MACS 30000 - Perspectives on Computational Analysis

	Dr. Richard Evans	Dr. Benjamin Soltoff	Ms. Ging Cee Ng (TA)
Email	[email protected]	[email protected]	[email protected]
Office	250 Saieh Hall	249 Saieh Hall	251 Saieh Hall
Office Hours	W 2:30-4:30pm	Th 2-4pm	M 2-3pm
GitHub	rickecon	bensoltoff	gingcee

• Meeting day/time: MW 11:30-12:50pm, 247 Saieh Hall for Economics
• Lab session: T 5-5:50pm, location TBD
• 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

• Salganik, Matthew J. Bit by Bit: Social Research in the Digital Age, Princeton University Press, Open review edition.
• Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.

Evaluation

Assignment	Quantity	Points	Total Points
Short paper	4	15	60
Problem set	4	10	40
Final exam	1	20	20

Disability services

If you need any special accommodations, please provide us 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
Mon, Sep. 26	Intro to Computational Social Science
Wed, Sep. 28	Scientific method
Mon, Oct. 3	No class (conference)
Wed, Oct. 5	Ethics
Mon, Oct. 10	Observational data	Ethics paper due
Wed, Oct. 12	Observational data
Mon, Oct. 17	Observational data
Wed, Oct. 19	Observational data
Mon, Oct. 24	Collecting your own data	Observational data paper due
Wed, Oct. 26	Collecting your own data
Mon, Oct. 31	Experiments	Asking questions paper due
Wed, Nov. 2	Simulated data
Mon, Nov. 7	Simulated data	Experiments paper due
Wed, Nov. 9	Data visualization and description	Problem Set 1 due
Mon, Nov. 14	Data visualization and description
Wed, Nov. 16	Data visualization and description
Mon, Nov. 21	Data visualization and description	Problem Set 2 due
Wed, Nov. 23	Data visualization and description
Mon, Nov. 28	Collaboration	Problem Set 3 due
Wed, Nov. 30	Collaboration
Fri, Dec. 2		Problem Set 4 due
Wed, Dec. 7	Final exam [10:30am-12:30pm]

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. The scientific method
   • Bhattacherjee, A. (2012). Social science research: principles, methods, and practices. Chapters 1-4. Skim/review as needed.
   • Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired.
   • Einav, L., & Levin, J. (2014). The Data Revolution and Economic Analysis. Innovation Policy and the Economy, 14(1), 1-24.
   • Schrodt, P. A. (2014). Seven deadly sins of contemporary quantitative political analysis. Journal of Peace Research, 51(2), 287-300.
3. No class (Big Questions, Big Data, and Big Computation (B³): Frontiers of Computational Social Science conference)
4. Ethics
   • "Chapter 6: Ethics." Bit by Bit.
   • 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.
   • Parry, M. (2011). Harvard Researchers Accused of Breaching Students' Privacy. Chronicle of Higher Education.
   • Zimmer, M. (2016). OkCupid Study Reveals the Perils of Big-Data Science. Wired.
   • UChicago Social & Behavioral Sciences Institutional Review Board
     • Skim site
     • Specifically read "Does My Research Need IRB Review?"
5. Observational data (counting)
   • "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.
   • Edelman, B. G., & Luca, M. (2014). Digital discrimination: The case of airbnb.com. Harvard Business School NOM Unit Working Paper, (14-054).
   • Chetty, R., Hendren, N., Kline, P., Saez, E., & Turner, N. (2014). Is the United States still a land of opportunity? Recent trends in intergenerational mobility. The American Economic Review, 104(5), 141-147.
6. Observational data (measuring)
   • 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.
7. 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.
   • 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.
8. 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.
9. 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.
   • Pew Research Center Survey Methodology
     • U.S. Survey Research: Our survey methodology in detail
     • U.S. Survey Research: Sampling
   • 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.
   • Introducing the YouGov Referendum Model
10. Asking questions (digitally-enriched)
    • "Chapter 3: Asking Questions." Bit by Bit. Sections 3.5-3.7.
    • 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.
    • Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076.
    • Sugie, N. F. (2016). Utilizing Smartphones to Study Disadvantaged and Hard-to-Reach Groups. Sociological Methods & Research, 0049124115626176.
11. Experiments
    • "Chapter 4: Running experiments." Bit by Bit.
    • King, G., Pan, J., & Roberts, M. E. (2014). Reverse-engineering censorship in China: Randomized experimentation and participant observation. Science, 345(6199), 1251722.
    • Edelman, B. G., Luca, M., & Svirsky, D. (2015). Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment. Harvard Business School NOM Unit Working Paper, (16-069).
    • 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.
12. Simulated data
    • "Indirect Inference," New Palgrave Dictionary of Economics
    • Wolpin, Kenneth I., The Limits of Inference without Theory, MIT Press, 2013.
    • Benoit, Kenneth, "Simulation Methodologies for Political Scientists," The Political Methodologist, 10:1, pp. 12-16.
    • Davidson, Russell and James G. MacKinnon, "Section 9.6: The Method of Simulated Moments," Econometric Theory and Methods, Oxford University Press, 2004.
13. Simulated data (cont.)
14. Data visualization and description
    • Scott, David W., Chapters 1-4, Multivariate Density Estimation: Theory, Practice, and Visualization, 2nd edition, John Wiley & Sons, 2015.
    • McKinney, Wes, Python for Data Analysis, O'Reilly Media, Inc. (2013).
15. Data visualization and description (cont.)
16. Data visualization and description (cont.)
17. Data visualization and description (cont.)
18. Data visualization and description (cont.)
19. Collaboration: distributed data collection and analysis
    • "Chapter 5: Collaborating". Bit by Bit.
    • Chacon, Scott and Ben Straub, Pro Git: Everything You Need to Know about Git, 2nd edition, Apress, 2014.
    • Evans, Richard W., "Chapter 3: Git and GitHub.com". Overlapping Generations Models for Policy Analysis: Theory and Computation., unpublished draft (2016).
    • OSPC Data Visualizations project
20. Collaboration: distributed data collection and analysis (cont.)