EDMS769V: Multilevel Modeling
Spring Semester 2014
Professor
Dr. Laura Stapleton
Office: 1230C Benjamin
phone: 301/405-1933
e-mail: Lstaplet@umd.edu
Office hours
Tuesday 4-5pm
first come, first served,
or at other times by appt
Required Course Material

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data
Analysis Methods. Thousand Oaks, CA: Sage Publications.

Articles/chapters to be handed out/posted; see reading list at end of syllabus.

Starting February 10th, bring a fully-charged laptop to class, with the following software loaded:
o HLM -- http://www.ssicentral.com/hlm/student.html (to be used starting the 3rd week of
class); note that this is a Window’s only program.
o Optimal Design software v.3.01 – http://sitemaker.umich.edu/groupbased/optimal_design_software -- not to be used until later in the semester – not sure if it
works on Mac.
o Mplus http://www.statmodel.com/demo.shtml -- not to be used until later in the
semester; can be used in Windows or Mac.
Course Overview
This course will allow students to obtain a firm grounding in the statistical theory of multilevel modeling
as it is employed in the social and behavioral sciences. More specifically, students will:
 gain an understanding of the central statistical concepts underlying the methods (e.g., intraclass
correlations, random slopes, random coefficients, variance components);
 learn about a variety of multilevel models (e.g., random effects ANOVA, compositional effect
models, slopes-as-outcomes models) as well as unique applications of the models (e.g.,
longitudinal analysis);
 and gain experience in estimating multilevel models with several software packages, interpreting
results, and drawing meaningful substantive conclusions.
This course assumes knowledge of estimation in a multiple regression context (intercept and slope
coefficients, standard errors of these parameter estimates, proportion of variance reduction) as well as
a conceptual understanding of multivariate matrices and analyses.
This course will require the use of SPSS and will introduce students to HLM and Mplus (using the free,
limited versions of both).
In general, the course will proceed through topics in multilevel modeling basics and estimation and will
then address more advanced applications and issues (see the calendar for specific topics).
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Course Assessment
Homeworks:
There will be five homework projects, each designed to give you a chance to apply and practice the
concepts learned in class:
 HW 1: Basic terminology; comparing multilevel and single-level estimates
 HW 2: Fully specified multilevel model; Evaluation of centering decisions
 HW 3: Comparison of estimates from ML, REML; EB estimates effects
 HW 4: Diagnostics assessment; model assumptions
 HW 5: Multilevel logistic regression
Feel free to consult each other when working on the projects, but the written homework should be done
individually. Assignments should be neat, and free from spelling, grammar and punctuation errors. Also,
you should always keep a photocopy or electronic copy of your work for your own protection.
Assignments are due as specified in class, and should be submitted on time for full earned credit. Late
work will be accepted for full earned credit IF AND ONLY IF arrangements are made with me at least 72
hours PRIOR TO THE DUE DATE. Otherwise, 5% of the points possible will be deducted for each
weekday the assignment is late. Homework may be turned in at class or via e-mail by 4:15pm.
Quizzes:
To encourage you to keep up with the readings and lecture notes, every other week we will have a short
15 (or so) minute quiz. You may bring one sheet of notes to use during the quiz (double sided).
Poster presentation and review:
On the last day of class, you will each present a poster on a research paper of your choosing. You will be
split into two groups and will present/converse about your poster for an hour. You will also review/ask
questions about others’ posters. I will visit each poster during that hour, and take a handout from each
poster. You should choose one of the following topics for your poster:




Analysis of your own or mentor’s data (assuming IRB approval)
Attempted replication of another author’s study (assuming public data)
A review of an advanced topic in multilevel analysis
A proposal for a methodological study in the area of multilevel modeling
You should each plan to meet with me at least twice about your poster as you proceed in your work. More
details about the timeline for meetings, what should be contained on your poster, and the rubric for
assessing your poster presentation will be provided later in the semester.
Final (take home) Exam:
There will be a comprehensive take-home exam assigned on the last day of classes; it will be due on or
before 4:15pm Monday, May 19th. The exam will consist of questions and activities that are fairly
practical in nature – you will need to apply your knowledge as a researcher. Students are on their honor
to prepare answers to this exam independently. You will not need access to multilevel software during
this week (any output required for the exam will be provided to you).
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Course Grades
Your grades on homeworks, quizzes, the poster presentation, and your final exam will be averaged
according to the percentages shown. Final grades will then be assigned based on the scale below:
assessment
weight
Homeworks
7% each (35% total)
Quizzes
4% each (20% total)
Poster presentation/reviews
15%
Total final exam points converted to a percentage
30%
overall course percent
98.0% - 100%
92.0% - 97.9%
90.0% - 91.9%
88.0% - 89.9%
82.0% - 87.9%
80.0% - 81.9%
70.0% - 79.9%
60.0% - 69.9%
below 60%
grade
A
A
AB+
B
BC
D
F
Grades of “Incomplete:”
Unless the student can demonstrate that near catastrophic events have led to a case of extreme hardship,
grades of “Incomplete” will not be given.
Academic Accommodations
In compliance with and in the spirit of the Americans with Disabilities Act (ADA), I want to work with you if you
have a documented disability that is relevant to successfully completing your work in this course. If you need
academic accommodation by virtue of a documented disability, please contact me as soon as possible to discuss
your needs. Students with documented needs for such accommodations must meet the same achievement
standards required of all other students, although the exact way in which achievement is demonstrated may be
altered. All requests for academic accommodations should be made as early as possible in the semester. For
further information concerning disability accommodations, please contact Dr. Jo Ann Hutchinson at the Disability
Support Service – (301) 314-7682.
Academic Integrity
The University of Maryland, College Park, has a nationally recognized “Code of Academic Integrity,”
administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all
undergraduate and graduate students. As a student you are responsible to uphold these standards for this course. It
is imperative that you are aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For
more information on the code of Academic Integrity or the Student Honor Council, please visit 
http://www.studenthonorcouncil.umd.edu/code.html for details.
On plagiarism -- It is important that the student synthesize pertinent information from the readings and
class lectures when writing up homework assignments. Synthesis does not occur when large blocks of
text are copied from the textbook or my notes and used to answer questions. It is understood that the
student will have to use some verbatim phrases and definitions from the textbook or notes. This is not
considered a case of scholastic misconduct. What must be avoided is extensive verbatim copying of
information from the textbook or my notes when answering the longer questions on the assignments.
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Evaluation of the course
At the end of the semester, a formal evaluative questionnaire regarding both the curriculum and my
instruction will be administered. The information gathered from this process will be used to improve
future courses and instruction. Additionally, students are strongly encouraged to provide feedback to me
during the semester either in person or anonymously – I am here to teach you and want to do it well!
Tentative Reading List
Enders, C. K., &Tofigi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A
new look at an old issue. Psychological Methods, 12, 121-138.
McCoach, D. B. (2010) Hierarchical linear modeling. In Hancock, G. R. & Mueller, R. O. (Eds.) The
Reviewer’s Guide to Quantitative Methods in the Social Sciences, pp. 123-140. New York, NY:
Routledge.
Spybrook, J. (2008). Power, sample size, and design. In O’Connell, A. A. & McCoach, D. B. (Eds.)
Multilevel Modeling of Educational Data, pp. 273-311. Charlotte, NC: Information Age Publishing.
Snijders, T.A.B., & Bosker, R. J. (2012). Multilevel Analysis, 2nd edition, pp. 6-13. London, England:
Sage Publications.
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Course Topic and Reading Calendar (note: some short readings may be added and this calendar is VERY
tentative!)
Date
January 27
February 3
February 10
February 17
February 24
March 3
March 10
March 17
March 24
March 31
April 7
April 14
April 21
April 28
May 5
May 12
May 19
Topic
Introductions
Why multilevel? Basic terminology.
Introduce ELS dataset
Select-a-school exercise in computer lab
Select a school; Cover all models; Present
output from using SPSS
Quiz 1
Walk through Chapter 4-type examples,
measures of reliability, plausible values;
Estimates of random effects. Introduce HLM
software
Discuss model building and centering,
proportion of variance explained, compositional
effects
HW 1 due
Quiz 2
Effect sizes; Estimation and hypothesis testing
More estimation
Quiz 3
Regression diagnostics; assumption checking
Spring Break
Regression diagnostics, continued
Hierarchical generalized linear models
HW 3 due
Quiz 4
Hierarchical generalized linear models,
continued (including IRT)
Power and sample size determination;
HW 4 due
Quiz 5
value added modeling; cross-classified models
Longitudinal modeling
HW 5 due
complex sampling issues; model-based vs.
design-based modeling
Missing data; plausible values
Poster Presentations
Final exam handed out
Final exam due, 4:15pm
5
Reading (by this date)
Snijders & Bosker (2012);
McCoach (2010)
R&B Ch 1, 2
R&B Ch 4
R&B Ch 5; Enders&Tofigi
(2007)
R&B Ch 3
R&B Ch 3
R&B Ch 9
R&B Ch 9, R&B Ch 10
R&B Ch 10, R&B pp. 365-372;
possible IRT reading
Spybrook (2008); Optimal Design
software documentation; R&B Ch
12
possible value-added reading;
R&B Ch 6
Reading to be added
Reading to be added