BASIC STATISTICS FOR SOCIAL AND POLITICAL ANALYSIS
Spring 2018
Instructor: Margarita Corral, PhD Time: Mondays & Wednesdays 2-3:20
Office: Farber 2 (Library) Room: Olin-Sang 201
Office Hours: Tuesdays 1-3pm and by appointment Computer Lab: TBD
Email:
Phone: 781-736-4654
Course Description and Objectives
Social Science disciplines have seen an increase in the use of statistical methods over the last few decades. Becoming familiar with basic statistical methodology will help you to prepare for a career as a social scientist as well as to better understand and make sense of all the information around us (news reports, political campaigns, public opinion surveys, etc.). This course is designed to provide a foundation in statistics focusing on descriptive statistics, inference, hypothesis testing and the basics of regression analysis.
We will use Stata to manage, inspect, and describe quantitative data. Some sessions will be held in the computer lab.
Learning Goals
Upon completion of this course, students will be able to:
1. Describe, manipulate and summarize data
2. Interpret, evaluate and use descriptive analysis (univariate statistics)
3. Complete and interpret inferential statistical data analyses (bivariate statistics)
4. Generate and interpret basic regression models
5. Apply statistical models to real world research questions
6. Think critically about arguments, based on the evaluation of evidence
7. Articulate reasoned arguments clearly, both orally and in written form
8. Use the concepts and methods of political science to conduct research and analysis
9. Students will gain familiarity with a variety of research methods for understanding politics using statistical analysis. Students will learn how to apply statistics to gain knowledge about the functioning and distinctive features of the American political system, and the diversity of other political systems and the significance of these differences, among others.
Course Requirements
This course has no formal prerequisites. No prior knowledge of statistics is assumed nor is a background in statistical software necessary.
Students are expected to read the assigned materials prior to coming to class.
Four-Credit Course (with three hours of class-time per week).
Success in this 4 credit hour course is based on the expectation that students will spend a minimum of 9 hours of study time per week in preparation for class (readings, papers, discussion sections, preparation for exams, etc.).
Course Materials:
The primary textbook for this course will be: Agresti, Allan and Barbara Finley. 2013. Statistical Methods for the Social Sciences, 4th Edition, Prentice-Hall.
There will be other readings available via LATTE
Evaluation
Grading will be divided among: participation (10%), short homework assignments (15%), a midterm exam (20%), a final exam (35%) and short data analysis report (20%).
Short Homework Assignments (4): You will analyze some data using Stata and interpret the results. You are welcome to discuss the problem sets with each other, but the final write-ups should be your own.
Midterm Exam: There will be an in-class midterm. Along with the short assignments will provide indications of your progress in the course.
Final Exam: There will be a take-home exam.
Short Data Analysis Report: Students will complete a short report (around 8-10 pages) employing basic methods to respond to a research question of their own choosing. Students may use the General Social Survey, the American National Election Studies surveys or any other dataset of their interest (United States Behavioral Risk Factor Surveillance System, Census Data, etc.). To aid them in completing their reports we will have a session on how to search for data and access/download the datasets. We will review the kind of questions that can be addressed with these data. Students will have to submit their question in advance so that they can get help locating the data they need.
This report will include a research question, hypothesis, a description of the sample and of the variables used to test the hypothesis (level of measurement, distribution of the responses); a section on the statistical method, and a discussion of the results.
Disabilities
If you are a student with a documented disability on record at Brandeis University and wish to have a reasonable accommodation made for you in this class, please see me immediately
Academic Integrity
You are expected to be honest in all of your academic work. Please consult Brandeis University Rights and Responsibilities for all policies and procedures related to academic integrity. Students may be required to submit work to TurnItIn.com software to verify originality. Allegations of alleged academic dishonesty will be forwarded to the Director of Academic Integrity. Sanctions for academic dishonesty can include failing grades and/or suspension from the university. Citation and research assistance can be found at LTS - Library guides.
Communications
We will make regular use of LATTE and a course mailing list. I will communicate any changes in the syllabus, and accommodations for “snow days” via email. Please check your Brandeis email and LATTE regularly.
Use of technology in the classroom
I come to class to help you learn and I assume that you are here because you want to learn. Using a cell phone or laptop to talk, text, email or surf the web on non-course related matters is disrespectful to me and to your fellow students. If you wish to leave your cell phone on in “Silent” mode because of an ongoing emergency situation that you may need to respond to, please speak to me at the start of class to let me know.
Course Plan
These topics may be amended with student input during the term.
Week 1: Introduction
Wednesday, January 10.
Course Overview
Week 2: Statistical Methodology
Monday, January 15: No Class
Wednesday, January 17: What is Statistics? Some Real-Life Applications
Video:
· Peter Donnelly: How juries are fooled by statistics (available via LATTE)
· Agresti and Finlay. Chapter 1. Introduction
Thrusday, January 18: Distinction between description and inference, types of variables, levels of measurement
Readings:
· Agresti and Finlay. Chapter 2. Sampling and Measurement
· Robert W. Jackman (1987) “Political Institutions and Voter Turnout in the Industrial Democracies” American Political Science Review. 81. Read: p. 409-411
· Hancock, Trevor et al (1999) “Indicators that count! Measuring Population Health at the Community Level” Canadian Journal of Public Health, 90, S22
Week 3: Descriptive Statistics.
Monday, January 22: Measures of Central Tendency (mean, median, mode, quartiles).
Readings:
· Agresti and Finlay. Chapter 3.1 and Chapter 3.2
· “The Median Isn't the Message” by Stephen Jay Gould (available via LATTE)
Wednesday, January 24: Measures of variation (range, variance, standard deviation).
Readings:
· Agresti and Finlay. Chapter 3.4 to Chapter 3.6
· Measures of variation in the news: Brooks, David. “The Harlem Miracle”. New York Times May 7, 2009
· Lofquist, Daphne, et al. (2012). “Households and Families: 2010”. 2010 Census Briefs
Week 4: Descriptive Statistics
Monday, January 29: Computer lab. Intro to Stata. Data display with Stata
Wednesday, January 31: Computer Lab. Intro to Stata II
Week 5: Probability Distributions
Short Assignment #1 due
Monday, February 5: Normal Distribution
Readings:
· Agresti and Finlay. Chapter 4.1 to 4.2
Wednesday, February 7: z-values, z-scores.
Readings:
· Bennet, Jeffrey et al. “A Normal World” in Statistical Reasoning for Everyday Life (available via LATTE)
· Glick, J. E. and Hohmann-Marriott, B. (2007), “Academic Performance of Young Children in Immigrant Families: The Significance of Race, Ethnicity, and National Origins”. International Migration Review, 41:371–402
Week 6. Sampling Distributions
Tuesday, February 12: Sampling distribution, standard error
Reading:
· Agresti and Finlay. Chapter 4.3 to Chapter 4.6
Tuesday, February 14: Central Limit Theorem
Reading:
· "As ‘Normal’ as Rabbits’ Weights and Dragons’ Wings" New York Times, September 23, 2013
· Video: Bunnies, Dragons and the ‘Normal’ World
Week 7: Feb 19-21. No class. Midterm Recess
Week 8: Confidence Intervals
Short Assignment #2 due
Monday, February 26: Point Estimates and confidence intervals for means
Readings:
· Agresti and Finlay. Chapter 5.1 to 5.4
Wednesday, February 28: Point Estimates and confidence intervals for proportions
Readings:
· Cumming, G., & Finch, S. (2005). Inference by Eye: Confidence Intervals and How to Read Pictures of Data.American Psychologist,60(2), 170-180
Week 9: Hypothesis Testing
Monday, March 2: Midterm Exam
Wednesday, March 4: Hypotheses, p-values, testing for the mean.
Readings:
· Agresti and Finlay. Chapter 6
· Jeffrey J. Mondak, “Newspapers and Political Awareness.” (1995) American Journal of Political Science 39; see especially pp. 523-525, for a two-sample t-test.
Week 10: Hypothesis Testing II
Monday, March 12: Testing proportions
Reading:
· Agresti and Finlay. Chapter 7
· Nuzo, Regina. “Scientific Method: Statistical Errors”. Nature. February 12, 2014
Wednesday, March 14: Computer Lab: Using Stata to test hypotheses
Week 11: Association between Categorical Variables
Monday, March 19: Contingency tables, Chi-square
Readings:
· Agresti and Finlay. Chapter 8
· Charles R. Chandler, Yung-mei Tsai. 2001. Social factors influencing immigration attitudes: an analysis of data from the General Social Survey, The Social Science Journal, Volume 38, Issue 2, Pages 177-188
· McClelland, G. M. and Teplin, L. A. (2001), Alcohol Intoxication and Violent Crime: Implications for Public Health Policy. The American Journal on Addictions, 10:s70–s85
Wednesday, March 21: Computer Lab. Using Stata to run contingency tables
Week 12: Correlation and Linear Regression
Short Assignment #3 due
Monday, March 26: Linear relationships, correlation
Readings:
· Agresti and Finlay. Chapter 9
· Amy Caiazza. (2004). “Does Women’s Representation in Elected Office Lead to Women-Friendly Policy?” Women and Politics 26 35-70. See especially. 45-53
Wednesday, March 28: Session about the Short Data Analysis Report.
Week 13: April 2-6. Passover and Spring Recess
Week 14: Correlation and Linear Regression, continued
Monday, April 9: Linear Regression
Reading:
· Agresti and Finlay. Chapter 10
· Lodola, German and Margarita Corral. (2012). “Support for Same-Sex Marriage in Latin America.” In Jason Pierceson, Adriana Piatti-Crocker, and Shawn Schulenberg (Eds). Same-Sex Relationship Recognition in Latin America: Promise and Resistance. Lexington: Lexington Books. Pages 41-49.(Available via LATTE)
Wednesday, April 11: Computer Lab: Using Stata to run correlations and simple regressions
Week 15: April 10-15. Correlation and Linear Regression, continued
Short Assignment #4 due
Monday, April 16: Linear Regression II (Assumptions)
Reading:
· Napier JL, Jost JT. 2008. Why are conservatives happier than liberals?Psychological Science.19: Pages 565–72
· Jia, H., & Lubetkin, E. I. (2005). The impact of obesity on health related quality-of-life in the general adult U.S. population. Journal of Public Health, 27, 156-164
Thursday, April 18: Computer Lab. Using Stata to run multivariate regressions
Week 15: April 23. Review for final
Short report due: April 30
Final Exam: May 4