Statistical Modeling
(Revision date: 12 August 2010)

Course Content and Objectives:

This course introduces students to quantitative methods as they are employed in the social sciences, and it satisfies the post-freshman GER writing requirement. The course is designed to be particularly useful to thesis writers, pre-law students who desire the capability to understand statistical matters relevant to court cases, undergraduates who may wish to pursue graduate study in political science, and undergraduates in general who seek a working knowledge of common statistical approaches to data analysis. Here we examine descriptive statistics, frequency tables, regression, and logistic regression. We begin the course with a discussion of some elementary topics in probability since this subject creates a basis for the remainder of the course. We will be using R to calculate our statistics in this course. If you want to know why R is important for every student to know, see my YouTube presentation on the subject.

I feel confident that you will all do fine in the course. Just remember to hand in all the assignments on the due date. The course requirements are listed below. I enjoy giving good grades to students who deserve them. You can do very well in this course.

The reading assignments listed below in the weekly outline are required of all students. Additional suggested reading assignments will be given as the course proceeds, and these readings will focus on applications of the methods covered in the core text. All students are recommended to work together, sharing information and discoveries.

Note relevant to students participating in an honors program (in any department): This class has a term paper/project that can be incorporated into the writing of an honor's thesis, should a student desire to do this. All students (regardless of whether or not they are participating in an honors program) will be offered a default data set to use in the class paper/project. However, should a student who is participating in an honors program in any department want to substitute his or her own data set in place of the default data set as a means of learning how to analyze the data for his or her honors thesis, this is OK. This is a great way to get ahead of the game with your thesis. If a student follows this route, the data set and project must be approved by the instructor in advance of submitting the first draft of the paper.

Class Requirements:

Regular reading and writing assignments are matched with class discussions, all focusing on the use and interpretation of various quantitative approaches to the study of social and political phenomena. The course grade depends on the evaluation of all writing assignments, tests, final project, as well as class participation and attendance. Some of the writing assignments will be built around the final project, and your final project will be constantly enhanced and revised as an ongoing work in progress throughout the semester. Each time you hand it in, you will be required to incorporate the corrections and other feedback made the last time you handed it in. That way the project will grow both in length and quality as the semester progresses. There are three formal drafts of the final writing project. All drafts are required.

The grades are determined as follows:
20% Attendance (Two absences are permitted without penalty.)
20% Weekly Writing assignments
20% Midterm Exam
20% Final Exam
20% Final Paper (final draft)

Note that attendance is important. If you are in class, I know that you have been exposed to the subject of the day.

The Department of Political Science has a grading standard that applies to all courses. You can read about it here.

The Honor Code is strictly enforced in this course. Plagiarism is an honor code violation. A signature forgery on attendance is an honor code violation.

Podcast Policy:

Podcasting courses can assist students tremendously. Students can listen to lectures more than once, and they can catch up on classes that were missed for, say, reasons of illness or religious obligation. I record and podcast many of the classes in this course. By taking this course, all students are automatically giving their permission to be recorded during class participation. No further written permission is required.

Disabilities Statement:

It is the policy of Emory University to make reasonable accommodations for qualified students with disabilities. All students with special requests or need for accommodations should make this request in person as soon as possible after first visiting the Office of Disabilities.

Required Texts:

Statistical Methods for the Social Sciences (4th Edition), by Alan Agresti and Barbara Finlay
Applied Regression: An Introduction, by Michael S. Lewis-Beck
Graph Algebra: Mathematical Modeling with a Systems Approach, by Courtney Brown

Reserved Reading:

Goldstein, Larry J, David I. Schneider, and Martha J. Siegel. 1988. Finite Mathematics and Its Applications, Third Edition. Englewood Cliffs, New Jersey: Prentice Hall, chapters 6 and 7 (probaility and statistics).

Patterson, Samuel C, and Gregory A Caldiera. 1983 (September). "Getting Out the Vote: Participation in Gubernatorial Elections," American Political Science ReView, Vol. 77, No. 3, pp. 675-89.

Patterson, Samuel C, and Gregory A Caldiera. 1984 (September). "The Etiology of Partican Competition," American Political Science Review, Vol. 78, No. 3, pp. 691-707.

Data Sets Used in This Course:

All Data (This is a zip file with three data sets. All data sets are formated as data frames in R.)

Internet Resources:

Emory University's Electronic Data Center
R Startup Guide (Installing help for Windows and Macs)
The Cran Home Page: This is where you get R
Wikipedia's discussion of the R programming language
A discussion in a political methodology journal about the use of R
John Fox's methods class page
The Quick-R page, a great resource for SAS and SPSS users
The "Kickstarting R" Intro Manual
Hugh C. Pumphrey's course notes on R
Thomas Lumley's course notes on R
Rob Cribbie's course notes on R
Frank McCown's easy intro to graphing using R
The R Graphical Manual: A really comprehensive collection of graphics methods
The American Phytopathological Society has published a great introduction to R
How to read a table
More on tables
Gary King's excellent advice on writing your first publishable paper
How to Use a Codebook, from Princeton University
Inter-University Consortium for Political and Social Research (ICPSR)
National Election Studies, The University of Michigan
Rice Virtual Lab in Statistics
SAS Documentation for version 8.2
SAS Documentation for version 9.1 (This is the one we use in class.)
Statistical Abstract of the United States
Statistics Calculators from UCLA
SticiGui Online Statistics Text, by Philip B. Stark, University of California, Berkeley
Surf Stat, an online statistics text

Special Meeting:

Dr. Rob O'Reilly and Chris Palazzolo will meet with the class on TBA during the normal class time in our normal room. This meeting is both vitally important and required of all students. Dr. O'Reilly will present an overview of how students can access a variety of data sources with which they can conduct empirical analyses. Chris Palazzolo will present an overview of a variety of data and statistical library resources that are in print. You will need these resources for your class projects. Students participating in an honors program may want to use these resources to obtain a data set to analyze for a thesis paper.

WEEKLY OUTLINE

Week 1:
Lectures: Sampling, Measurement, and an Introduction to Probability
Readings:
Agresti and Finlay, Chapters 1 & 2
Goldstein, Schneider, and Siegel: Chapter on probability
Nagler, Jonathan. "Coding Styles and Good Computing Practices."

Week 2:
Lectures: Descriptive Statistics & Probability
Readings:
Agresti and Finlay, Chapters 3&4
Goldstein, Schneider, and Siegel: Chapter on probability and statistics
Streiner, D. L. 1996. "Maintaining standards: Differences between the standard deviation and standard error, and when to use each." Canadian Journal of Psychiatry, 41(8), 498-502.
Written Assignment: Regular Assignment #1, due TBA

Week 3:
Lectures: Statistical Inference: Estimation
Readings:
Agresti and Finlay, Chapter 5
Goldstein, Schneider, and Siegel: finish.
Nelson Polsby, "The Institutionalization of the U.S. House of Representatives."
Written Assignment: Regular Assignment #2, due TBA

Week 4:
Lectures: Statistical Inference: Significance Tests
Readings:
Agresti and Finlay, Chapter 6 & 7
Wood, Sandra L., Linda Camp Keith, Drew Noble Lanier, and Ayo Ogundele, "'Acclimation Effects' for Supreme Court Justices: A Cross-Validation, 1888-1940."
Written Assignment: TBA

Week 5:
Lectures: Hypothesis Testing with Groups and Categorical Variables
Readings:
Agresti and Finlay, Chapters 8
Written Assignment: Regular Assignment #3, due TBA

Week 6:
Lectures: Problem set review for the mid-term
Readings:
Agresti and Finlay, Problem Set
Written Assignment: No written assignment is due on exam week. Work on the practice problems.
MID-TERM EXAM IS ON TBA: Be sure to work on the exam practice problem set. . . . .

Week 7:
Lectures: Line Fitting and Simple Regression
Data set link: This is a data set using international relations data that will be used in the lecture.
Codebook link: This is the codebook for the above data set.
Readings:
Agresti and Finlay, Chapter 9
Lewis-Beck, Chapters 1 - 3
Brown, chapter 1
Written Assignment: Final Paper/Project Assignment Draft #1: — Hand in the first draft of your final project. This should include an introduction, a discussion of the problem under investgation, a modest literature review that is relevant to the problem, and a discussion of the data to be used in the project as well as the unit of analysis. Feedback will be given to you by the next class. Read more detailed instructions for this first draft. due TBA

Week 8:
Lectures: Multiple Regression
Readings:
Agresti and Finlay, Chapter 10 & 11
Brown, chapter 2
Written Assignment: Regular Assignment #4, due TBA

Week 9:
Lectures: Dummy Variable Regression
Readings: Gerald Wright, "Linear Models for Evaluating Conditional Relationships"
Agresti and Finlay, Chapter 13
Brown, chapter 3
Written Assignment: Regular Assignment #5, due TBA
Final Paper/Project Assignment Draft #2: Hand in a revised (corrected) version of your previous initial draft of your final project, but now also include a statement of the hypotheses being tested and a discussion of the variables. Be sure to identify the dependent and independent variables. Also include a table with relevant descriptive statistics. Feedback will be given by the next class. Read more detailed instructions for this second draft. Due TBA.

Week 10:
Lectures: Model Building and Multicollinearity
Readings:
Agresti and Finlay, Chapter 14
Brown, chapter 4
Written Assignment #8: Final Paper/Project Assignment Draft #3: Hand in the third draft of the final project that includes all revisions and corrections suggested for the previous draft. Now include some more advanced statistical analysis as well. This should probably be in the form of tables. Be sure to interpret the statistics. Don't just report them. Feedback will be given by the next class. Read more detailed instructions for this third draft. Due TBA.

Week 11:
Lectures: Nonlinear Specifications
Readings:
Bear F. Braumoeller, "Hypothesis Testing and Multiplicative Interaction Terms"
Thomas Brambor, William R. Clark, and Matt Golder, "Understanding Interaction Models: Improving Empirical Analyses"
Final Paper/Project Assignment: Continue working on your final project that includes all revisions and corrections suggested for the previous draft. For the final draft, finish the statistical analysis, try to incorporate one or more plots, and refine your conclusion, summary, or implications section. The final draft is due on the last day of normal class, 12 December.
Written Assignment: Regular Assignment #6, due Thursday TBA

Week 12:
Lectures: Logistic Regression 1
Readings:
Agresti and Finlay, Chapter 15
Written Assignment: Regular Assignment #7, due TBA

Week 13:
Lectures: Logistic Regression 2
Readings:
Agresti and Finlay, Chapter 15
Written Assignment: Regular Assignment #8: Final Paper/Project Assignment Final Version Due TBA

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