Métodos Quantitativos de Pesquisa na Ciência Política IV

FLP0468 - Métodos Quantitativos de Pesquisa na Ciência Política IV

2º semestre / 2021

*This course will be taught in English*

Tuesday and Friday, 14:00-18:00 Wednesday and Friday, 19:00-21:00

Lorena G. Barberia

This course is the second course in a three-course sequence, following Quantitative Methods I/FLP0406/FLS5028 (Introductory Statistics). This course is intended to provide students with the tools necessary to conduct empirical research and to critically read the empirical research of others in the fields of political science, public policy and social sciences more generally. A significant share of the course is dedicated on the effective interpretation and presentation of statistical results, and on understanding already-published analyses. The course assumes students have a background in algebra and basic statistical analysis and based on this background introduces students to modern econometrics. The course emphasizes innovative tools for learning quantitative methods and most classes are taught as a flipped classroom. Students will learn to use Stata for the purposes of statistical analysis, and will learn to work with observational and simulated data. For the final assignment, students will replicate a recent published study in a prominent journal in the field.

Teaching Fellows: Fabiana da Silva Pereira and Isabel Seelaender Costa Rosa Required Texts and Other Readings

Barberia, Lorena G. 2019. Desenho de Pesquisa em Política Comparada. Brasília: Coleção Metodologias de Pesquisa for Escola Nacional de Administração Pública (ENAP).

Gill, Jeff. Essential Mathematics for Political and Social Research. New York: Cambridge University Press. Greene, William H. 2017. Econometric Analysis, 8th edition. New York: Pearson.

Kellstedt, Paul M., and Guy D. Whitten. 2013. The Fundamentals of Political Science Research. 2nd ed. Cambridge; New York: Cambridge University Press. (Também disponível em português: Kellstedt, Paul M., and Guy D. Whitten. 2015. Fundamentos da Pesquisa em Ciência Política (Lorena Barberia, Gilmar Masiero and Patrick Cunha Silva, Translators). São Paulo, Brazil: Editora Blucher.

Neumayer, Eric and Thomas Plümper. 2017. Robustness Tests for Quantitative Research (Methodological Tools in the Social Sciences). Cambridge; New York: Cambridge University Press.

Stock, James and Mark Watson. 2018. Introduction to Econometrics, 4th edition. Boston: Addison Wesley. STATA Tutorials

Baum, Christopher F. 2006. An Introduction to Modern Econometrics Using Stata. Stata Press.

Cameron, Colin and Pravin Trivedi. 2010. Microeconometrics Using Stata. 2nd ed. Stata Press.

Rodríguez, Germán. 2011. Stata Tutorial. See: http://data.princeton.edu/stata/.

UCLA Academic Technology Services. Resources to help you learn and use Stata. See: http://www.ats.ucla.edu/stat/stata/default.htm.

Grades

1. Problem Sets (60%)
2. Final Replication Assignment (40%)

All readings will be posted in Perusall (https://perusall.com/). Please use access code: BARBERIA-EMHXU.

Topics

1. Introduction

Gary King “Flipping My Research Methods Course: Video Lectures, OERs & The Hybrid Classroom.” Research Design: Kellstedt and Whitten, Chapters 1-4

Stock and Watson, Chapters 2-3

Lab: Introduction to Stata

2. The Bivariate Regression Model

Stock and Watson, Chapters 4-5

Beck, Nathaniel. 2010. Making Regression and Related Output More Helpful to Users. The Political Methodologist

18(1): 4-9.

Lab: Hypothesis Testing Revisited (Difference between Means and the Bivariate Regression Model)

3. From the Bivariate Regression to the Multivariate Regression Model

Stock and Watson, Chapters 6-7

Gelman, Andrew, and Hal Stern. 2006. The Difference Between "Significant" and "Not Significant" is not Itself Statistically Significant. The American Statistician 60 (4): 328-331.

Lab: A First Exercise in Simulation

4. The Multivariate Regression Model in Matrix Notation

Gill, Jeff. Essential Mathematics for Political and Social Research. New York: Cambridge University Press. Ch. 3 and 4.

Stock and Watson, Chapter 19

Lab: Derivation of OLS using Matrix Algebra

5. Multicollinearity

Stock and Watson, Chapter 6. Lab: Multicollinearity Simulation

6. Omitted Variable Bias

Neumayer and Plümper, Chapter 9 Lab: Omitted Variable Bias

7. Multivariate Regression with Dummy Variables and Interactions

Stock and Watson, Chapter 8

Braumoeller, Bear. 2004. "Hypothesis Testing and Multiplicative Interaction Terms," International Organization

(Fall): 807-20.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. Understanding Interaction Models: Improving Empirical Analyses. Political Analysis 14 (1): 63-82.

Clark, William, Michael Gilligan & Matt Golder. 2006. A Simple Multivariate Test for Asymmetric Hypotheses.

Political Analysis 14: 311-331.

Kellstedt and Whitten. Capítulo 11, Multiple Regression Models II: Crucial Extensions, pp. 202-220. Lab: Revisting Duverger´s Theory with Interaction Models

8. Multivariate Regression with Non-Linear Interactions

Hainmueller, J., Mummolo, J., & Xu, Y. (2019). How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice. Political Analysis, 27(2), 163-192.

Lab: Nonlinear Interactions

9. Robustness Tests

Stock and Watson, Chapter 9