SOCY7708: Hierarchical Linear Modeling
Spring 2016
Wednesday 1:30 – 4:00 pm
O’Neill 245
Professor: Sara Moorman
Office: 404 McGuinn Hall
Office hours: Tuesdays 10:30 am - 11:30 am and by appointment
E-mail: moormans@bc.edu
About the Course
This applied course on hierarchical linear modeling is designed for graduate students with a
thorough knowledge of OLS regression. It will cover 2-level models for continuous, categorical,
and count outcomes, 3-level models, growth curve models, and models for couple data. The
goals of the course are to develop the skills necessary to identify an appropriate technique for
multilevel data analysis, estimate models, conduct diagnostics, and interpret results. We will use
HLM 6 to perform the analyses; no prior knowledge of this software is required.
Readings
Required textbook:
Hox, Joop. 2010. Multilevel Analysis: Techniques and Applications (2nd ed.).
ISBN: 9781848728462
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.
As promised, we’ll also use the program HLM. You can download the free student version here:
http://www.ssicentral.com/hlm/student.html
Assessment
Grading scale
A
93 – 100%
B
83 – 86%
F
0 – 59%
AB-
90 – 92%
80 – 82%
B+
C
87 – 89%
60 – 79%
SC704 Regression Models for Categorical Data
Task
Article presentation
Project draft
Peer review
Presentation
Final paper draft
Due date
For you to select
April 6
April 13
May 4
May 11
page 2 of 4
Percentage of grade
20
15
20
25
20
Article Presentation (More detail to follow)
In addition to being able to use hierarchical linear models in your work, it’s important that you
be able to understand others’ published work that uses the technique. 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 one or more of the methods covered in
class from February 17 through April 13.
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 and make a good-faith
effort on it, 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 during the last class, and on May 11, 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.
SC704 Regression Models for Categorical Data
page 3 of 4
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.
Schedule
Date
January 20
January 27
February 3
February 10
February 17
February 24
March 2
Topic
Using Stata; Ordinary
least squares (OLS)
regression: Review and
diagnostics
Locating and using
data for secondary
research
Data management and
missing data
Between- and withingroup variance
Random intercept
models
Dyadic data
Random coefficients
models
Reading
Hox Chapter 12
Hox Chapter 1
Hox pp. 59-63; 68-69
Hox Chapter 2
SC704 Regression Models for Categorical Data
page 4 of 4
Date
Topic
Reading
March 16
Hox Chapters 3-4
March 30
Random coefficients
models
Categorical and count
outcomes
Growth curves
April 6
Growth curves
Hox Chapter 16
April 13
Three level models
Hox pp. 32-36
April 20
HLM (the software)
April 27
HLM (the software)
May 4
Presentations
March 23
Hox Chapters 6-7
Hox Chapter 5