MACS 30200 - Perspectives on Computational Research (Spring 2017)

• Meeting day/time: MW 11:30-12:50pm, Saieh Hall, Room 247
• Office hours also available by appointment

Course description

This course focuses on applying computational methods to conducting social scientific research through a student-developed research project. Students will identify a research question of their own interest that involves a direct reference to social scientific theory, use of data, and a significant computational component. The students will collect data, develop, apply, and interpret statistical learning models, and generate a fully reproducible research paper. We will identify how computational methods can be used throughout the research process, from data collection and tidying, to exploration, visualization and modeling, to the final communication of results. The course will include modules on theoretical and practical considerations, including topics such as epistemological questions about research design, writing and critiquing papers, and additional computational tools for analysis.

Grades

Students will turn assignments in via their own public GitHub repository named MACS30200proj. The directory structure of this repository should be the following.

• github.com/yourgithubhandle/MACS30200proj
  • ProblemSets
  • PS1
  • PS2
  • PS3
  • Proposal
  • LitReview
  • Theory
  • MethodsResults
  • Poster
  • FinalPaper

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)

References and 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.

• Diagnostic tests for OLS/GLM
  • Ch 3.3.3 in An Introduction to Statistical Learning
• Interaction terms
  • Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political analysis, 63-82.
• p-hacking
  • Significant
  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA's statement on p-values: context, process, and purpose. Am Stat, 70(2), 129-133.
  • "Science Isn't Broken" by Christie Aschwanden. FiveThirtyEight.
  • Beall, A. T., & Tracy, J. L. (2013). Women are more likely to wear red or pink at peak fertility. Psychological Science, 0956797613476045.
  • "Too Good to Be True" by Andrew Gelman. Slate.
  • "Response by Jessica Tracy and Alec Beall to my critique of the methods in their paper, 'Women Are More Likely to Wear Red or Pink at Peak Fertility'" by Andrew Gelman.