The University of North Carolina at Chapel Hill
School of Social Work
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring Semester, 2015
INSTRUCTOR
Roderick Rose Ph.D.
Room 245D
Tate Turner Kuralt, CB #3550
School of Social Work, Chapel Hill, NC 27599-3550
Phone: (919) 260-0542
Email: rarose@email.unc.edu
TEACHING ASSISTANT
Vivien Li
Room 245A
wenli@email.unc.edu
CLASS MEETING TIMES & OFFICE HOURS
Class meets on Wednesdays 9:00-11:50 am (Room 102 TTK)
Office hours are Tuesday 10-noon with Dr. Rose and Wednesday 12-1 with Vivien Li
Course Description:
This course introduces statistical frameworks, analytical tools, and social behavioral
applications of OLS regression model, weighted least-square regression, logistic
regression models, and generalized linear models.
Course Objectives:
At the completion of the course, students will be able to:
1. Understand the type and nature of research questions and data that are suitable for
regression analysis;
2. Use Stata computing software package to manage and analyze data with the OLS
regression model;
3. Understand the Gauss-Markov theorem and the BLUE property of OLS, especially
conditions under which BLUE does not hold;
4. Have a solid understanding of the five assumptions embedded in the OLS regression;
5. Know how to conduct statistical tests detecting violations of OLS assumptions (i.e.,
multicollinearity, heteroskedasticity, influential data and outliers, etc.);
6. Know how to take remedial measures if harmful violations exist (i.e., weighted leastsquares regression, etc.);
7. Understand the type and nature of research questions and data that are suitable for the
generalized linear models;
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8. Have a solid understanding of basic concepts of categorical data (i.e., odds ratio, relative
risk, marginal probability, and conditional probability);
9. Use Stata computing software package to manage and analyze data with the binary,
ordered, and multinomial logistic regressions;
10. Know how to interpret results of regression analysis and logistic regression analysis, and
communicate findings to general audiences clearly and effectively in writing;
11. Understand limitations of the regression and logistic regression models, and common
pitfalls in using these models;
12. Understand the basics of conducting a Monte Carlo study.
Pre-requirement:
Students are assumed to be familiar with descriptive and inferential statistics. They
should have statistical and statistical software background at least equivalent to that
provided by SOWO 911. Students without such prerequisites should contact the
instructor to determine their eligibility to take the course.
Statistical Software Package:
This course will use Stata as the main software package. However, you may also use
SAS.
Required Textbook:
Kutner, M. H., Nachtsheim, C.J., and Neter, J. (2004). Applied Linear Regression Models, 4th
Edition, McGraw Hills/Irwin.
Required Articles for Reading
All required journal articles are available on E-journals. Required book chapters will be
distributed in class.
Recommended Textbooks:
Kennedy, P. (2003). A Guide to Econometrics, 5th ed. Cambridge, MA: The MIT Press.
Berk, R.A. (2004). Regression Analysis: A Constructive Critique, Thousand Oaks, CA: Sage
Publications
Assignments
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Class Project/Participation
Midterm Exam
Final Exam
Grade Percentage
8%
8%
8%
8%
8%
10%
20%
30%
GRADING SYSTEM
The standard School of Social Work interpretation of grades and numerical scores will be used.
H = 94-100
P = 80-93
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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.
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.
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SESSION SCHEDULE
All sessions meet in Tate-Turner-Kuralt Room 102 except as noted.
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1/7
-SSWR Wed 1/14, no class2
1/21
3
1/28
4
2/4
5
2/11
6
2/18 by Vivien Li
7
2/25
8
3/4
-Spring break on 3/11, no class9
3/18
10
3/25
11
4/1 by Vivien Li
12
4/8
13
4/15
14
4/22
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COURSE OUTLINE (TOPICS, READINGS, AND ASSIGNMENTS)
1
Introduction and course overview
Course overview
Project: Orientation to Stata or SAS on the VCL
Review of important concepts:
Association versus causation
Univariate, bivariate, and multivariate analyses
Statistical properties
Summation operator 
No readings due for this session.
Readings and practice to be completed after this session:
 Neter et al: 1.1-1.2
 Guo, S. (2008). Quantitative research. An entry of the Encyclopedia of
Social Work, 20th Edition. New York, NY: The Oxford University Press.
2
Simple linear regression
Optimization: minimization versus maximization
Least-square estimator
Interval estimation and hypothesis testing
Readings to be completed for this session:
 Kutner et al: 1.3-1.7, Chapters 2 & 4
Seminar reading to be completed for this session:
 Holland, Paul W. (1986) Statistics and causal inference. Journal of the
American Statistical Association, 81: 945-960. doi:10.2307/2289064
Assignment 1 (due in session 4). In this Assignment, you will solve problems
pertaining to simple linear regression and basic matrix algebra.
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Matrix algebra
Matrices, vectors, and scalars
Matrix operations
Readings to be completed for this session:
 Kutner et al: 5.1-5.9
Seminar reading to be completed for this session:
 Kenney, G. M., Ruhter, J., & Selden, T. M. (2009). Containing costs and
improving care for children in Medicaid and CHIP. Health Affairs, w1026w1036.
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4
Multiple linear regression
Model specifications
Estimation of regression coefficients
Inferences concerning 
Readings to be completed for this session:
 Kutner et al: Chapter 6 (the first half)
Seminar reading to be completed for this session:
 Haveman, R., Holden, K., Romanv, A., & Wolfe, B. (2007). Assessing the
maintenance of savings sufficiency over the first decade of retirement.
International Tax and Public Finance 14, 481-502.
Assignment 1 due today
Assignment 2 out (Due in session 6):In this Assignment, you will solve problems
pertaining to multiple linear regression models.
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ANOVA and R2
Review of variance, covariance and correlation
Decomposition of total sum of squares, F test
R2 and adjusted R2
Readings to be completed for this session:
 Kutner et al: Chapter 6 (the second half)
 Guo, S. (1996). “Determinants of fertility decline in urban China”, in Alice
Goldstein and Feng Wang (Eds.): China, Many Facets in Demographic
Change, Westview Press.
Look ahead to assignment 3. Students should find the article they will use to
complete assignment 3 by session 6.
Seminar reading to be completed for this session:
 Bowen, Powers & Rose, 2005 (in Sakai folder)
6
The Five Assumptions of OLS
BLUE criterion and properties of OLS
Maximum likelihood estimator
The five OLS assumptions
Readings to be completed for this session:
 Kutner et al: 1.8
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
Kennedy, P. (2003). A Guide to Econometrics, 5th Edition, Chapter 3,
Cambridge, MA: The MIT Press. (available on Sakai)
No seminar reading.
Assignment 2 due today
Assignment 3 out (Due in session 7). You will find an article that uses multiple
linear regression and you will do a brief 10 minute presentation. In this
presentation, you will critique how the authors address the 5 assumptions of OLS.
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The Five Assumptions & Power
Project: a Monte Carlo study showing the consequences for OLS under various data
generation conditions.
Power analysis
Students will present their critiques (assignment 3).
Assignment 3 due today
Readings to be completed for this session:
 Kutner et al: Chapter 3
 Rose & Bowen. Available on Sakai.
 Guo, S., & Hussey, D.L. (2004). Nonprobability sampling in social work
research: Dilemmas, consequences, and strategies. Journal of Social
Service Research, 30(3): 1-18.
No seminar reading.
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Violating OLS Assumptions and Remedial Measures – I
Specification errors and selection of Predictors
Influential data and outliers
Multicollinearity
Readings to be completed for this session:
 Kutner et al: Chapters 9 and 10
 Lum & Chang, 1998
 Guo, response to Lum & Chang
Seminar reading
 Parish, S. L., Rose, R. A., & Swaine, J. G. (2012). (in Sakai folder)
Midterm exam out (DUE DATE PROVIDED BY INSTRUCTOR):
Use data sets provided by the course or data set you choose to run a multiple linear
regression model. Write a paper (no more than 12 pages, double spaced) to present
findings. The paper should include: (1) research questions and data that are suitable
to a regression analysis; (2) methods and specification of the regression model; (3)
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diagnostics detecting at least two problems pertaining to violation of regression
assumptions and discussion of remedial measures; (4) interpretation of findings;
and (5) presentation of findings that answers research questions effectively and
efficiently.
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9. Violating OLS Assumptions and Remedial Measures – II
Heteroskedasticity
Weighted Least Squares
Readings to be completed for this session:
 Kutner et al: Chapter 11
 Gujarati, D. N. (1995). Basic Econometrics, Third Edition, McGraw-Hill,
pp. 365-389.
No seminar reading.
Assignment 4 out today (due session 11): In this Assignment, you are required to
solve problems pertaining to interpretation of standardized regression coefficients,
diagnostics of heteroskedasticity, performing partial F test, and testing interactions.
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Other Topics in Regression Analysis -- I
Regression through origin
Partial-correlation coefficient
Standardized regression coefficient
Functional form, curvilinear relationship and polynomial regression models
R2 increment: Hierarchical regression analysis
Readings to be completed for this session:

Kutner et al: Chapter 7

http://andrewgelman.com/2014/06/02/hate-stepwise-regression/

http://www.danielezrajohnson.com/stepwise.pdf

White, M., Biddlecom, A. and Guo, S. (1993) “Immigration,
naturalization and residential assimilation among Asian Americans in
1980s”, Social Forces,72(1): 93-118.
Seminar reading to be completed for this session:
 No seminar reading this week; read the Gelman and Johnson stepwise
essays/comments (shown above) and be prepared to talk about them.
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Other Topics in Regression Analysis -- I
Dummy variables as predictors:
Interpretation of regression coefficients
Comparison of several regression equations
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Indirect effects: mediation, suppression and confounding
Testing interactions:
Mediator versus moderator
Interaction, joint effect and moderator
ANCOVA
Experimental design
Concomitant variables
Readings to be completed for this session:

Kutner et al: Chapter 8
Seminar reading to be completed for this session:
 Rose, Parish et al. (in Sakai folder)
Assignment 4, due today.
Assignment 5 out today (due in session 13): In this Assignment, you are required:
(a) to perform maximum likelihood estimation using Excel; (b) to solve problems
pertaining to interpretation of odds ratio and running binary logistic regression; and
(c) do a group exercise on critical review of a study using regression.
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Logistic Regression Analysis - I
Violations of OLS assumption when dependent variable is dichotomous
Logistic response function
Maximum likelihood estimator
Readings to be completed for this session:

Kutner et al: Chapter 13
Seminar reading to be completed for this session:
 Parish, S. L., Rose, R. A., & Andrews, M. E. (2009). (in Sakai folder)
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Logistic regression analysis - II
Odds ratio
Relative risk
Predicted probability
Readings to be completed for this session:

Kutner et al: Chapter 14

Agresti, A. (1990). Categorical Data Analysis, pp. 13-19. Hoboken, NJ:
John Wiley & Sons, Inc.
No seminar reading
(Hand out Final)
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Final Exam (DUE DATE PROVIDED BY INSTRUCTOR): DESCRIBE
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Other topics
Multinomial logistic regression
Ordinal logistic regression
Overview of the generalized linear models
Common pitfalls in conducting and reporting regression analysis
Missing data
Readings to be completed for this session:

Rose & Fraser 2008 (on Sakai)
No seminar reading
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