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Short workshops in stats - starting May 16!


Subject: Short workshops in stats - starting May 16!

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Take a look at these short workshops!

The 2022 ICPSR Summer Program is just weeks away from officially beginning! It's hard to believe, but the calendar doesn't lie, and neither do we. Our first short workshops start May 16 with 2 courses about using R, and another on better measurement of variables in social science research. Read on for a highlight of some of our first workshops this year!

See our schedule for 2022 Short Workshops for the full list of courses.
Methods for Better Measurement of Social Scientific Variables
May 16-20
Instructor: Adam Enders, University of Louisville
*This course will be held entirely online.*

This course begins with an exploration of the concept of statistical reliability, including techniques designed to estimate reliability under different circumstances. We will then consider how to enhance the reliability of measures vis-à-vis multiple-variable scales. Next, we will survey several methodologies––such as principal components analysis and factor analysis––useful for examining the dimensionality of a set of variables. These methods help bolster both the reliability and validity of our measurements. Finally, we will learn how to properly execute these methods in conjunction with one another, what results to report in scientific books and articles, and other practical aspects of incorporating better measurement into research projects.
Introduction to Mixed Methods Research
May 23-27
Instructor: Shiri Noy, Denison University

Social scientists are often interested in untangling complex social issues, which require creative and expansive data and methods to address. 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. We will focus on practical tools and challenges confronted across the stages of mixed methods research.
Process Tracing in Qualitative and Mixed Methods Research
June 6-10
Instructor: Derek Beach, Aarhus University

Process tracing is a research method designed to learn how things work in real-world cases. Increasingly used across the social sciences and in applied policy evaluation, process tracing involves unpacking causal processes as they play out within cases and tracing them empirically, enabling within-case causal inferences about the processes that link causes and outcomes together. The course will combine pre-class readings with live sessions aimed at understanding the core elements of Process Tracing as a distinct case study method. Participants will be encouraged to use their own research to explore how Process Tracing methods can improve your own design.
Network Analysis: Statistical Approaches
June 6-17
Instructor: John Skvoretz, University of South Florida

The workshop covers advanced statistical methods for analyzing social network data.  It covers testing hypotheses about network structure (e.g. reciprocity, transitivity, closure, density, clustering, path lengths, geometrically weighted graph metrics), models for the formation of ties based on attributes (e.g. homophily) and on structural effects (e.g., closure in triads), and models for network effects on individual attributes (social influence or contagion models).  Topics include: random graph distributions, statistical analysis of local structural regularities in dyads and triads, assessment of hypotheses about graph-level indices, biased net models for realized ties and for complete networks, peer influence models and other regression based models for network data, exponential random graph models, and stochastic actor-oriented models.  Each session divides into lecture/discussion of methods and a lab using those methods. This workshop assumes that participants have already taken a first course in network analysis.
Full Short Workshop Schedule
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