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...

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. ...

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 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 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.  Erica Dorman, CSSI graduate consultant and...