The University of North Carolina at Chapel Hill
School of Social Work
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring Semester, 2013
Instructor
Shenyang Guo, Ph.D.,
Room 524j, Tate Turner Kuralt Building
CB #3550,
School of Social Work, Chapel Hill, NC 27599-3550
Phone: (919) 843-2455
Email: sguo@email.unc.edu
Class Meeting Times & Office Hours
Class meets on Wednesdays 9:00-11:50 am (Room 135 TTK)
Office hours are Tuesdays 8:30 – 10:30 (Room 524j TTK)
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;
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
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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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.
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
Grade Percentage
Assignment 1
8%
Assignment 2
8%
Assignment 3
8%
Assignment 4
8%
Assignment 5
8%
Midterm Exam (take home)
20%
Final Exam Part I (In-class open-book exam)
20%
Final Exam Part II (take home)
20%
Grading System
The standard of School of Social Work’s interpretation of grades and numerical scores will be
used.
H = 94-100
P = 80-93
L = 70-79
F = 69 and below
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. Extensions may be granted by the professor given advance notice of at least 24 hours.
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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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 Class Attendance
No official record is taken to track class attendance. However, students are expected to attend all
class sessions. Missing one class will result in big loss in learning, and missing two sessions will
make the understanding of course materials almost impossible.
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 which affect their participation in the course may notify the instructor if
they wish to have special accommodations in instructional format, examination format, etc.,
considered.
Course Outline (Topics, readings, and assignments):
Jan. 9
1. Introduction and Review
Course overview
Review of important concepts:
Association versus causation
Univariate, bivariate, and multivariate analyses
Statistical properties
Causal inferences
Summation operator 
Readings:
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.
Jan. 23
2. Simple Linear Regression
Optimization: minimization versus maximization
Least-square estimator
Interval estimation and hypothesis testing (t-test)
Review of Stata basics
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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Readings:
Kutner et al: 1.3-1.7, Chapters 2 & 4
Jan. 30
3. Basic Matrix Algebra
Why matrix?
Matrix operations
Example: solve for b=(X’X)-1(X’Y)
Stata Lab 1: Stata basics and running regression
Readings:
Kutner et al: 5.1-5.9
Assignment 1 out (Due: 2-6-13):
In this Assignment, you will solve problems pertaining to simple linear regression and
basic matrix algebra. You will run Stata to solve some of the problems.
Feb.6
4. Multiple Linear Regression
Model specifications
Estimation of regression coefficients
Inferences concerning 
Readings:
Kutner et al: Chapter 6 (the first half)
Assignment 2 out (Due: 2-20-13):
In this Assignment, you will solve problems pertaining to multiple linear regression
models. You will run Stata to solve some of the problems.
Feb.13
Feb.20
5. The ANOVA Table and R2
Review of variance, covariance and correlation
Decomposition of total sum of squares, F test
R2 and adjusted R2
Readings:
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.
6. Properties of OLS and the Five Assumptions
BLUE criterion and properties of OLS
Maximum likelihood estimator
The five OLS assumptions
Stata Lab 2: Running multiple regression
Readings:
Kutner et al: 1.8
Kennedy, P. (2003). A Guide to Econometrics, 5th Edition, Chapter 3, Cambridge,
MA: The MIT Press.
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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Feb. 27
7. Stata Lab 3: Diagnostic Tests in Regression Analysis
Residual analysis
Looking for influential data
Multicollinearity diagnostics – Variance Inflation Factor
A Monte Carlo study demonstrating OLS properties
Readings:
Kutner et al: Chapter 3
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.
Assignment 3 out (Due: 3-6-13):
In this Assignment, you will conduct a Monte Carlo study that shows consequences of
OLS regression under various conditions of data generation. Running Stata is required.
Mar.6
8. Violating OLS Assumptions and Remedial Measures – I
Specification errors and selection of Predictors
Influential data and outliers
Multicollinearity
Model validation
Readings:
Kutner et al: Chapters 9 and 10
Midterm exam out (Due: 3-27-13):
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 14 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) 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.
Mar. 13
Happy Spring Break! No Class
Mar. 20
9. Violating OLS Assumptions and Remedial Measures – II
Heteroskedasticity
Weighted Least Squares
Readings:
Kutner et al: Chapter 11
Gujarati, D. N. (1995). Basic Econometrics, Third Edition, McGraw-Hill,
pp. 365-389.
Mar.27
10. Other Topics in Regression Analysis -- I
Regression through origin
Partial-correlation coefficient
Standardized regression coefficient
Functional form, curvilinear relationship and polynomial regression models
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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R2 increment: Hierarchical regression analysis
Readings:
Kutner et al: Chapter 7
White, M., Biddlecom, A. and Guo, S. (1993) “Immigration,
naturalization and residential assimilation among Asian
Americans in 1980s”, Social Forces,72(1): 93-118.
Assignment 4 out (Due: 4-3-13):
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. Some of the problems require running Stata.
Apr. 3
11. Other Topics in Regression Analysis – II
Dummy variables as predictors:
Interpretation of regression coefficients
Comparison of several regression equations
Testing interactions:
Mediator versus moderator
Interaction, joint effect and moderator
ANCOVA
Experimental design
Concomitant variables
Stata Lab 4: Diagnostics of OLS regression
Readings:
Kutner et al: Chapter 8
Apr.10
12. Logistic Regression Analysis -- I
Violations of OLS assumption when dependent variable is dichotomous
Logistic response function
Maximum likelihood estimator
Readings:
Kutner et al: Chapter 13
Assignment 5 out (Due: 4-24-13):
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.
Apr.17
13. Logistic Regression Analysis – II
Odds ratio
Relative risk
Predicted probability
Stata Lab 5: Running logistic regression models
Readings:
Kutner et al: Chapter 14
Agresti, A. (1990). Categorical Data Analysis, pp. 13-19. Hoboken, NJ: John
SOWO 918: Applied Regression Analysis and Generalized Linear Models
Spring 2013
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Wiley & Sons, Inc.
“Final exam part II” out (Due: 5-6-13):
Use data sets provided by the course or data set you choose to run a binary logistic
regression model. Write a paper (no more than 14 pages, double spaced) to present
findings. The paper should include: (1) research questions and data that are suitable to a
logistic regression; (2) methods and specification of the logistic regression model; (3)
diagnostics detecting potential problems in your data that might violate the logistic
regression assumptions; (4) tests of interactions; (5) interpretation of findings; and (6)
presentation of findings that answers research questions effectively and efficiently.
14. Logistic Regression Analysis – III
A Critical Review of the Applications of Regression
Multinomial logistic regression
Ordinal logistic regression
Overview of the generalized linear models
Common pitfalls in conducting and reporting regression analysis
Why Berk claims that “the practice of regression analysis and its extension is a
disaster”?
What to do?
Where to go after this course?
Readings:
Agresti, A. (1990). Categorical Data Analysis, pp. 313-322. Hoboken, NJ: John
Wiley & Sons, Inc.
Schwartz, I., Guo, S., & Kerbs, J. (1993). "The impact of demographic variables
on public opinion regarding juvenile justice: Implications for public
policy". Crime & Delinquency 39(1): 5-28.
Goldstein, A., Goldstein, S., & Guo, S. (1991). "Temporary migrants in Shanghai
household, 1984". Demography 28(2): 275-291.
Barth, R.P., Greeson, J.K, Guo, S., Green, R.L., Hurley, S., & Sisson, J. (2007).
“Changes in family functioning and child behavior following intensive inhome therapy”, Children and Youth Services Review, 29(8): 988-1009.
Berk, A.R. (2005). Regression Analysis: A Constructive Critique, Chapters 1, 5,
11, Thousand Oaks, CA: Sage Publications.
Apr. 24
May 1 Final exam part I (in-class open-book exam)
9:00-11:00 (Students with English as 2nd language will have 30 minutes more)
May 6
“Final exam part II” due
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Spring 2013
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