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No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science

Published online by Cambridge University Press:  31 December 2014

Burt L. Monroe ,
Jennifer Pan ,
Margaret E. Roberts ,
Maya Sen  and
Betsy Sinclair
Burt L. Monroe
Affiliation:
Pennsylvania State University
Jennifer Pan
Affiliation:
Harvard University
Margaret E. Roberts
Affiliation:
University of California, San Diego
Maya Sen
Affiliation:
Harvard University
Betsy Sinclair
Affiliation:
Washington University in St. Louis
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Abstract

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Type
Symposium: Big Data, Causal Inference, and Formal Theory: Contradictory Trends in Political Science?
Information
PS: Political Science & Politics , Volume 48 , Issue 1 , January 2015 , pp. 71 - 74
Copyright
Copyright © American Political Science Association 2015 

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References

REFERENCES

Aldhous, Peter. 2012. “Specialist Knowledge Is Useless and Unhelpful.” New Scientist, December 7. Available athttp://www.newscientist.com/article/mg21628930.400-specialist-knowledge-is-useless-and-unhelpful.html.CrossRefGoogle Scholar
Ansolabehere, Stephen, Hersh, Eitan, and Shepsle, Kenneth. 2012. “Movers, Stayers, and Registration: Why Age Is Correlated with Registration in the U.S.” Quarterly Journal of Political Science 7 (4): 333–63.Google Scholar
Bond, Robert M., Fariss, Christopher J., Jones, Jason J., Kramer, Adam D. I., Marlow, Cameron, Settle, Jaime E., and Fowler, James H.. 2012. “A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489: 295–8.Google Scholar
boyd, danah, and Crawford, Kate. 2012. “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon.” Information, Communication & Society 15 (5): 662–79.CrossRefGoogle Scholar
Brandt, Patrick T., Freeman, John R., and Schrodt, Philip A.. 2011. “Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict.” Conflict Management and Peace Science 28 (1): 48–64.Google Scholar
DiGrazia, Joseph, McKelvey, Karissa, Bollen, Johan, and Rojas, Fabio. 2013. “More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior.” PLoS ONE 8 (11): e79449.Google Scholar
Dougherty, Keith L., Lynch, Michael S., and Madonna, Anthony J.. 2014. “Partisan Agenda Control and the Dimensionality of Congress.” American Politics Research 42 (4): 600–27.Google Scholar
Granato, Jim, and Scioli, Frank. 2004. “Puzzles, Proverbs, and Omega Matrices: The Scientific and Social Significance of Empirical Implications of Theoretical Models (EITM).” Perspectives on Politics 2 (2): 313–23.Google Scholar
Hersh, Eitan D. 2013. “Long-Term Effect of September 11 on the Political Behavior of Victims’ Families and Neighbors.” Proceedings of the National Academy of Sciences 110 (52): 20959–63.Google Scholar
King, Gary, Lucas, Christopher, and Nielsen, Richard. 2014. “The Balance-Sample Size Frontier in Matching Methods for Causal Inference.” Available athttp://gking.harvard.edu/files/gking/files/frontier_2.pdfGoogle Scholar
King, Gary, Pan, Jennifer, and Roberts, Margaret E.. 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review 107 (2): 326–43.Google Scholar
King, Gary, Pan, Jennifer, and Roberts, Margaret E.. 2014. “Reverse Engineering Chinese Censorship: Randomized Experimentation and Participant Observation.” Science 345 (6199): 1–10.Google Scholar
Lazer, David M., Kennedy, Ryan, King, Gary, and Vespignani, Alessandro. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176): 1203–5.Google Scholar
Lazer, David, Pentland, Alex (Sandy), Adamic, Lada, Aral, Sinan, Barabási, Albert-László, Brewer, Devon, et al. 2009. “Life in the Network: The Coming Age of Computational Social Science.” Science 323 (5915): 721–3.Google Scholar
Linzer, Drew. 2013. “Dynamic Bayesian Forecasting of Presidential Elections in the States.” Journal of the American Statistical Association 108 (501): 124–34.Google Scholar
Monroe, Burt L. 2013. “The Five Vs of Big Data Political Science: Introduction to the Virtual Issue on Big Data in Political Science.” Political Analysis, Virtual Issue 5.Google Scholar
Monroe, Burt L., Colaresi, Michael P., and Quinn, Kevin M.. 2008. “Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict.” Political Analysis 16 (4): 372–403.Google Scholar
Nagler, Jonathan, and Tucker, Joshua. 2015. “Drawing Inferences and Testing Theories with Big Data.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Nickerson, David. 2008. “Is Voting Contagious? Evidence from Two Field Experiments.” American Political Science Review 102 (1): 49–57.Google Scholar
Osorio, Javier. 2013. “Democratization and Drug Violence in Mexico.” Paper presented to the American Political Science Association, Chicago, August 31.Google Scholar
Patty, John W., and Penn, Elizabeth Maggie. 2015 “Analyzing Big Data: Social Choice, & Measurement.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1991. “Patterns of Congressional Voting.” American Journal of Political Science 35 (1): 228–78.Google Scholar
Schrodt, Philip A. 2014. “Seven Deadly Sins of Contemporary Quantitative Political Analysis.” Journal of Peace Research 51 (2): 287–300.CrossRefGoogle Scholar
Sinclair, Betsy. 2012. The Social Citizen. Chicago: University of Chicago Press.Google Scholar
Stephens-Davidowitz, Seth I. 2014a. “The Cost of Racial Animus on a Black Presidential Candidate: Evidence Using Google Search Data.” Journal of Public Economics. 118: 26–40.Google Scholar
Stephens-Davidowitz, Seth I. 2014b. “Estimating the Closeted Gay Male Population.” Presentation to the Centers for Disease Control and Prevention, STD Prevention Conference, Atlanta, June 10.Google Scholar
Tzelgov, Eitan. 2012. “Damned If You Do and Damned If You Don’t: Rhetorical Heresthetic in the Israeli Knesset.” Party Politics. Available at doi:10.1177/1354068812462926.Google Scholar
Varian, Hal R. 2014. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives 28 (2): 3–27.Google Scholar