Contributors: Katherine Lasonde ('23), Kyra McLaughlin ('23), Osman Khan (Data Visualization Fellow, DALI Lab), Emily Walton (Professor of Sociology), Scott Pauls (DIFUSE PI, Professor of Mathematics), Taylor Hickey ('23, Project Manager)
This module was developed through the DIFUSE project at Dartmouth College and funded by the National Science Foundation award IUSE-1917002.
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 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
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Give students exposure to data science through the analysis and visualization of data, and interpreting and communicating results in the context of sociological questions about the relationship between statistical data from Texas counties and self-reported health measures.
- Apply data visualization methods and interpret the results.
- Generate and test hypotheses about relationships between different variables and health outcomes.
- Build and use models to predict outcomes.
Students complete four modules that each analyze how different factors might affect health outcomes. The module uses the state of Texas as a case study that will help students come to broader conclusions about the geography of health disparities in the United States. The first module focuses on social and economic factors, the second on health behaviors, the third on physical environment, and the fourth on access to health care. Students visualize how each of these factors relates to self-rated health, through an analysis of an interactive map that overlays these data. Students generate and test hypotheses about relationships between aspects of the data and the module tools include visualizations to identify correlation and a linear regression tool, allowing a deeper statistical analysis.
The module uses statistical data from Texas counties including measures of high school graduation, median household income, smoking, physical inactivity, food insecurity, air pollution, severe housing problems, unisured, mental health providers, and self-reported health.
This module uses Python notebooks through google CoLab.
Use this page to get an idea of the timeline of the module, what components are involved, and what documents are related to each component. This is the schedule intended for module deployment by the DIFUSE team, though instructors are welcome to modify the timeline to fit their course environment.
| Date | In/Out of Class | Assignment Description | Linked course content | Assignment Files (Linked to Repository Contents) |
|---|---|---|---|---|
| Week 1 | In class | Introduction to modules, variables, techniques, and colab | Introductory materials | |
| Week 2 | Out of class | Module #1: Socioeconomic impact on health outcomes | "Socioeconomic Disparities in Health Behaviors” by Pampel et al. (2010). | Module 1 |
| Week 4 | Out of class | Module #2: Smoking, physical inactivity, and food insecurity. | "Flint’s Children Suffer in Class After Years of Drinking the Lead-Poisoned Water" | Module 2 |
| Week 6 | Out of class | Module #3: Impact of the physical environment: air pollution and severe housing problems. | "Neighborhood effects on primary care access in Los Angeles" (Prentice, 2005). | Module 3 |
| Week 8 | Out of class | Module #4: Impact of access to clinical care | Module 4 |
This course was developed for a sociology course, Health Disparities, at Dartmouth College which explores the interrelations between health outcomes and key sociological concepts such as race, wealth, gender, and other social determinants of health. The course is a lower major level course in the Sociology Department. While there are no prerequisites, most students have completed one or more introductory sociology courses.
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
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For instructors and interested parties, the history of this repository (with detailed commits), can be found here.