Status: updating for 23F or 23S
Bailey, Michael A. 2021. Real stats : using econometrics for political science and public policy. 2nd edition. Oxford University Press. 1 | 2 | 3 | Instructor's resource
Kellstedt, P.M. and Whitten, G.D., 2018. The fundamentals of political science research. Cambridge University Press. library | here | Instructor's resource
Install R (ver. 4.2.1) here and RStudio Desktop (will be renamed as POSIT) here. Do not update your R during the semester.
Bergstrom, C.T. and West, J.D., 2021. Calling bullshit: the art of skepticism in a data-driven world. Random House Trade Paperbacks. website
KW, Ch 1
King, Gary. "Replication, replication." PS: Political Science & Politics 28, no. 3 (1995): 444-452. here
King, G., 2003. The future of replication. International Studies Perspectives. 4(1): 100–105. here
Janz, N., 2016. Bringing the gold standard into the classroom: replication in university teaching. International Studies Perspectives, 17(4), pp.392-407.
KW, Ch 6
Related readings
Tufte, E.R., 1969. Improving data analysis in political science. World Politics, 21(4), pp.641-654.
Cleveland WS (1993) Visualizing data. Hobart Press, Sebastopol, CA
Tufte ER (2001) The visual display of quantitative information, 2nd edn. Graphics Press, Cheshire, CT
Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading, PA
Kastellec JP, Leoni EL (2007) Using graphs instead of tables in political science. Perspectives of Politics 5(4):755–771
Rosling, H., Rosling, O. and Rönnlund, A.R., 2018. Factfulness: Ten Reasons We're Wrong About the World--and Why Things Are Better Than You Think. Flatiron Books. Gapminder | Hans Rosling's TED | Critique
Cairo, A., 2019. How charts lie: Getting smarter about visual information. WW Norton & Company.
Journal of Quantitative Description: Digital Media, JQD:DM
KW Ch 9
Related readings
Radean, Marius. The Significance of Differences Interval: Assessing the Statistical and Substantive Difference between Two Quantities of Interest. The Journal of Politics 85, no. 3 (July 2023): 969–83. https://doi.org/10.1086/723999.
Wasserstein, Ronald L., and Nicole A. Lazar. 2016. The ASA Statement on P-Values: Context, Process, and Purpose. The American Statistician.
McCaskey, Kelly and Carlisle Rainey. 2015. Substantive Importance and the Veil of Statistical Significance. Statistics, Politics, and Policy 6(1-2): 77-96.
King, G. and Roberts, M.E., 2015. How robust standard errors expose methodological problems they do not fix, and what to do about it. Political Analysis, 23(2), pp.159-179.
Rainey, Carlisle. 2014. Arguing for a Negligible Effect. American Journal of Political Science 58(4): 1083-1091.
KW Ch 10
Related readings
Achen, C.H., 2002. Toward a new political methodology: Microfoundations and ART. Annual review of political science, 5(1), pp.423-450. here
Bailey, Ch 6
Related readings
King, G., Tomz, M. and Wittenberg, J., 2000. Making the most of statistical analyses: Improving interpretation and presentation. American journal of political science, pp.347-361.
Bailey, Ch 6
Related readings
Hainmueller, J., Mummolo, J. and Xu, Y., 2019. How much should we trust estimates from multiplicative interaction models? Simple tools to improve empirical practice. Political Analysis, 27(2), pp.163-192.
William Berry, Matt Golder, & Daniel Milton. 2012. Improving Tests of Theories Positing Interaction.” Journal of Politics 74: 653-671.
Franzese, R.J. and Kam, C., 2009. Modeling and interpreting interactive hypotheses in regression analysis. University of Michigan Press.
Thomas Brambor, William Roberts Clark, & Matt Golder. 2006. Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14: 63-82.
William Roberts Clark, Michael Gilligan & Matt Golder. 2006. A Simple Multivariate Test for Asymmetric Hypotheses.” Political Analysis 14: 311-331.
Braumoeller, B.F., 2004. Hypothesis testing and multiplicative interaction terms. International organization, 58(4), pp.807-820.
Bailey Ch 7 slide | Data | code | natural log change as percentage change
Review
Main
Bailey Ch 12
Related readings
Hellevik, O., 2009. Linear versus logistic regression when the dependent variable is a dichotomy. Quality & Quantity, 43(1), pp.59-74.
Berry, W.D., DeMeritt, J.H. and Esarey, J., 2010. Testing for interaction in binary logit and probit models: is a product term essential?. American Journal of Political Science, 54(1), pp.248-266.
Rainey, Carlisle. 2016. Compression and Conditional Effects: A Product Term Is Essential When Using Logistic Regression to Test for Interaction. Political Science Research and Methods. 4(3): 621-639.
Review
Main
Bailey Ch 3.6
Exercise
Related readings
Green, D.P., Kim, S.Y. and Yoon, D.H., 2001. Dirty pool. International Organization, 55(2), pp.441-468.
Bell, A. and Jones, K., 2015. Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3(1), pp.133-153.
Clark, T.S. and Linzer, D.A., 2015. Should I use fixed or random effects?. Political science research and methods, 3(2), pp.399-408.
Mummolo, J. and Peterson, E., 2018. Improving the interpretation of fixed effects regression results. Political Science Research and Methods, 6(4), pp.829-835.
Kellstedt and Whitten Ch 12.3 | Bailey Ch 13
Review
Main
Bailey Ch 8
Related readings
Beck, N. and Katz, J.N., 1995. What to do (and not to do) with time-series cross-section data. American political science review, 89(3), pp.634-647. here
Beck, N., 2001. Time-series–cross-section data: What have we learned in the past few years?. Annual review of political science, 4(1), pp.271-293. here
Beck, N., & Katz, J. N. (2011). Modeling dynamics in time-series–cross-section political economy data. Annual Review of Political Science, 14, 331-352. here
Bailey Ch 1, 10
The Prize in Economic Sciences 2021 | 中文 | Ostrom | Simon | Johan Skytte Prize in Political Science
Related readings
Sekhon, Jasjeet and F. Daniel Hidalgo,. 2012. Causality. International Encyclopedia of Political Science. here
Pearl, J. and Mackenzie, D., 2018. The book of why: the new science of cause and effect. Basic books. 因果革命 : 人工智慧的大未來 因果革命 : 人工智慧的大未來 / 朱迪亞.珀爾(Judea Pearl), 達納.麥肯錫(Dana Mackenzie)合著 ; 甘錫安譯 珀爾 (Pearl, Judea) 新北市 : 行路出版 : 遠足文化發行, 民108 | website | library Chinese ebook
Angrist, J.D. and Pischke, J.S., 2008. Mostly harmless econometrics. Princeton university press. library ebook
Angrist, J.D. and Pischke, J.S., 2014. Mastering 'metrics: The path from cause to effect. Princeton University Press.
Murnane, R.J. and Willett, J.B., 2010. Methods matter: Improving causal inference in educational and social science research. Oxford University Press.
Bailey Ch 9
Neal, D., 1997. The effects of Catholic secondary schooling on educational achievement. Journal of Labor Economics, 15(1), pp.98-123. here
Sovey, A.J. and Green, D.P., 2011. Instrumental variables estimation in political science: A readers’ guide. American Journal of Political Science, 55(1), pp.188-200.
Moore, W. H., & Siegel, D. A. (2013). A Mathematics Course for Political and Social Research. Princeton University Press. Book Website | Youtube Lecture
Gill, J. (2006). Essential Mathematics for Political and Social Research. Cambridge University Press.
Quick-R: R in Action: Data Analysis and Graphics with R
R Cookbook: R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
Data Visualization with R by Rob Kabacoff
Introductory Statistics by Openstax College
Monogan, J.E., 2015. Political analysis using R. Springer. NSYSU available | Use R! NSYSU-available
R for Data Science by Hadley Wickham and Garrett Grolemund
