IR 211: Approaches to Research
IR 211: Approaches to Research.
This class is an introduction to social science research methodology. My main goal is to teach you the basics of creating and consuming research in the social sciences, and international relations in particular. The course will lead you through conceptualization and theory construction, the derivation of testable hypotheses, and a variety of methodologies that may be used to evaluate these hypotheses. We will discuss causal inference, observation and measurement, and other issues encompassing both qualitative and quantitative research methods. We will discuss the way in which academic articles in the social sciences are written, and how they should be read.
This course includes some very basics statistics, and requires use of Stata (a statistical software package) for some class assignments. These include some simple description and cross tabulation of data from the General Social Survey. Computers with Stata are available in an on-campus computer lab.
The syllabus for the course is here (Updated 10/5).
Lecture Slides and Requred Material
Lecture 2-1: Scientific Method
Lecture 2-2: Scientific Method Solves Wicked Problems
Reading: Slantchev. Scientific Method
Exercise: New York Times Puzzle
Listening: "Is there a better way to fight terrorism?"
Reading: "Are all terrorists Muslim? It's not even close"
Viewing: "Definition of Terrorism"
Reading (optional): Many Psychology Findings Not as Strong as Claimed, Study Says
Lecture 3-1: Literature Reviews and Research Questions
Lecture 3-2: Literature Reviews and Research Questions
Reading: Jeffrey Knopf. 2006. “Doing a Literature Review.” PS: Political Science 39(1): 127-132
Section Notes: Research Questions
Lecture 4-1: Deriving Testable Hypotheses
Lecture 4-2: Hypothesis Testing
Lecture 4-3: Measurement
Reading: Sebastian Rosato, “The Flawed Logic of Democratic Peace Theory,” American Political Science Review 97:1 (November 2003), pp. 585-602.
Reading: Branislav L Anna Alexandrova, and Erik Gartzke, “Probabilistic Causality, Selection Bias, and the Logic of the Democratic Peace,” American Political Science Review 99:3 (August 2005), pp. 459-462.
Lecture 5-1: Measurement part 2
Lecture 5-2: Measurement part 3
Lecture 5-3: Measurement Application
Viewing: What is GDP?
Reading: What does Nigeria's new GDP number actually mean?
Reading: Adcock and Collier. Measurement Validity: A shared standard for qualitative and quantitative research
Section Notes: Measurement
Lecture 6-1: Sampling
Lecture 6-2: Sampling 2
Reading: Examples of Sampling Techniques
Viewing: Sampling Error
Exercise: Random Sample Warmup
Exercise (Optional): Sampling Distribution
Section Notes: Sampling
Lecture 7-1: True Experiments
Lecture 7-2: True Experiments 2
Lecture 7-3: College to Career
Reading: McDermott, Rose. “Experimental Methods in Political Science.” Annual Review of Political Science. 5(2002), 31-61.
Reading: Miguel, Primary School Deworming in Kenya
Section Notes: True Experiments
Lecture 8-1: Natural Experiments
Lecture 8-2: Quasi-Experiments
Lecture 8-3: Regression Discontinuity
Reading: Impact of Women Policy-Makers
Reading: Survey Methods Chapter, Political Science Research Methods by Johnson and Reynolds
Reading (Optional): Raghabendra Chattopadhyay and Esther Duflo. "Women as Policy Makers: Evidence from a Randomized Policy Experiment in India." Econometrica, Vol. 72, No. 5 (Sep., 2004), pp. 1409-1443
Lecture 9-1: Surveys 1
Lecture 9-2: Surveys 2
Listening: Smith, "Rutgers Survey Underscores Challenges Collecting Sexual Assault Data"
Reading: Leech, “Asking Questions”
Reading: Woliver, “Ethical Dilemmas”
Reading: Goldstein, “Getting in the Door”
Reading: Focus Groups
Lecture 11-1: Ethics 1
Lecture 11-2: Ethics 2
Lecture 11-3: Ethics 3
Reading: "Indian Tribe Wins Fight to Limit Research of Its DNA"
Reading: Schutt, Ch. 3
Reading: Ioannidis. "Why most published research findings are false."
Lecture 12-1: Descriptive Statistics
Lecture 12-2: Descriptive Statistics 2
Lecture 12-3: Descriptive Statistics 3
Reading: Agresti and Finlay, Descriptive_Statistics
Viewing: The Central Limit Theorem
Section Notes: Descriptive Stats in Excel
- toy data, video 1, video 2, video 3, toy data with plots
Lecture 13-1: Inferential Statistics 1
Lecture 13-2: Inferential Statistics 2
Section Notes: Regression Line and t-test in Excel
- toy data, video 1, video 2, toy data with scatter and fitted line
- useful link 1, useful link 2
Reading: Jensen, "Democratic Governance and Multinational Corporations"
Reading: How Elite Students Get Elite Jobs
Reading: Why a Harvard Professor Has Mixed Feelings When Top Students Take Jobs in Finance.
Homework 1: Wicked Problems, Due Sept 2
Homework 2: Lit Review, Due Sept 18
Homework 3: Measurement Validity, Due Oct 2
Homework 4: Experimental Design, Due Oct 16
Homework 5: Descriptive Statistics, Due Nov 16, ir211data.xlsx, WDI_mini.xls
Homework 6: Inferential Statistics, Due Nov 23, ir211data.xlsx, WDI_mini.xls
Intention to treat vs. actual treatment. Not everyone in the treatment group always takes the drug (or adheres to the treatment, whatever that might be).
When you read this, think about how the failure to include Spanish tweets affects the sample. How might that omission bias the results?
Data of many types is rapidly increasing in abundance and quality, but a lack of good data is still crippling in many areas of social science, including the study of economic development. For example, we really don't know how economies in Africa are doing.
Vreeland's Goldilocks and the Three Regimes.
Here is more detail on Progresa if you're interested.
Regarding the risks of rigorous outside evaluation, here is the Heritage Foundation using an outside evaluation of the Headstart program to club the program over the head and argue for cutting funding. Here is the outside evaluation itself.
File this under "observation/description/measurement = hard." Even when its something we take for granted, like GDP figures.
Related to Homework 1, here is an opportunity to volunteer counting homeless in LA.