The University of North Carolina at Chapel Hill
School of Social Work
SOWO 917 Longitudinal and Multilevel Analysis
Fall Semester, 2014
INSTRUCTOR
Roderick Rose Ph.D.
Room 245D
Tate Turner Kuralt, CB #3550
School of Social Work, Chapel Hill, NC 27599-3550
Phone: (919) 260-052
Email: rarose@email.unc.edu
CLASS MEETING TIMES & OFFICE HOURS
Class meets on Wednesdays 9:00-11:50 am (Room 135 TTK)
Office hours are Wednesday 12-1 and Thursday 10-2
COURSE DESCRIPTION
This course introduces the context and intuition for longitudinal and multilevel models,
and the statistical frameworks, analytical tools, and social behavioral applications of three
types of models: event history analysis (EHA), multilevel modeling (MLM), and growth
curve analysis.
COURSE OBJECTIVES
At the completion of the course, students will have a solid understanding of the
challenges and problems in longitudinal and multilevel analysis. They will know how to
choose appropriate statistical analyses that best suit the type of data and research
questions for a given study. They are expected to be able to conceptualize, design, run,
interpret, and communicate results clearly and effectively in spoken and written settings
based on multilevel modeling (including two-level and three-level hierarchical linear
models, growth curve analysis, categorical MLMs, and understanding cross-classification
and cross-level effects) and event history analysis (life tables, Kaplan-Meier’s estimate of
survivor function, discrete time model, Cox proportional hazard model, marginal models
handling multilevel event data).
PRE-REQUISITES
Students are assumed to be familiar with descriptive and inferential statistics as well as
multiple regression analysis. They should have statistical and statistical software
background at least equivalent to that provided by SOWO918, SOCI209, PSYC282,
EDUC284 (linear regression), or SOCI211 (categorical data analysis). Students without
such prerequisites should contact the instructor to determine their eligibility to take this
course.
1
SAKAI COURSE SITE
Go to: https://www.unc.edu/sakai/
Enter your ONYEN
Navigate to SOWO917.001.FA14
This syllabus is under “syllabus” on the left-hand navigation menu
All class lecture notes, assignments, and other materials as needed will be provided under
“resources” on the left-hand navigation menu
All course materials are on the web site and students are responsible for bringing their
materials to class.
STATISTICAL SOFTWARE PACKAGES
Students may choose to use Stata, SAS, or R as the primary statistical software package
for the course. I will use all three at various times in classroom lectures, materials, and
demonstrations.
TEXTBOOKS
Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and
Data Analysis Methods, Second Edition, Thousand Oaks, CA: Sage Publications
Ltd.
Singer, J.D., & Willett, J.B., (2003). Applied Longitudinal Data Analysis: Modeling
Change and Event Occurrence, New York, NY: Oxford University Press
RECOMMENDED TEXTBOOKS
Allison, P.D. (1995). Survival Analysis Using the SAS System. Cary, NC: SAS Institute
Inc.
Cleves, M.A., Gould, W.W., & Gutierrez, R.G. (2004). An introduction to survival
analysis using Stata, Rev. ed., College Station, TX: Stata Press.
Guo, S. (2010). Survival Analysis: A Practical Guide to Social Work Research. New
York, NY: Oxford University Press.
Rabe-Hesketh, S., & Skrondal, A. (2005). Multilevel and Longitudinal Modeling Using
Stata, College Station, TX: Stata Press.
SAS Online documentation for Proc Mixed:
http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.
htm#mixed_toc.htm
R Manual for MLM
http://cran.r-project.org/doc/contrib/Bliese_Multilevel.pdf
2
ASSIGNMENTS
GRADE PERCENTAGE
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Assignment 7
Term project
Midterm Exam (take home)
Final Exam (take home)
5%
5%
5%
5%
5%
5%
5%
15%
30%
20%
GRADING SYSTEM
The standard School of Social Work interpretation of grades and numerical scores will be used.
H = 94-100
P = 80-93
L = 70-79
F = 69 and below
POLICY ON CLASS ATTENDANCE
Class attendance is an important element of class evaluation, and you are expected to attend all
scheduled sessions. You will easily fall behind the course if you miss a class session it will affect
the class learning project, so it is imperative to attend. Students are responsible for informing the
instructor when they must miss a class session.
POLICY ON INCOMPLETE AND LATE ASSIGNMENTS
Assignments are to be turned in to the professor by 5pm of the due date noted in the course
outline. Brief extensions may be granted by the professor given advance notice of at least 24
hours. Late assignments (not turned in by 5pm on the due date) will be reduced 10 percent for
each day late (including weekend days). A grade of incomplete will only be given under
extenuating circumstances and in accordance with University policy.
POLICY ON ACADEMIC DISHONESTY
Students are expected to follow the UNC Honor Code. Please include the honor code statement
along with your signature on all assignments: “I have neither given nor received unauthorized aid
on this assignment.”
Please refer to the APA Style Guide, the SSW Manual, and the SSW Writing Guide for
information on attribution of quotes, plagiarism and appropriate use of assistance in preparing
assignments.
If reason exists to believe that academic dishonesty has occurred, a referral will be made to the
Office of the Student Attorney General for investigation and further action as required.
3
POLICY ON ACCOMMODATIONS FOR STUDENTS WITH DISABILITIES
Students with disabilities that affect their participation in the course may notify the instructor if
they wish to have special accommodations in instructional format, examination format, etc.,
considered.
SESSION SCHEDULE
All sessions meet in Tate-Turner-Kuralt Room 135 except as noted.
1
8/20
2
8/27
3
9/3
4
9/10 Class will meet in the Tate-Turner-Kuralt telepresence room, 101.
5
9/17
6
9/24
7
10/1
8
10/8
-Fall break on 10/15, no class9
10/22
-Midterm exam due on 10/2310
10/29
11
11/5
12
11/12
13
11/19
-Thanksgiving on 11/26, no class14
12/3
-Final exam due on 12/7-
4
COURSE OUTLINE (TOPICS, READINGS, AND ASSIGNMENTS)
1
Introduction and course overview
Overview and rationale for multilevel models.
Overview and rationale for survival models.
Review of fundamental statistical concepts.
Readings to be completed for this session:
 Guo, S. (2013). Advanced statistical analysis. Entry for the Encyclopedia of
Social Work Online. New York, NY: The Oxford University Press.
Optional Reading:
 Guo, S. (2013). Maximum likelihood estimator: The untold stories, caveats,
and tips for application. Chinese Sociological Review 45(3), 74-101.
Assignment #1 (Due in session 2): In this assignment you will demonstrate your
readiness to use SAS, Stata and R and convert data between them. You
will be asked to do a simple 2 level longitudinal multilevel model in each,
as well as some other random things to get you started on each package.
2
Introduction to multilevel and hierarchical linear modeling
The importance of context to social and behavioral science.
Overview of MLM/HLM.
Multi-level hypotheses in social sciences
Variance decomposition, intra-class correlation & reliability
Random effects & fixed effects
Two-level model
Readings to be completed for this session:
 Raudenbush & Bryk, Chapters 1 and 2
 Singer, J. D. (1998). Using SAS Proc Mixed to fit multilevel models,
hierarchical models, and individual growth models. Journal of Educational
and Behavioral Statistics 23(4), 323-355.
 Hedges, L. V. (2007). Correcting a significance test for clustering. Journal
of Educational and Behavioral Statistics 32(2), 151-179.
Seminar reading to be completed for this session:
 Sampson, R.J., Raudenbush, S.W., & Earls, F. (1997) “Neighborhoods and
violent crime: A multilevel study of collective efficacy.” Science 277(15):
918-924.
Assignment 1 due today
Optional Reading:
 Guo, S. (2005). “Analyzing grouped data with hierarchical linear modeling”,
Children and Youth Services Review 27:637-65.
5
Assignment 2 (due in session 3): For this assignment you will be asked to explain
the meaning of several equations in chapter 3 of Raudenbush & Bryk, 2002.
3
Multilevel models in organizational applications
Two-level model (finish)
Writing out equations and substitution.
Estimation theory.
Organizational designs
Variance explained and presenting results.
Readings to be completed for this session:
 Raudenbush & Bryk, Chapter 5 (99-130) and selections from chapter 3.
o For chapter 3, I am not expecting you to read it from beginning to
end. However, in order to familiarize you with estimation concepts, I
have designed assignment 2 to facilitate learning from this chapter.
 Primo, D., Jacobsmeier, M. L., and Milyo, J. (2007). Estimating the impact
of state policies and institutions with mixed-level data. State Politics and
Policy Quarterly, 7(4), 446-449.
 Snijders, T. A. B., & Bosker, R. J. (1994). Modeled variance in two-level
models. Sociological Methods and Research 22(3), 342-363.
Seminar reading to be completed for this session:
 Hedges, L. V. & Hedberg, E. C. (2007). Intraclass correlations forplanning
group randomized experiments in rural education. Educational Evaluation
and Policy Analysis 29(1), 60-87.
Assignment 2 due today
Assignment 3 (due in session 4): Multilevel models in organizational applications.
Optional readings:
 Raudenbush & Bryk, Finish chapter 3 and read chapter 4.
4
Multilevel models in organizational applications, continued.
Special topics including:
Centering.
Three-level organizational models (intro).
Model fitting and goodness-of-fit indices.
Power.
Prediction of effects for organizations.
Readings to be completed for this session:
 Raudenbush & Bryk, Finish chapter 5
 Rose, R. A., & Bowen, G. L. (2009). Power analysis in social work
intervention research design: Designing cluster randomized trials. Social
Work Research, 33(1), 43–52.
6

Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster
randomized trials. Psychological Methods, 2(2), 173-185.
Seminar reading to be completed for this session:
 Sanders, Saxton & Horn. The Tennessee Value-Added Assessment System.
(Provided on Sakai).
Optional reading:
 Schochet, P. Z. (2005). Statistical power for random assignment evaluations
of education programs. Washington, DC: Mathematica Policy Research.
Assignment 3 due today
5
Individual growth models
Questions related to change
Longitudinal or panel data and specifying time
Random effects vs. fixed effects models
The multilevel model for change; model building
Readings to be completed for this session:
 Singer & Willett, Chapters 1-3
 Raudenbush, S. W. (2001). Comparing personal trajectories and drawing
causal inferences from longitudinal data. Annual Review of Psychology 52,
501-525.
 Raudenbush, SW., & Liu, X. (2000). “Statistical power and optimal design
for multisite randomized trials.” Psychological Methods 5(2): 199-213.
Seminar reading to be completed for this session:
 Smokowski et al (Externalizing) (Under review).
Assignment 4 (due in session 6): Individual growth models.
Optional reading:
 Raudenbush & Bryk, Chapter 6.
6
Individual growth models, continued.
The multilevel model for change; model building (continued)
Flexible time specifications
EBEs of individual growth parameters
Moderators and cross-level interactions
Readings to be completed for this session:
 Singer & Willett, Chapter 4-5.
 Morrell, C. H., Brant, L. J., & Ferrucci, L. (2009). Model choice can
obscure results in longitudinal studies. Journal of Gerontology: Medical
Sciences 64A(2), 215-222.
7
Seminar reading to be completed for this session:
 Akos, P. T., Rose, R. A., & Orthner, D. (2014). Sociodemographic
moderators of middle school transition effects on academic achievement.
The Journal of Early Adolescence, Online first, 1-29.
Optional reading:
 Marsh, H. W. & Hau, K. T. (2002). Multilevel modeling of longitudinal
growth and change: Substantive effects or regression toward the mean
artifacts? Multivariate Behavioral Research 37(2), 245-282.
Assignment 4 due today
Assignment 5 (Due either session 8 or 9): Seminar reading assignment.
7
Advanced MLM: Non-linearities
Non-linear and discontinuous change
Hierarchical generalized linear model (HGLM)
Multilevel models for binary and multinomial outcomes
Multilevel models for count data
Readings to be completed for this session:
 Raudenbush & Bryk, Chapter 10
 Singer & Willett, Chapter 6
Seminar reading to be completed for this session. For this session, each student will
be assigned one of the readings:
 Rose, R. A., Woolley, M. E., Orthner, D. K., Akos, P. T., & Jones-Sanpei,
H. J. (2012). Increasing teacher use of career-relevant instruction: A
randomized control trial of CareerStart. Educational Evaluation and Policy
Analysis 34(3), 295-312.
 Snelgrove, J. W., Pikhart, H., & Stafford, M. (2009). A multilevel analysis
of social capital and self-rated health: Evidence from the British Household
Panel Survey. Social Science & Medicine 68, 1993-2001.
 Parish, S., Thomas, K., Rose, R., Kilany, M., & McConville, R. (2012).
State insurance parity legislation for autism services and family financial
burden. Intellectual and Developmental Disabilities 50(3), 190-198.
8
Advanced MLM: Complex data structures.
Three level models
Cross-classified models
Readings to be completed for this session:
 Raudenbush & Bryk, Chapter 8 and Chapter 12
 Luo, W., & Kwok, O. (2009). The impacts of ignorable a crossed factor in
analyzing cross-classified data. Multivariate Behavioral Research 44, 182212.
8

Grady, M. W., & Beretvas, N. (2010). Incorporating student mobility in
achievement growth modeling: A cross-classified multiple membership
growth curve model. Multivariate Behavioral Research 45, 393-419.
Seminar readings will be shared by students (assignment 5; half of the students
will go today, half in the next session).
9
Advanced MLM: practice and applications.
Diagnostic and model building
Estimation and convergence
Covariance structures
Innovative applications
Readings to be completed for this session:
 Raudenbush & Bryk, Chapter 9
 Singer & Willett, Chapter 7
 Bauer, D. J. & Cai, L. (2009). Consequences of unmodeled nonlinear effects
in multilevel models. Journal of Educational and Behavioral Statistics,
34(1), 97-114.
 Guo, S. & Hussey, D. (1999). Analyzing longitudinal rating data: a threelevel hierarchical linear model. Social Work Research 23(4), 258-269.
Optional readings:
 Guo, S. & Bollen, K. A. (2013). Research using longitudinal ratings
collected by multiple raters: One methodological problem and approaches to
its solution. Social Work Research 37(2), 85-98.
Seminar readings will be shared by students (assignment 5; the remaining half of
the students will go today).
Hand Out Midterm Exam (Due on date noted in session schedule): Use data sets
provided by the course or data set you choose to run a multilevel regression model.
Write a brief paper (no more than 12 pages, double spaced) to present findings. The
paper should include: (1) 2 research questions; (2) data description and specification
of the multilevel regression; (3) description of the process by which the model will
be fitted; (4) a description of model diagnostics and sensitivity tests; and (5) report
and interpret the findings from each of (2)-(3). At least one of your research
questions should imply a cross-level interaction term. You should be able to explain
the findings to a lay audience.
10
Intro to Survival Analysis
Review of binary and multinomial logistic regression
Overview of event history analysis
Censoring
Discrete-time event occurrence
Life tables
Hazard and survival functions/curves
9
Readings to be completed for this session:
 Singer & Willett, Chapters 9-10.
 Yang, T. & Aldrich, H. E. (2012). Out of sight but not out of mind: Why
failure to account for left truncation biases research on failure rates. Journal
of Business Venturing 27, 477-492.
Seminar reading to be completed for this session:
 Berger, M. C. & Black. D. A. (1998). The duration of Medicaid spells: An
analysis using flow and stock samples. The Review of Economics and
Statistics 80(4), 667-675.
Optional readings:
 Guang Guo (1993). “Event history analysis for left-truncated data”,
Sociological Methodology, 23, 217-243.
 Harris, K.M. (1993). “Work and welfare among single mothers in poverty.”
American Journal of Sociology 99: 317-352.
11
Discrete-time models, continued
The discrete time hazard model
Alternate specifications for time
Time-varying covariates
Proportionality and unobserved heterogeneity
Parametric models (Weibull, accelerated failure time, etc.)
Readings to be completed for this session:
 Singer & Willett, Chapters 11-12.
 Nam, Y. (2005). The roles of employment barriers in welfare exits and
reentries after welfare reform: Event history analysis. Social Service Review
79(2), 268-293.
 Haque, M. M. & Washington, S. (2014). A parametric duration model of the
reaction times of drivers distracted by mobile phone conversations. Accident
Analysis and Prevention 62, 42-53.
Seminar reading to be completed for this session:
 Glick, J. E. & Van Hook, J. (2011). Does a house divided stand? Kinship
and the continuity of shared living arrangements. Journal of Marriage and
Family 73, 1149-1164.
Optional readings:
 Lee, E. T. & Go, O. T. (1997). Survival analysis in public health research.
Annual Review of Public Health 18, 105-134.
 Allison, P.D. (1982). “Discrete-time methods for the analysis of event
histories”, Sociological Methodology, 13, 61-98.
 Hetling, A., Ovwigho, P. C., & Born, C. E. (2007). Do welfare avoidance
grants prevent cash assistance? Social Service Review 81(4), 609-631.
Assignment 6, due in session 12.
10
12
Kaplan Meier & Cox proportional hazards model
The clog-log model
Rare event models
Kaplan-Meier’s estimate of survivor functions
The cumulative hazard function and kernel smoothing
Partial likelihood estimator
Cox regression
Readings to be completed for this session:
 Singer & Willett, Chapters 13 and 14 up to page 516.
 Heise, M. (2012). Law and policy entrepreneurs: Empirical evidence on the
expansion of school choice policy. Notre Dame Law Review 87(5), 19171940.
Seminar reading to be completed for this session:
 Kosterman, R., Hawkins, D., Guo, J., Catalano, R. F., & Abbott, R. D.
(2000). The dynamics of alcohol and marijuana initiation: Patterns and
predictors of first use in adolescence. American Journal of Public Health
90(3), 360-366.
Assignment 6 due today
Optional readings:
 Sandefur & Cook, (1998). “Permanent exits from public assistance: The
impact of duration, family, and work”. Social Forces, 77(2) 763-786.
 Guo, S., Biegel, D., Johnson, J. & Dyches, H. (2001) “Assessing the impact
of mobile crisis services on preventing hospitalization: A community-based
evaluation”. Psychiatric Services 52(2):223-228.
 Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal
Statistical Society 2(XX), 187-220.
 Efron, B. (1977). The efficiency of Cox’s likelihood function for censored
dta. Journal of the American Statistical Association 72(359), 557-565.
13
Cox proportional hazards model, continued.
Partial likelihood method
Interpreting results
Alternate structures for time
Non-proportional hazards and interactions with time
Diagnostics
Competing risks
Power analysis for survival models
Introduction to multilevel event time data (multivariate failure time data)
Readings to be completed for this session:
 Singer & Willett, finish chapter 14 and 15.
11


Jozwiak, K. & Moerbeek, M. (2012). Power analysis for trials with discretetime survival endpoints. Journal of Educational and Behavioral Statistics
37(5), 630-654.
MORE
No seminar reading
Optional readings:
 Stata documentation on STPOWER:
http://www.stata.com/manuals13/ststpower.pdf
 Heckman, J.J., & Singer, B. (1985), “Social science duration analysis”, in
Longitudinal Studies of Labor Market Data, New York, NY: Cambridge
University Press. Chapter 2.
 Grilli, L. (2005). The random effects proportional hazards model with
grouped survival data: A comparison between the group continuous and
continuation ratio versions. Journal of the Royal Statistical Society Series A,
168(1), 83-94.
 Guo, S., & Wells, K. (2003). Research on timing of foster-care outcomes:
one methodological problem and approaches to its solution. Social Service
Review 77(1): 1-24.
 Lin, D.Y. (1994). Cox regression analysis of multivariate failure time data:
The marginal approach. Statistics in Medicine 13: 2233-2247.
 Trussell, J., & Richards, T. (1985). “Correcting for unmeasured
heterogeneity in hazard models using the Heckman-Singer procedure.”
Sociological Methodology 15: 242-276.
Assignment 7, Due before Session 14: Revisit one article from any assigned
seminar reading to perform a critical review. This review (no more than two pages,
single-spaced) should focus on: (1) strengths and limitations (very briefly), (2)
major statistical problems, and (3) recommendations for revisions. You will present
your review in class during session 14.
(Hand out Final)
Final Exam (Due on date noted in session schedule): Use data sets provided by
the course or data set you choose to run an event history/survival regression model
(any type). Write a brief paper (no more than 12 pages, double spaced) to present
findings. The paper should include: (1) 2 research questions; (2) data description
and specification of the survival regression; (3) description of the process by which
the model will be fitted; (4) a description of model diagnostics and sensitivity tests;
and (5) report and interpret the findings from each of (2)-(3). You should be able to
explain the findings to a lay audience.
14
Presentation of assignment 7 article reviews, no readings or other assignment.
12