Carolina

Background:

• PhD from NYU, Postdoc here!
• Comparativist & Quantitative Methods

Interests:

• Crime, policing, violence, human rights, Latin America
• Quantitative (experimental and non experimental) methods

Jeremy

Background:

• PhD from UPenn PSCI, Postdoc at Duke
• Very applied work

Interests:

• Democracy and civil society, foreign aid and NGOs
• Randomized experiments, machine learning
• Field Projects: Uganda, Ethiopia, Cambodia, Serbia

You

Please tell us:

• Name, Year, Major
• One thing you’re interested in
• One thing you’d like to get from the course

Course Description

Course Description

• Follow-up to PSCI 1102
  • 1102 covered big academic debates that we won’t (ex. institutions vs geography)
• Focus on applied research with development agencies/industry
  • What is the state of the art?
  • “Big ideas” of political science only as context
  • MUST have a good understanding of the basics of RM

Course Description

• Substantive focus areas
  • Democracy and Autocracy
  • Migration
  • Gender
  • Poverty and Inequality
  • Crime and Conflict
  • Foreign Aid
  • Climate change and adaptation

Course Description

• Methods:
  • Deepen understanding of ‘workhorse’ statistical methods and research designs
  • How these methods can be used to make inferences about population characteristics and causal relationships
• Tools
  • Introduce the computational tools that are needed to implement these methods
  • Software necessary to prepare professional documents and reproducible data analysis workflows

Course Objectives

At the end of the course you should be able to:

• Have a good overview of the field and be capable of evaluating the quality of evidence
• Think clearly about how data can be used to learn about development and governance challenges
• Use tools for data analysis such as R, RStudio, Quarto, and GitHub
• Produce professional-quality documents that summarize original research

Prerequisites

• Substance
  • Big academic debates in development research (PSCI 1102)
• Methods and Tools (PSCI 1800)
  • Basic familiarity with R and RStudio
  • Basic knowledge of statistics/econometrics/data science

Textbook

• Data Analysis for Social Science: A Friendly and Practical Introduction (DSS)
  • We will use DSS as a jumping-off point for this course
  • Some of this material will be review

Grading

Performance in this class will be evaluated by according to performance on the following course requirements:

Quizzes

• On 4 randomly selected meetings, there will be a brief quiz
• If you paid any attention or did readings, you should get full credit
• One pre-approved absence allowed

Workshops

• 4 interactive, hands-on workshops working with diverse types of data, covering different statistical methods, or using new computational tools.
  • Tools: R and Rstudio, Quarto, github
  • Data: Survey data, text data, financial data
  • Methods: Randomized and quasi-experiments, text analysis.
• You will be required to submit a product demonstrating completion of the workshop

Data Assignments

• 6 data assignments designed to make sure you are keeping up with the tools covered in class and making progress in your final project.
• For assignments where you are required to submit something, you will be required to submit your own code and write-up.
• You can see the date of each assingment on the schedule

Final Project

• Data analysis project with data of your choosing
  • Formulate a research question
  • Find data that can help you answer that question
  • Apply the tools and methods from this course
  • Write-up analysis
• Produce a webpage to present your results for public consumption

Final Project

Late Submissions and Regrading

• Late submission of assignments
  • penalty of 2 points for every day late
  • except in documented cases of serious illness or family emergency
• Regrade request
  • detailed write-up of your dispute
  • Regrade of the entire assignment (might increase or decrease)

Use of AI Tools

• You are welcome to use generative AI tools (if you must) but beware!
• Do not let it come at the peril of your understanding of the material
• AI tools frequently make errors and ‘hallucinate’ (journal articles, R functions, etc.)
• It is your responsibility to verify the information provided
• You must disclose your use of AI tools for assignments in the form of footnotes or citations

Electronic Devices

• Laptops will be required in class
• All other electronic devices should be silenced and hidden

Controversial Topics and Statements

• Diverse perspectives, experiences, and backgrounds are essential for effective development research and practice
• Contact me directly if you feel we’re not achieving an inclusive environment
• Students are required to treat one another with respect
• Engage with any evidence that challenges your prior beliefs

Academic Honesty

• Students are expected to follow the University of Pennsylvania’s Code of Academic Integrity
• Suspected violations will be referred to university administration for disciplinary action.