This workshop on social network analysis offers participants an in-depth introduction to the principles and methodologies used to study the structure and dynamics of social networks. Attendees will explore concepts such as centrality and network density, and learn how to apply these in analyzing relationships within various social systems. Through hands-on exercises, participants will utilize software tools like Gephi or iGraph to visualize and interpret network data. The workshop aims to equip...

Are you a faculty member, staff researcher, or graduate student looking to enhance your peer review skills? Do you find reviewing to be challenging or time-consuming? Join us at the Center for Social Science Innovation for a workshop on best practices for reviewing manuscripts in the social sciences!

This two-hour workshop introduces time series methods and shows their utility for examining social science data. Topics that will be covered include stationarity, ARIMA models, ADL/ECM models, and a brief introduction to more advanced time series models. Sara Mitchell, professor in the Department of Political Science, will run the workshop. Register for the workshop here. 

Survival analysis is a statistical method used to analyze and predict the time until an event of interest, such as death or failure, occurs. This workshop introduces participants to the fundamental concepts and techniques used to analyze time-to-event data.  Jon Davis, assistant professor in the Department of Occupational and Environmental Health, will run the workshop. Register for the workshop here.

This workshop will discuss the basics of multilevel modeling. Participants will learn how nesting or clustering in data creates dependency not accounted for in general linear models. Multilevel models allow for additional research questions and account for the dependency due to nesting. This workshop will focus on the conceptual background of multilevel models, but resources will also be provided and discussed on the implementation of these models.  This will workshop will be held synchronously...

This workshop will introduce different kinds of graphics in R. Various data visualization examples will be demonstrated during the workshop. By the end of this workshop, participants will be able to create and edit presentation or publication quality graphics (e.g., bar graphs, box plots, scatterplots) using R code. This is an introductory workshop appropriate for those with only basic familiarity with R. Participants will be asked to install R, RStudio, and required packages prior to the...

This beginner-friendly workshop introduces participants to Python programming and machine learning using Jupyter Notebook/Lab. You'll learn Python basics such as syntax, variables, data types, and essential data structures like lists and dictionaries. The session covers writing control flow statements, loops, and functions to make your code efficient and reusable. You'll discover how to import, clean, and manipulate data using powerful libraries like NumPy and Pandas, and create insightful...

This workshop is designed for those who are familiar with regression analysis, providing the basics of spatial regression using the spdep package in R. This workshop highlights why spatial models are necessary and provides step-by-step guidelines on how to analyze geospatial data. Participants will learn to measure spatial dependency in geographic data and perform spatial regression in R. Some familiarity with spatial data is recommended but not necessary. Datasets and slides will be provided. ...

Efforts to understand causality are important for evidence-based decision-making in fields like medicine, public health, social science, and policy. Still, challenges persist in comprehending key relationships and enhancing methods to analyze them. A deeper understanding of causal pathways can be essential to identify the primary determinants of health outcomes and to design effective interventions. Evaluating potential causal relationships within a rigorous framework can be accomplished by...