KENNESAW STATE UNIVERSITY
GRADUATE COURSE PROPOSAL OR REVISION,
Cover Sheet (03/24/2010)
Course Number/Program Name Stat 8225/Master of Science in Applied Statistics
Department Mathematics and Statistics
Degree Title (if applicable) Master of Science in Applied Statistics
Proposed Effective Date August 1, 2011
Check one or more of the following and complete the appropriate sections:
X New Course Proposal
Course Title Change
Course Number Change
Course Credit Change
Course Prerequisite Change
Course Description Change
Sections to be Completed
II, III, IV, V, VII
I, II, III
I, II, III
I, II, III
I, II, III
I, II, III
Notes:
If proposed changes to an existing course are substantial (credit hours, title, and description), a new course with a
new number should be proposed.
A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as part of a new
program. Current catalog information (Section I) is required for each existing course incorporated into the program.
Minor changes to a course can use the simplified E-Z Course Change Form.
Submitted by:
Faculty Member
Approved
_____
Date
Not Approved
Department Curriculum Committee Date
Approved
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Approved
Approved
Approved
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Department Chair
Date
School Curriculum Committee
Date
School Dean
Date
GPCC Chair
Date
Dean, Graduate College
Date
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Vice President for Academic Affairs Date
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President
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KENNESAW STATE UNIVERSITY
GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE
I.
Current Information (Fill in for changes)
Page Number in Current Catalog
Course Prefix and Number
Course Title
Credit Hours
Prerequisites
Description (or Current Degree Requirements)
II.
Proposed Information (Fill in for changes and new courses)
Course Prefix and Number __Stat 8225____________________
Course Title _Applied Longitudinal Data Analysis____________
Credit Hours 3
Prerequisites Stat 8210: Applied Regression Analysis
Description (or Proposed Degree Requirements)
This course introduces students to methods of longitudinal data analysis and issues involved with
the analysis of repeated measures data. The course will be based on multilevel models (also
referred to as hierarchical models, mixed effects models, and random coefficient models) with a
major emphasis on modeling intraindividual effects as a precursor to modeling interindividual
effects. Students will learn how to choose an appropriate model so that specific research
questions of interest can be addressed in a methodologically sound way.
III.
Justification:
In STAT 7100: Statistical Methods, the focus is predominantly on univariate analysis; students
are taught to analyze the data one variable at a time using the tools of descriptive and inferential
statistics. In STAT 8210: Applied Linear Regression Analysis and in succeeding courses,
students learn to analyze the aggregate effect of several independent variables on a continuous or
dichotomous response. Such a multivariable regression approach gives students the flexibility to
investigate, for example, confounding and interaction effects. Unfortunately, all these courses
primarily deal only with cross-sectional data. Increasingly, contemporary research involves
longitudinal data; often the more interesting scientific questions are concerned with how rapidly
things grow or develop over time. For instance, the cross-sectional research of Brown, et al.
(1998) found that gestational age moderated the effects of cocaine exposure among newly born
children. A more interesting question to ask is whether the effect of cocaine use was sustained
for a certain period of time. This question was addressed in Espy, Francis, and Riese (2000): not
only did they observe that cocaine-exposed infants have slower rates of growth, they also found
that the effect of exposure was greater the later the infant was delivered. A major focus of this
proposed course is on growth models, as exemplified in the work of Espy, Francis and Riese. In
short, the new course, STAT 8225: Applied Longitudinal Data Analysis, hopes to reveal research
opportunities unavailable in the world of cross-sectional data.
Answering questions that involve time and change requires the application of statistically
innovative methods for analyzing longitudinal data. During the past two decades, there have
been major improvements in these methods. The goal of STAT 8225 is to introduce individual
growth modeling to students who have been introduced to linear and logistic regression analysis.
The emphasis is not on the theoretical development of the methods but on the appropriate
application of the methods and correct interpretation of the results.
IV.
Additional Information (for New Courses only)
Instructor: Dr. José N. G. Binongo
Text: Judith D. Singer & John B. Willett,
University Press, 2003.
Applied Longitudinal Data Analysis, Oxford
Prerequisites: Stat 8210 – Applied Regression Analysis
Objectives: Upon successful completion of the course students should be able to:
1:
a.
b.
c.
d.
e.
Fit multilevel models for change to data
Compare models using deviance statistics
Use Wald statistics to test composite hypotheses about fixed effects
Evaluate the tenability of a model's assumptions
Use empirical Bayes estimates of the individual growth parameters
a.
b.
c.
d.
Fit models with variably spaced measurement occasions
Fit models with varying numbers of measurement occasions
Incorporate time-varying (as opposed to time-invariant) predictors
Recenter the effects of time
2:
3.
a. Model discontinuous individual change
b. Use transformations to model nonlinear individual change
c. Represent individual change using a polynomial function of time
d. Analyze truly nonlinear trajectories
4.
Model the covariance structure
Instructional Method
Class time will be divided between regular lecture with explanation of theory and
methodology and discussion of readings and case studies and discussion of some of the
problems in the homework assignments.
Method of Evaluation
Written Assignments: There will be weekly quizzes, bimonthly data analysis homework, a
midterm exam, final exam, and a final project. The midterm and final exams have an in-class
component and a take-home component.
Project: One of the objectives of this course is to give students the opportunity to acquire the
skills necessary for the appropriate statistical analysis of real longitudinal data -- the kind they
may use later in the workplace. The skills learned in this course are best synthesized when
students get their hands on a real dataset. They will thus be asked to do an end-of-semester
project.
Students will analyze a longitudinal data set from beginning to end: from deciding on questions
and hypotheses, to deciding on the data manipulation techniques and statistical procedures
needed to do the analyses, to running the procedures, to interpreting the results, to writing the
research report. Students may use their own data set (if one is available to them, if it is
longitudinal, with at least three time points and if it is approved by the instructor) or one
provided by the instructor.
Grading: This will be calculated based on student performance and learning in the class.
Homework:
Quizzes:
Midterm:
Final Exam:
Project:
15%
20%
20%
25%
20%
Grade in the course will be assigned according to the following scale:
90% – 100 %
80% – 89%
70% – 79%
60% – 69%
0% – 59%
V.
A
B
C
D
F
Resources and Funding Required (New Courses only)
Resource
Amount
Faculty
Other Personnel
Equipment
Supplies
Travel
None
None
None
None
None
New Books
New Journals
Other (Specify)
None
None
None
TOTAL
None
Funding Required Beyond
Normal Departmental Growth
None
VI. COURSE MASTER FORM
This form will be completed by the requesting department and will be sent to the Office of the
Registrar once the course has been approved by the Office of the President.
The form is required for all new courses.
DISCIPLINE
COURSE NUMBER
COURSE TITLE FOR LABEL
(Note: Limit 16 spaces)
CLASS-LAB-CREDIT HOURS
Approval, Effective Term
Grades Allowed (Regular or S/U)
If course used to satisfy CPC, what areas?
Learning Support Programs courses which are
required as prerequisites
Statistics
Stat 8225
Applied Longitudinal Data Analysis
3-0-3
Fall 2011
Regular
NA
NA
APPROVED:
________________________________________________
Vice President for Academic Affairs or Designee __
VII Attach Syllabus
Text: Judith D. Singer & John B. Willett, Applied Longitudinal Data Analysis, Oxford
University Press, 2003. Other references include:
-
Robert E. Weiss, Modeling Longitudinal Data, Springer, 2005.
-
Brady West, Kathleen B. Welch & Andrzej T. Galecki, Linear Mixed Models, Chapman & Hall/CRC,
2006.
-
Helen Brown & Robin Prescott, Applied Mixed Models in Medicine (2nd ed) John Wiley & Sons, 2006.
-
Geert Verbeke & Geert Molenberghs, Linear Mixed Models for Longitudinal Data, Springer-Verlag, 2000.
-
N. W. Galwey, Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance, John
Wiley & Sons, 2006.
-
Ramon C. Littell, George A. Milliken, Walter W. Stroup & Russell D. Wolfinger, SAS for Mixed Models
(2nd ed), SAS Institute, 2006.
Instructor: Dr. José N. G. Binongo
Office: Willingham 105
Phone: 404 323 9822
E-Mail: jbinongo@kennesaw.edu
Web Page: http://math.kennesaw.edu/~jbinongo
Software: Students must be able to program in SAS. In this course, students will add to their
repertoire of computing skills SAS procedures appropriate for the analysis of longitudinal data.
Additionally, they will learn particular programming techniques for both data manipulation and
data presentation not taught in the previous courses. Without a statistical package like SAS,
analyzing longitudinal data is next to impossible.
Written Assignments: There will be weekly quizzes, bimonthly data analysis homework, a
midterm exam, final exam, and a final project. The midterm and final exams have an in-class
component and a take-home component.
Project: One of the objectives of this course is to give students the opportunity to acquire the
skills necessary for the appropriate statistical analysis of real longitudinal data -- the kind they
may use later in the workplace. The skills learned in this course are best synthesized when
students get their hands on a real dataset. They will thus be asked to do an end-of-semester
project.
Students will analyze a longitudinal data set from beginning to end: from deciding on questions
and hypotheses, to deciding on the data manipulation techniques and statistical procedures
needed to do the analyses, to running the procedures, to interpreting the results, to writing the
research report. Students may use their own data set (if one is available to them, if it is
longitudinal, with at least three time points and if it is approved by the instructor) or one
provided by the instructor.
The project will be divided into the following meaningful chunks and turned in along the way as
follows:
1. Phase I: Definition/approval of the data set, variables, study design, and area of study. A
fairly-detailed, codebook-like description of all variables in the data set and the nature of
the variables, plus a brief description of the study, its design, goals, and background.
2. Phase II: Conceptualization of change, research questions, hypotheses, and data analysis
plan. A list of the main research questions, the main hypotheses, and a very specific
description of how students are going to go about testing the hypotheses.
3. Phase III: Exploratory data analysis. Annotated output from students’ exploratory and
preliminary analysis procedures, including data cleaning, transformations, data reduction,
and recoding procedures if any. This also includes a description of how the results of the
exploratory data analysis affect the data analysis strategy.
4. Phase IV: Final report. An research report of the results of the analyses, complete with a
brief introduction to the topic, a brief method and procedures section, an expanded results
section (in which students describe what was done, and why, and what was found), and a
brief discussion section.
5. Phase V: Oral presentation. Students will give an oral presentation briefly summarizing
their course project.
Grading: This will be calculated based on student performance and learning in the class.
Homework:
Quizzes:
Midterm:
Final Exam:
Project:
15%
20%
20%
25%
20%
Grade in the course will be assigned according to the following scale:
90% – 100 %
80% – 89%
70% – 79%
60% – 69%
0% – 59%
A
B
C
D
F
Course Topic Outline
This course is focused on continuous outcomes. It starts with a review of linear regression and
analysis of variance. After this brief warm-up, we look at the type of exploratory analyses that
can be used with individual growth modeling when the longitudinal data have been formatted in
what has come to be known as the person-period dataset. We will then introduce and discuss
simple multilevel models appropriate for representing processes within these data. Finally, we
will extend the methods to more complex analytic situations that involve curvilinear and
discontinuous growth trajectories and complex risk profiles, the inclusion of time-varying
covariates, and the testing of complex interactions among time-invariant and time-varying
predictors.
1. Introduction/Review - Binongo's handouts
a. Recentering predictors
b. Interpreting interactions
c. Logarithmically transforming the response variable (and calculating and comparing
geometric means)
d. Performing two-way ANOVA
e. Analysis of covariance (ANCOVA)
f. Introducing random (as opposed to fixed) effects and mixed models
2. Exploratory analysis of longitudinal data - Chapters 1 & 2
a. Creating a longitudinal data set
b. Analyzing descriptively individual change over time
c. Exploring differences in change across subjects
d. Improving the precision and reliability of ordinary least squares estimated rates of
change
3. Multilevel models for change - Chapters 3 & 4
f. Fitting the multilevel model for change to data
g. Comparing models using deviance statistics
h. Using Wald statistics to test composite hypotheses about fixed effects
i. Evaluating the tenability of a model's assumptions
j. Empirical Bayes estimates of the individual growth parameters
4. How to treat time more flexibly - Chapter 5
e. Dealing with variably spaced measurement occasions
f. Dealing with varying numbers of measurement occasions
g. Incorporating time-varying (as opposed to time-invariant) predictors
h. Recentering the effects of time
5. Discontinuous and nonlinear change - Chapter 6
a. Studying discontinuous individual change
b. Using transformations to model nonlinear individual change
c. Representing individual change using a polynomial function of time
d. Analyzing truly nonlinear trajectories
6. Modeling the covariance structure - Chapter 7