README.md

MACS 30000 - Perspectives on Computational Analysis

	Dr. Benjamin Soltoff	Ryan C. Hughes (TA)	Joshua G. Mausolf (TA)
Email	[email protected]	[email protected]	[email protected]
Office	249 Saieh Hall	251 Saieh Hall	251 Saieh Hall
Office Hours	Th 1-3pm	M 8:00-10:00am	F 9:30-11:30am
GitHub	bensoltoff	rchughes	jmausolf

• Meeting day/time: MW 11:30-1:20pm, 247 Saieh Hall for Economics
• Lab session: W 4:30-5:20pm, 247 Saieh Hall for Economics
• Office hours also available by appointment

Course description

Massive digital traces of human behavior and ubiquitous computation have both extended and altered classical social science inquiry. This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. We will reexamine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. Students will review fundamental research designs such as observational studies and experiments, statistical summaries, visualization of data, and how computational opportunities can enhance them. The focus of the course is on exploring the wide range of contemporary approaches to computational social science, with practical programming assignments to train with these approaches.

Required textbooks

All textbooks are available in electronic editions either directly from the author or via the UChicago library (authentication required). Hardcopies can be purchased at your preferred retailer.

• Salganik, Matthew J. Bit by Bit: Social Research in the Digital Age, Princeton University Press, Open review edition.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. New York: Springer.
• VanderPlas, Jake. (2016). Python Data Science Handbook. O'Reilly Media, Inc.

Evaluation

Assignment	Quantity	Points	Total Points
Short assignments	8	10	80
Final exam	1	20	20

• Short assignments will vary depending on subject matter. They could include writing assignments analyzing computational research designs and/or problem sets implementing specific computational methods.
• Final exam will be a timed take-home exam. Details to be furnished near the end of term.

Disability services

If you need any special accommodations, please provide me (Dr. Soltoff) with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

Course schedule (lite)

#	Date	Topic	Assignment due
1.	Mon, Sep. 25	Introduction to Computational Social Science
2.	Wed, Sep. 27	Science in a computational era
3.	Mon, Oct. 2	Observational data - counting things
4.	Wed, Oct. 4	Observational data - measurement
5.	Mon, Oct. 9	Observational data - forecasting
6.	Wed, Oct. 11	Observational data - approximating experiments
7.	Mon, Oct. 16	Asking questions - fundamentals	Proposing an observational study
8.	Wed, Oct. 18	Asking questions - digital enrichment
9.	Mon, Oct. 23	Experiments	Proposing a survey study
10.	Wed, Oct. 25	Experiments
11.	Mon, Oct. 30	Simulated data	Proposing an experiment
12.	Wed, Nov. 1	Simulated data
13.	Mon, Nov. 6	Collaboration	Simulating your income
14.	Wed, Nov. 8	Collaboration
15.	Mon, Nov. 13	Ethics	Collaboration
16.	Wed, Nov. 15	Ethics
17.	Mon, Nov. 20	Exploratory data analysis - univariate visualizations	The ethics of the Montana election experiment
18.	Wed, Nov. 22	Exploratory data analysis - multivariate visualizations
19.	Mon, Nov. 27	Exploratory data analysis - clustering	Exploring the General Social Survey
20.	Wed, Nov. 29	Exploratory data analysis - dimension reduction
21.	Mon, Dec. 4		Unsupervised learning

The final exam will be distributed on Tuesday December 5 at 12pm and must be submitted by 11:59pm Wednesday December 6.

Course schedule (readings)

All readings are required unless otherwise noted. Adjustments can be made throughout the quarter; be sure to check this repository frequently to make sure you know all the assigned readings.

1. Introduction to computational social science
   • Watts, D. J. (2007). A twenty-first century science. Nature, 445(7127), 489-489.
   • Lazer et. al. (2009) Computational Social Science. Science, 323, 721-723.
2. Social science in a computational era
   • Bhattacherjee, A. (2012). Social science research: principles, methods, and practices. Chapters 1-4. Skim/review as needed.
   • Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289-310.
   • Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired.
   • Schrodt, P. A. (2014). Seven deadly sins of contemporary quantitative political analysis. Journal of Peace Research, 51(2), 287-300.
3. Observational data (counting things)
   • "Chapter 2: Observing Behavior." Bit by Bit. Sections 2.1-2.4.1.3.
   • King, G., Pan, J., & Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective expression. American Political Science Review, 107(02), 326-343.
   • Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88-90.
4. Observational data (measurement)
   • Bonica, A. (2014). Mapping the ideological marketplace. American Journal of Political Science, 58(2), 367-386.
   • Wojcik, S. P., Hovasapian, A., Graham, J., Motyl, M., & Ditto, P. H. (2015). Conservatives report, but liberals display, greater happiness. Science, 347(6227), 1243-1246.
   • Emotional timeline of September 11, 2001
     • Back, M. D., Küfner, A. C., & Egloff, B. (2010). The emotional timeline of September 11, 2001. Psychological Science, 21(10), 1417-1419.
     • Pury, C. L. (2011). Automation can lead to confounds in text analysis Back, Küfner, and Egloff (2010) and the Not-So-Angry Americans. Psychological Science, 22(6), 835-836.
     • Back, M. D., Küfner, A. C., & Egloff, B. (2011). "Automatic or the people?" Anger on September 11, 2001, and lessons learned for the analysis of large digital data sets. Psychological Science, 22(6), 837-838.
5. Observational data (forecasting)
   • 2.4.2 Forecasting and nowcasting. Bit by Bit.
   • Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with Web search. PNAS, 107(41), 17486-17490.
   • Schrodt, P. A., Yonamine, J., & Bagozzi, B. E. (2013). Data-based computational approaches to forecasting political violence. In Handbook of computational approaches to counterterrorism (pp. 129-162). Springer New York.
   • Google Flu Trends
     • Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
     • Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: traps in big data analysis. Science, 343(6176), 1203-1205.
6. Observational data (approximating experiments)
   • 2.4.3 Approximating experiments. Bit by Bit.
   • Phan, T. Q., & Airoldi, E. M. (2015). A natural experiment of social network formation and dynamics. PNAS, 112(21), 6595-6600.
   • Hersh, E. D. (2013). Long-term effect of September 11 on the political behavior of victims' families and neighbors. PNAS, 110(52), 20959-20963.
   • Cohen, P., et al. (2016). Using Big Data to Estimate Consumer Surplus: The Case of Uber. Working paper.
7. Asking questions (fundamentals)
   • "Chapter 3: Asking Questions." Bit by Bit. Sections 3.1-3.4.
   • Schuldt, J. P., Konrath, S. H., & Schwarz, N. (2011). "Global warming" or "climate change"? Whether the planet is warming depends on question wording. Public Opinion Quarterly, 75(1): 115-124.
   • Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31(3), 980-991.
   • The Upshot: We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results.
8. Asking questions (digitally-enriched)
   • "Chapter 3: Asking Questions." Bit by Bit. Sections 3.5-3.7.
   • Sugie, N. F. (2016). Utilizing Smartphones to Study Disadvantaged and Hard-to-Reach Groups. Sociological Methods & Research, 0049124115626176.
   • Lax, J. R., & Phillips, J. H. (2009). How should we estimate public opinion in the states?. American Journal of Political Science, 53(1), 107-121.
   • Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805.
9. Experiments
   • "Chapter 4: Running experiments." Bit by Bit.
   • Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295-298.
   • Milkman, K. L., Akinola, M., & Chugh, D. (2015). What happens before? A field experiment exploring how pay and representation differentially shape bias on the pathway into organizations. Journal of Applied Psychology, 100(6), 1678.
10. Experiments (more)
    • Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon. com's Mechanical Turk. Political Analysis, 20(3), 351-368.
    • King, G., Pan, J., & Roberts, M. E. (2014). Reverse-engineering censorship in China: Randomized experimentation and participant observation. Science, 345(6199), 1251722.
    • Munger, K. (2017). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 39(3), 629-649.
11. Simulated data
    • "Indirect Inference," New Palgrave Dictionary of Economics
    • Benoit, Kenneth, "Simulation Methodologies for Political Scientists," The Political Methodologist, 10:1, pp. 12-16.
    • Recommended readings on simulation methods (not required for class)
      • Wolpin, Kenneth I., The Limits of Inference without Theory, MIT Press, 2013.
      • Davidson, Russell and James G. MacKinnon, "Section 9.6: The Method of Simulated Moments," Econometric Theory and Methods, Oxford University Press, 2004.
12. Simulated data (cont.)
13. Collaboration
    • "Chapter 5: Collaborating". Bit by Bit.
    • Pro Git
14. Collaboration (cont.)
15. Ethics
    • "Chapter 6: Ethics." Bit by Bit.
    • Zimmer, M. (2016). OkCupid Study Reveals the Perils of Big-Data Science. Wired.
16. Ethics (cont.)
    • UChicago Social & Behavioral Sciences Institutional Review Board
      • Skim site
      • Specifically read "Does My Research Need IRB Review?"
    • Facebook emotional contagion study
      • Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS, 111(24), 8788-8790.
      • Editorial Expression of Concern: Experimental evidence of massive-scale emotional contagion through social networks. (2014) PNAS, 111(29), 10779.
      • Watts, D. J. (2014). Stop complaining about the Facebook study. It's a golden age for research. The Guardian.
      • Rosen, J. (2014). Facebook's controversial study is business as usual for tech companies but corrosive for universities. The Washington Post.
      • Vertesi, J. (2014). The Real Reason You Should Be Worried About That Facebook Experiment. Time.
    • Kosinski, M., & Wang, Y. (2017, September 24). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology. Retrieved from psyarxiv.com/hv28a
    • Parry, M. (2011). Harvard Researchers Accused of Breaching Students' Privacy. Chronicle of Higher Education.
17. Exploratory data analysis
    • Exploring Histograms
    • Unwin, A. (2015). Graphical data analysis with R (Vol. 27). CRC Press. - lots of good material here on graphical methods for EDA and how to implement them using different packages in R (e.g. graphics, ggplot2, lattice)
    • VanderPlas, Jake. (2016). Python Data Science Handbook. O'Reilly Media, Inc. - see chapter 4 for implementing visualization methods in Python with matplotlib and seaborn
18. Exploratory data analysis (cont.)
19. Exploratory data analysis - dimension reduction
    • Chapter 10.1-10.2 in An Introduction to Statistical Learning
20. Exploratory data analysis - clustering
    • Chapter 10.1-10.3 in An Introduction to Statistical Learning