SC704: Regression Models for Categorical Data
Spring 2014
Tuesday/Thursday 12:00 – 1:15 pm
O’Neill 245
Professor: Sara Moorman
Office: 404 McGuinn Hall
Office hours: Tuesdays 2:15 - 3:15 pm; Thursdays 9:15 - 10:15 am
E-mail: moormans@bc.edu
About the Course
This applied course is designed for students in sociology, education, nursing, organizational
studies, political science, psychology, or social work with a prior background in statistics at the
level of SC703: Multivariate Statistics. It assumes a strong grounding in multivariate regression
analysis. The major topics of the course will include OLS regression diagnostics, binary,
ordered, and multinomial logistic regression, models for the analysis of count data (e.g., Poisson
and negative binomial regression), treatment of missing data, and the analysis of clustered and
stratified samples. All analyses in the course will be conducted using Stata, but no previous Stata
experience is necessary.
Readings
Required textbooks:
 Enders, Craig K. 2010. Applied Missing Data Analysis. ISBN: 9781606236390

Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent
Variables. ISBN: 0803973748

Long, J. Scott and Jeremy Freese. 2006. Regression Models for Categorical Dependent
Variables Using Stata. 2nd ed. ISBN: 1597180114
Recommended textbook:
 Acock, Alan C. 2012. A Gentle Introduction to Stata. 3rd ed. revised ISBN:
9781597181099
Course reserves online:
Access “*” entries as .pdf files through the library website (http://www.bc.edu/libraries/).
SC704 Regression Models for Categorical Data
page 2 of 5
Software
This course requires the use of the statistical program Stata. It is available on the computers in
McGuinn 410, the Sociology graduate student lounge. For use on your own computer, you have
two options: (1) access the program through remote connection to apps.bc.edu, or (2) purchase
the program through BC’s Research Services.
Assessment
Grading scale
A
93 – 100%
B
83 – 86%
F
0 – 59%
Task
Article presentation
Project draft
Peer review
Presentation
Final paper draft
AB-
90 – 92%
80 – 82%
Due date
For you to select
April 1
April 15
April 29 / May 1
May 6
B+
C
87 – 89%
60 – 79%
Percentage of grade
20
15
20
25
20
Article Presentation (More detail to follow)
It’s important to be able to understand published work that uses regression models for categorical
data, even if you never use them in your own work. You will (a) sign up for a presentation topic
(i.e., method we’ve learned in class) and date; (b) find a published article that uses the method,
either in a peer-reviewed journal in your area or in a general Sociology journal; (c) share the
article with your classmates; (d) briefly present on the article and lead a class discussion
appraising its merits.
Research Project (More detail on each step to follow)
I find that the best way to learn statistics is to practice them on real data that mean something to
you. Therefore, you’ll spend the semester producing a chunk of a journal article, including
methods, results, tables, and any helpful figures. You will leave off introduction/lit review and
discussion sections, except for a few paragraphs to set up the research question and draw a
conclusion about it. The article is required to include two or more of the methods covered in
class from January 30 onward. For example, you might estimate (a) a test of mediation in a
complex survey dataset, or (b) one model that requires binary logistic regression and a second
that requires Poisson regression, or (c) a multinomial logistic regression on multiply-imputed
data. Neither using Stata for your analyses nor testing a simple OLS regression model “count”
towards your two methods.
SC704 Regression Models for Categorical Data
page 3 of 5
At the beginning of April, you will submit a draft of the paper. The draft has two
purposes: (a) to ensure that you pace your work throughout the semester, rather than try to write
the whole paper the night before it is due, and (b) to provide opportunity for my feedback on
your work. As such, the update is required but not graded. If you turn it in, you will receive full
credit. You will also exchange your draft with a classmate and complete peer reviews for one
another. I’ll match you up later in the semester based on the similarity of your topic, data, or
methods. Finally, you’ll give a conference-style presentation of your project in one of the last
two classes, and on May 6, submit your completed paper.
Although it’s certainly not a requirement, you should seriously consider using this project
as an opportunity to meet a degree requirement (e.g., area exams), prepare a conference
presentation, and/or develop a submission for publication. If you’re already working on a project,
I encourage you to use this course to develop it. If you’re starting from scratch, many datasets
are publicly available from universities and government agencies, and many more are available
to researchers through BC’s subscription to the Inter-University Consortium for Political and
Social Research (ICPSR) at the University of Michigan. We’ll talk about accessing secondary
data in January.
Submitting Your Work
 E-mail me your work, including your last name in the title of the document.
 All materials are due by 11:59 pm on their due dates. I will not accept late work.
Academic Honesty
Cheating, plagiarism, collusion, and other academic offenses will result in (a) automatic failure
of the assignment, and (b) a report to the Dean and the Committee on Academic Integrity. For
further information, please review BC’s policies on academic integrity at: www.bc.edu/integrity
Accommodations
If you are a student with a documented disability seeking reasonable accommodations in this
course, please contact Kathy Duggan, (617) 552-8093, dugganka@bc.edu, at the Connors Family
Learning Center regarding learning disabilities and ADHD, or Paulette Durrett, (617) 552-3470,
paulette.durrett@bc.edu, in the Disability Services Office regarding all other types of disabilities,
including temporary disabilities. Advance notice and appropriate documentation are required for
accommodations.
SC704 Regression Models for Categorical Data
Schedule
Date
Topic
January 14
Using Stata
January 16
Using Stata
January 21
January 30
Locating and using
data for secondary
research
Ordinary least squares
(OLS) regression:
Review and
diagnostics
Ordinary least squares
(OLS) regression:
Review and
diagnostics
Complex survey data
February 4
Complex survey data
February 6
Mediation
February 11
Mediation
February 13
Moderation
February 18
Moderation
February 20
Missing data
February 25
Missing data
February 27
Multiple imputation
January 23
January 28
page 4 of 5
Reading
 Long chapter 2
 Long & Freese chapters 1-3
 Johnson and Elliott*
 Kreuter and Valliant*
 Winship and Radbill*
 Baron and Kenny*
 Hayes*
 MacKinnon, Fairchild, and Fritz*
 Fairchild and McQuillin*
 Wu and Zumbo*
 Enders chapters 1, 2, and 10
 Enders chapters 7, 8, and 9
SC704 Regression Models for Categorical Data
Date
Topic
March 4
NO CLASS
March 6
NO CLASS
March 11
Multiple imputation
March 13
Multiple imputation
March 18
Multiple imputation
March 20
Binary outcomes
March 25
Binary outcomes
March 27
Ordinal outcomes
April 1
Ordinal outcomes
April 3
Nominal outcomes
April 8
Nominal outcomes
April 10
Nominal outcomes
April 15
Count data
April 17
NO CLASS
April 22
Count data
April 24
Count data
April 29
Presentations
May 1
Presentations
page 5 of 5
Reading
 Long chapter 3
 Long & Freese chapter 4
 Long chapter 5
 Long & Freese chapter 5
 Long chapter 6
 Long & Freese chapters 6 and 7
 Long chapter 8
 Long & Freese chapter 8