Place-based Research Approaches to Local Communities

This course will provide researchers with qualitative tools and perspectives to authentically conduct community-based participatory research. Participants will learn how to center community perspectives and marginalized voices. Community groups often lack resources and platforms to alleviate narratives about emerging and systemic social and public health challenges. Researchers are often challenged by lacking community partnerships to gather narratives and resources within these communities. Additionally, participants will learn how to establish trust, gain entry, and maintain relationships and reciprocity, adjust to data collection changes by being malleable and learning from failure, develop strategies to help community members’ narratives and experiences get to decision-making tables, and engage in strategies that reflect community-academic partnerships focused on institutional and community level change.

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Version Control with Git and GitHub

This 2-part (9/9/2025 and 9/11/2025) 5-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

In this course, participants will learn how to keep track of the code they use in their research using the version control system Git and the collaboration platform GitHub. Git allows you to keep track of changes to your code, easily revert to previous versions, and “tag” versions of code used in publications so that the exact code used can be retrieved at a later date. GitHub allows Git users to collaborate with each other on projects by managing simultaneous changes to the same files and allowing users to review and discuss each others’ code. Git and Github are applicable to any text-based programming or analysis language, including R, Python, Stata, Julia, and others.

PLEASE NOTE: Participants should create a github account at github.com and install git prior to the class. Windows users can download git at https://git-scm.com/download/win; there are multiple installation options, the first link is fine. Mac users can install git by opening the terminal application (in Applications -> Utilities -> Terminal) and typing “git version” (no quotes) and pressing enter. If git is not installed, you will be prompted to install it.

Register

Version Control with Git and GitHub

This 2-part (9/9/2025 and 9/11/2025) 5-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

In this course, participants will learn how to keep track of the code they use in their research using the version control system Git and the collaboration platform GitHub. Git allows you to keep track of changes to your code, easily revert to previous versions, and “tag” versions of code used in publications so that the exact code used can be retrieved at a later date. GitHub allows Git users to collaborate with each other on projects by managing simultaneous changes to the same files and allowing users to review and discuss each others’ code. Git and Github are applicable to any text-based programming or analysis language, including R, Python, Stata, Julia, and others.

PLEASE NOTE: Participants should create a github account at github.com and install git prior to the class. Windows users can download git at https://git-scm.com/download/win; there are multiple installation options, the first link is fine. Mac users can install git by opening the terminal application (in Applications -> Utilities -> Terminal) and typing “git version” (no quotes) and pressing enter. If git is not installed, you will be prompted to install it.

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Foundations of Applied Mixed Methods Research Design

This workshop provides participants with practical instruction on the topics of integrating qualitative and quantitative data, and the essential principles behind designing fundable mixed methods research. Specific topics include crafting mixed methods objectives and research questions, choosing appropriate sampling strategies, and selecting the most effective data collection methods (qualitative and quantitative). A significant amount of time is spent demonstrating the nuts and bolts of integrating qualitative and quantitative datasets, including diagramming points of interface. Although this workshop is primarily embedded in an applied context, the instructor will present some of the more common typologies and nomenclature used in the field. Course structure includes lecture, interactive discussion, and hands-on exercises.

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Introduction to Multilevel Modeling

This course will take place over two days (9/24/2025 and 9/26/2025) and will be offered via Zoom. Attendance is required as the course will not be recorded.

Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist). It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so: Tests of significance are likely biased and within- and between-group effects are confounded with one another. All of these limitations can be addressed within the multilevel model. In this workshop, we provide an introduction to the application of multilevel models with nested data, including software implementation in SAS, SPSS and Stata.

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Introduction to Multilevel Modeling

This course will take place over two days (9/24/2025 and 9/26/2025) and will be offered via Zoom. Attendance is required as the course will not be recorded.

Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist). It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so: Tests of significance are likely biased and within- and between-group effects are confounded with one another. All of these limitations can be addressed within the multilevel model. In this workshop, we provide an introduction to the application of multilevel models with nested data, including software implementation in SAS, SPSS and Stata.

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Discrete Choice Modeling

This course introduces participants to discrete choice models. These econometric models are used to explain how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models, namely the multinomial logit and nested logit. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge of discrete choice modeling is expected. Hands-on exercises will be conducted in Python.

Random utility models are used across many disciplines. They allow one to use regression techniques to model choices between multiple outcomes, something not possible with many other models. Unlike many other models of discrete outcomes, random utility models are interpretable—it is easy to see which predictor variables are associated with which choices. Random utility models are also consistent with rational economic theory, meaning that properly specified estimates can be interpreted as willingness-to-pay and transformed into dollar amounts to understand the welfare impacts of policy. This course will prepare participants both to estimate these models and to interpret and evaluate them when encountered in practice.

Participants should be familiar with linear regression. Some understanding of binary logistic regression, as well as experience using Python, is recommended not required.

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