causal claims with observational, rather than experimental, data. It will cover the potential outcomes framework for thinking about causality and will introduce students to several approaches for estimating causal effects when true randomized experiments are not possible. The topics covered will include regression, matching, instrumental variables, differences in differences, and regression discontinuity designs. Students will also learn how to think about potential problems with causal claims including selection bias, controlling for post treatment variables, and other issues.
Linear Regression Analysis in the Social Sciences (May 18-22)
Instructors: Patrick Shea, University of Houston; Sunny Wong, University of Houston
The course will be centered around several main topics covering the basic analysis of ordinary least squares (OLS), the technique of estimating bivariate and multivariate regression models, the overall fitness of a regression equation, and the hypothesis and diagnostic testings, and more. This course takes the “learning by doing” approach by discussing the major themes in regression analysis with detailed examples, which show how the subject works in practice using Stata.
Network Analysis (May 18-22)
Instructor: Jimi Adams, University Colorado Denver
This course will lay the groundwork behind social network analysis (SNA) from conceptual, mathematical, empirical and computational perspectives. This approach will draw from the rich multidisciplinary history that has shaped the field’s development – incorporating perspectives from sociology to physics, math to public health.
SNA differs from other analytic perspectives in ways that require unique strategies for data collection, storage, descriptive and statistical analysis. The course will address each of these by sampling from a range of the most commonly used analytic concepts, and demonstrate their empirical applications, and computation (primarily in R).
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 or contagion models). Topics include random graph distributions, statistical models for local structure (dyads and triads), biased net models for complete networks and for aggregated tie count data, dyadic independence models, autocorrelation models, exponential random graph models, and stochastic models for dynamic network analysis.
Introduction to Mixed Methods Research (May 20-22)
Instructor: Shiri Noy, Denison University
The goal of the course is to introduce students to conceptual and practical frameworks and considerations in developing, designing, implementing, executing, analyzing, presenting, and writing up mixed methods research. Mixed methods research typically refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis techniques. In the course we will interrogate the utility of mixed methods research in light of the limitations of any specific methodological tool and approach, and review the theory and practice of mixed methods research in the social sciences.
Group-based Trajectory Modeling for the Medical and Social Sciences (May 20-22)
Instructors: Daniel Nagin, Carnegie Mellon University; Thomas Loughran, Pennsylvania State University
A developmental trajectory describes the course of a behavior over age or time. This two-and-a-half-day workshop aims to provide participants with the training to apply a group-based method for analyzing developmental trajectories. Participants should have a statistical background of matrix algebra and multiple regression. This workshop is targeted at researchers from the social and behavioral sciences and medicine who investigate developmental processes.
REMINDER: All 2020 Summer Program courses are being offered virtually!