Global Health 811: Applied Research Methods
Boston University, School of Public Health, Fall 2018
Instructor: Andrew Stokes
TA: Josh Smith-Sreen
Overview
In this course we focus on building skills in data, tools and methods for carrying out global health research. The class will introduce you to all stages of the research pipeline, from asking interesting questions to conducting a literature review, data collection, analysis, crafting an effective research paper and communicating results.
Course Objectives
After taking this class, you will be able to:
- Implement a mixed methods study from start to finish
- Design and implement a questionnaire
- Conduct a literature review and integrate bibliographic software
- Use R for applied data analysis
- Conduct qualitative data analysis
- Visualize data
- Apply, interpret and communicate results from regression analyses
- Write a clear and effective research paper
- Effectively communicate results with diverse audiences
Assignments
The key assignments in this class are:
- Preparing for class: Most classes will have required readings. These will come in different flavors, including articles from the scientific literature, popular pieces from the media, videos and software documentation.
- Problem sets: You will use data from the Demographic and Health Survey Program to complete three problem sets during the semester. These problem sets will be focused on topics in global health and will be implemented in R.
- Weekly peer reviews: You will be providing feedback on the work of your peers towards their final papers.
- Applied research methods journal: You will submit three journal entries during the course of the semester covering your research team’s process, decision-making, progress towards goals and putting course lessons in real-world context.
- Final project: Students will write a research paper. This is the major assignment in this class and you will be working on it throughout the semester. More information is available on the project page.
R Learning Modules
- R Learning Module 1
- R Learning Module 2
- R Learning Module 3 (Example Data)
- R Learning Module 4
- R Learning Module 5: Introduction to Dplyr
- R Learning Module 6
- R Learning Module 7
Week 1: Introduction
January 23, 2020
GH811 Course Syllabus Spring 2020
Spring 2020 Group Assignments & Spring 2020 Peer Review Schedule
- Susan Seligson. What big data won’t tell you. Bostonia. Fall 2014.
- R Learning Module 1
- R Learning Module 2
Further Reading:
- Benedict Carey. Many psychology findings not as strong as claimed, study says. The New York Times. Aug. 27, 2015.
- Benedict Carey. Science, now under scrutiny itself. The New York Times. Jun. 15, 2015.
- Loading, merging and analyzing DHS using R. Int J Public Health. 2014.
Multimedia:
- Scientific Studies: Last Week Tonight with John Oliver
- Planet Money: How much should we trust economics?
TA Lab Session (Thursday, January 23 from 5-6 pm):
- No lab session today
Project Components (due Sunday, January 26 at 5 pm):
Problem Set 1: Analysis of Ghana DHS 2014 (due Thursday, February 13 at 6 pm):
Week 2: Research Question & Literature Review
January 30, 2020
Before class:
- Review advice from previous students:
- Review R Learning Module 1 & R Learning Module 2 to prepare for the in-class activity
PubMed Exercises (Available for download here)
Before class:
- Mobarak et al. 2012. Low demand for nontraditional cookstove technologies. PNAS. (Abstract, Introduction & Background Sections).
- CM Varkevisser. Vol. 1. Modules 4 (Analysis of the Problem), 6 (Formulation of Objectives), 8 (Variables)
- Download Mendeley (online account & desktop version); video tutorials
Further Reading:
Literature review; academic writing
- Literature review: a few tips on conducting it. From the Health Sciences Writing Center at UT
- Field Trials of Health Interventions. Chapter 3, pp. 19-32
- Howard Becker. Writing for Social Scientists. 2nd Edition. Chapter 8. pp. 135-149.
Conflicts of interest
- Anahad O’Connor. How the sugar industry shifted blame to fat. New York Times. Sep. 2016.
- Julia Belluz. The obesity paradox: Why coke is promoting a theory that being fat won’t hurt your health. Vox. Oct. 2015.
- Anahad O’Connor. Coca-cola funds scientists who shift blame for obesity away from bad diets. New York Times. Aug. 2015.
- Eric Lipton. Rival industries sweet-talk the public. New York Times. Feb. 2014.
Multimedia
- Planet Money: The Experiment Experiment (Episode 677).
- NPR. Coca-Cola Funds Scientists Who Downplay Diet’s Role In Obesity. Aug. 10, 2015
TA Lab Session (Thursday, January 30 from 5-6 pm):
- General R syntax and vocabulary (R Lecture Slides)
- Review R Module 1
Project Components (due Sunday, February 2 at 5 pm):
- Final research topic
Peer Review (due by Thursday, February 6 at 6 pm):
- Final research topic
Reminder: Problem Set 1 due by 6pm on Thursday, 2/13!
Reminder: Journal 1 due by 6pm on Thursday, 2/20!
Week 3: Designing Questionnaires
February 6, 2020
Before class:
- Field Trials of Health Interventions. Chapter 14, pp. 224-248.
- Kathryn H. Jacobsen. Introduction to health research methods: A practical guide. Chapter 18. pp. 125-137.
- CM Varkevisser. Vol. 1.
- Module 10A (Overview of data collection techniques)
- Module 10B (Design of research instruments and interview guides).
In-Class Activity 1:
Further Reading:
- Jon Krosnick and Stanley Presser. Question and questionnaire design. Chapter 9 (pp. 263-313) in Handbook of Survey Research.
- KoBoToolbox Quick start guide
Optional Resources:
- KoBoToolbox support
TA Lab Session (Thursday, February 6 from 5-6 pm):
Project Components (due Sunday, February 9 at 5 pm):
- Annotated Bibliography
- Problem Diagram. Example
- Research Objectives. Examples
- Variables Worksheet
Peer Review (due by Thursday, February 13 at 6 pm):
- Research objectives
Reminder: Problem Set 1 due by 6pm on Thursday, 2/13!
Reminder: Journal 1 due by 6pm on Thursday, 2/20!
Week 4: Introduction to Qualitative Research
February 13, 2020
Reminder: Problem Set 1 due today!
Before class:
- The Trials of Alice Goffman. New York Times Magazine. Jan. 12, 2016.
- Field Trials of Health Interventions. Chapter 15. Social and Behavioral Research. pp. 249-267.
Further Reading:
- R. Power. 2002. The application of qualitative research methods to the study of sexually transmitted infections. Sex Transm Inf.
- T. Greenhalgh and R. Taylor. 1997. How to read a paper: Papers that go beyond numbers. BMJ.
Multimedia:
- Alice Goffman. How we’re priming some kids for college – and others for prison. TED Talk. March, 2015.
TA Lab Session (Thursday, February 13 from 5-6 pm):
Project Components (due Sunday, February 16 at 5 pm):
- Questionnaire (first draft)
Peer Review (due Thursday, February 20 at 6 pm):
- Questionnaire
- Problem Set 2
- Data
- Codebook
- Final Report
Reminder: Journal 1 due by 6pm on Thursday, 2/20!
Week 5: Sampling, Sample Size & Planning Data Collection
February 20, 2020
- Lecture slidesBefore class:
- Field Trials of Health Interventions. Chapter 5. pp. 71-97.
- Sample size for a single mean or proportion
- Smith Ch. 5 Tables & Figures
Further Reading:
- CM Varkevisser. Vol. 1.
- Module 11: Sampling
- Module 12: Plan for data collection
TA Lab Session (Thursday, February 20 from 5-6 pm):
Project Components (due Sunday, February 23 at 5 pm):
- Interview Guide (Example)
- Introduction Section
Peer Review (due Thursday, February 27 at 2 pm):
- Interview guide
Reminder: Problem Set 2 due by 6pm on Thursday, 3/19!
Reminder: Journal 2 due by 6pm on Thursday, 3/26!
Week 6: Preparing for Data Analysis
February 27, 2020
Before class:
- Field Trials of Health Interventions. Chapters 20 (data management), 21 (methods of analysis). pp. 338-385.
- Lohr 2014. For Big-Data Scientists, ‘Janitor Work’ is Key Hurdle to Insights
- R Learning Module 7
Further Reading:
- Data Wrangling with dplyr and tidyr. R Studio Cheat Sheet
- Hadley Wickham’s presentation at UseR 2014
- Wickham, H. Tidy Data. Journal of Statistical Software. 2015.
- Introduction to dplyr with accompanying video tutorial by Kevin Markham
- Peng. 2015. Managing Data Frames with the dplyr package. In Exploratory Data Analysis with R.
- Tippmann 2014. Programming tools: Adventures with R. Nature
TA Lab Session (Thursday, February 27, from 5-6 pm):
Project Components (due Sunday, March 1 at 5 pm):
- Sampling plan
- Power calculations
- Data collection plan
Peer Review (due Thursday, March 5 at 2 pm):
- Data collection plan
Reminder: Problem Set 2 due by 6pm on Thursday, 3/19!
Reminder: Journal 2 due by 6pm on Thursday, 3/26!
Week 7: Qualitative Analysis
March 5, 2020
Guest lecture on qualitative analysis by Houda Chergui, Senior Research Assistant at the Boston Center for Refugee Health and Human Rights (BCRHHR)
Class Materials:
Further Reading:
- Malterud et al. 2016. Sample size in qualitative interview studies: guided by information power. Qualitative Health Research.
In Class Activity 3:
TA Lab Session (Thursday, March 5 from 5-6 pm):
- Review R Learning Module 5: Introduction to Dplyr
Project Components (due Sunday, March 8 at 5 pm):
- Questionnaire (final)
- Interview guide (final)
Peer Review:
- No peer review this week
Reminder: Problem Set 2 due by 6pm on Thursday, 3/19!
Reminder: Journal 2 due by 6pm on Thursday, 3/26!
No class this week – enjoy your spring break!
Reminder: Problem Set 2 due by 6pm on Thursday, 3/19!
Reminder: Journal 2 due by 6pm on Thursday, 3/26!
Week 8: NVivo Workshop (Guest Lecture: Dr. Paul Ashigbie)
March 19, 2020
Reminder: Problem Set 2 due today!
- NVivo Workshop:NVivo10-for-Mac-Getting-Started-Guide NVivo11-Getting-Started-Guide-Pro-edition FOR WINDOWS
Optional Resources:
- NVivo 9 (note: you are not required to purchase NVivo for this class; access to the software is available in school computer labs; you may also download a trial version from the website, however, note the trial version expires after 14 days).
- NVivo Tutorial
TA Lab Session (Thursday, March 19 from 5-6 pm):
- Review Activity 3
Project Components (due Sunday, March 22 at 5 pm):
- Data dictionary (Example)
- Table shells
- Methods section (Examples: Turan et al.; Stokes & Preston)
- Team Charter Review
Peer Review (due Thursday, March 26 at 2 pm):
- Table shells
Problem Set 3 (due Thursday, April 9 by 6 pm):
Reminder: Journal 2 due by 6pm on Thursday, 3/26!
Week 9: Messy Data, Tidyr, Dplyr & Rmd;
March 26, 2020
Reminder: Journal 2 due today!
In Class Activity 4:
TA Lab Session (Thursday, March 26 from 5-6 pm):
- R Learning R Learning Module 6
- R Learning Module 7
Project Components (due Sunday, March 29 at 5 pm):
- Qualitative themes
Peer Review (due Thursday, April 2 at 2 pm):
- Qualitative themes
Reminder: Problem Set 3 due by 6pm Thursday, 4/9!
Week 10: Data Visualization
April 2, 2020
Lecture Slides (Visualization)
Before class:
- Review R Learning Module 4 (Example Data)
- Review R Learning Module 5: Introduction to Dplyr
- R Learning Module 7 (for help with PS3)
- Wickham. ggplot2: Elegant Graphics for Data Analysis. Introduction.
- Tufte. Visual and Statistical Thinking: Displays of Evidence for Making Decisions.
Further Reading:
- Healy and Moody. Data Visualization in Sociology. Annu Rev Sociol 2014.
- Gelman et al. 2002. Let’s Practice What We Preach: Turning Tables into Graphs. The American Statistician.
- Kastellec and Leoni (2007). Using Graphs Instead of Tables in Political Science. Perspectives on Politics.
- Rougier et al (2014). Ten Simple Rules for Better Figures. PLOS Computational Biology.
- David Robinson. Visualizing Data Using ggplot2 (videos)
- Peng. 2015. Principles of Analytic Graphics; Exploratory Graphs; The ggplot2 plotting system (parts 1 & 2). Exploratory Data Analysis with R.
- R Graph Catalog
- Introduction to ggplot2. Office of Population Research. Princeton.
Further Reading:
- Data Wrangling with dplyr and tidyr. R Studio Cheat Sheet
- Hadley Wickham’s presentation at UseR 2014
- Wickham, H. Tidy Data. Journal of Statistical Software. 2015.
- Introduction to dplyr with accompanying video tutorial by Kevin Markham
- Peng. 2015. Managing Data Frames with the dplyr package. In Exploratory Data Analysis with R.
- Tippmann 2014. Programming tools: Adventures with R. Nature
- R Markdown Cheat Sheet
TA Lab Session (Thursday, April 2 from 5-6 pm):
- Review Activity 4
Project Components (due Sunday, April 5 at 5pm):
- Descriptive tables
- Bivariate tables
Peer Review (due Thursday, April 9 at 2 pm):
- Descriptive tables
Reminder: Problem Set 3 due by 6pm Thursday, 4/9!
Week 11: Correlation & Linear Regression
April 9, 2020
Reminder: Problem Set 3 due today!
Lecture Slides: Statistical-Inference, Slope Transformation/Regression Diagnostics, & Sources of Data
ggplot Demo:
Code for ggplot demo (PDF file)
Code for ggplot demo (text file)
Before class:
- Caffo. 2015. Regression Models for Data Science in R. Leanpub. pp. 1-45.
- Statistical Inference Background Slides
TA Lab Session (Thursday, April 9 from 5-6 pm):
- Review Problem Set 3
Project Components (due Sunday, April 12 at 5 pm):
- Multivariable tables
- Results section
Peer Review (due Thursday, April 16 at 6pm):
- Multivariable tables
Week 12: Logit Modules for Dichotomous Outcomes
April 16, 2020
Demonstration: Logistic Regression in R (data link: Data)
Flash Presentation workshop
Before class
- Field Trials of Health Interventions. Chapter 21 (methods of analysis). pp. 338-385.
Further Reading:
- Caffo. 2015. Regression Models for Data Science in R. Leanpub. pp. 105-115.
- Women flocking to statistics, the newly hot high-tech field of data science. Washington Post. Dec. 19, 2014.
- Hadley Wickham, the man who revolutionized R. Priceonomics. July 24, 2015.
TA Lab Session (Thursday, April 16 from 5-6 pm):
- Coding Workshop
Project Components (due Sunday, April 19 at 5 pm):
- Complete draft
- Final flash presentation
Peer Review:
- No peer review this week
Reminder: Journal 3 due by 6pm on Thursday, 4/30!
Week 13: Project and Presentation Workshop
April 23, 2020
TA Lab Session (Thursday, April 23 from 5-6pm):
- Josh will be available for feedback on projects & presentation slides
Project Components (due Sunday, April 26 by 5 pm):
- Flash presentation final draft
- Final research report
Reminder: Journal 3 due by 6pm on Thursday, 3/30!
