Social Science 534
Quantitative Analysis in
Social Science
Thursday
3006
INSTRUCTOR: Dr. WU Xiaogang
OFFICE: 3371
PHONE: 23587827
EMAIL: sowu@ust.hk
OFFICE HOURS: Wednesday
INSTRUCTIONAL ASSISTANT: Mr. Stephen WAN Hoi Ming
OFFICE: 3001
PHONE: 23587817
EMAIL: sohmsw@ust.hk
OFFICE HOURS: TBA
OVERVIEW
This course surveys the statistical
models commonly used in empirical social research, building on the topics
covered in SOSC 509. We will start with a basic review of multiple linear
regression models, and then consider models and methods that should be used
when the assumptions of the classical linear regression model are violated. We
will cover methods for categorical data analysis, including techniques for binary,
ordered and unordered polytomous (dependent) variables,
frequency counts, and censored and truncated variables; methods for longitudinal
data, including panels and event histories. Additional topics such as hierarchical
linear models and structural equation models are also to be introduced to those
who seek further training in quantitative analysis.
PREREQUISITE
SOSC509 or its equivalent is
required.
COURSE WEBSITE AND DISCUSSION BOARD
I have established a WebCT account for this course (http://webct.ust.hk). You may use your ITSC
username and password to log in and find the course syllabus, homework
assignments, data sets, and links to other learning resources. You may also post
your questions oand exchange ideas with me, IA, or
your fellow classmates on the discussion board. I will load the lecture notes
on the web by the
● Required Textbooks
Jeffrey M.
Wooldridge. 2003. Introductory
Econometrics: A Modern Approach (2nd edition)
Daniel A. Powers and Yu Xie 2000. Statistical
Methods for Categorical Data Analysis.
● Recommended textbook
Scott J. Long and Jeremy Freese
2001. Regression Models for Categorical Dependent
Variable Using Stata.
The three
books have been ordered via the HKUST bookshop.
● Supplementary
You may be asked to read a few research
articles as exemplary applications of the models covered in class. They will be
distributed in electronic format.
REQUIREMENTS
You will be assessed through 5
assignments (50% of final grade), a take-home midterm exam (20% of final grade),
and a term paper (30%). Stephen, the Instructional Assistant, will help you use
STATA to solve homework problems in lab session.
COMPUTING
You will be doing substantial
amount of data analysis with the aid of a software package called STATA 8.0,
which many of you have got familiar with via SOSC509. Other software packages
are introduced if necessary. You can either purchase STATA under “STATA Grad
Plan” program, or use it in Social Science Computing lab. If you decide to
purchase the software, please go to http://www.stata.com/info/order/new/edu/gradplans/gp2-order.html, and tell them the course id is socsci25. You can get it by a discounted price.
You are assumed to have taken
SOSC509 and known the basics for using STATA. The tutorial sessions are focused
on how to use STATA to estimate and interpret specific models taught in lecture.
If you have no experience of using STATA before, you should put some extra
effort on the software. Your IA will be available for help.
TOPICS AND
TENTATIVE SCHEDULE
Week 1 [5
February] Linear Regression I
Powers and Xie 2
Wooldridge
2-4
Week 2 [12
February] Linear Regression II
Wooldridge
6-7
Paper
Proposal Due (refer to Wooldridge 19)
Week 3 [19
February] Linear Regression III
Wooldridge
8-9
Week 4 [26
February] Logit and Probit
Models
Powers and Xie 3
Wooldridge 17.1
Long and Freese: 3-4
Homework
1 Due
Week 5 [4 March] Ordered Logit, Multinomial Logit, and Conditional
Logit Models I
Powers and Xie 6 - 7
Long and Freese 5
Week 6 [11
March] Ordered Logit, Multinomial Logit,
and Conditional Logit Models II
Powers and Xie 6 & 7
Wooldridge 17.3
Long and Freese 6
Homework 2 Due
Week 7 [18 March] Poisson
Regression, Loglinear Models for Contingency
Tables
Powers and Xie
4
Long and Freese 7
Week 8
[25 March] Censored and Truncated Regression Models
Wooldridge
17.2-17.5
Homework 3 Due
Week 9 [1
April] TAKE-HOME MIDTERM EXAM (NO CLASS)
[5-10 APRIL,
MID-SEMESTER BREAK]
Week 10 [15 April] Event
History Analysis I
Powers and Xie 5
Week 11
[22 April] Event History Analysis II
Powers and Xie
5
Week 12 [29 April] Models
for Panel and Other Types of Clustered Data
Wooldridge 13-14
Homework 4 Due
Week 13 [6 May] Multi-level Models
DiPrete, Thomas A. and Jerry D. Forristal 1994
“Multilevel Models: Methods and Substance” Annual Review of Sociology 20:331-57
Mason,
William 2001 “Multilevel Methods of Statistical Analysis”
Population
Research Working Paper Series
Week 14
[13 May] Structural Equation Models: Endogeneity and Simultaneity
Wooldridge
15-16
Homework 5 Due
TERM
PAPER DUE AT
*****************************************************************************
Supplementary
1. Xie, Yu and Emily Hannum 1996 “Regional Variation in Earnings Inequality in
Reform-era
2. Meng, Xin
and Paul Miller 1995 “Occupational Segregation and Its Impact on Gender Wage
Discrimination in
3. Entwisle, Barbara, Gail
Henderson, et. al. 1995.“Gender and
Family Business in Rural
4. Lin,
5. Greeley, Andrew M. and Michael Hout
1999. “Americans’ Increasing Belief in Life After
Death: Religious Competition and Acculturation.” American Sociological Review 64:813-835 [Binary and ordered logit
model]
6. Kung, James Kai-sing. 2002. “Off-farm Labor Markets and
the Emergence of Land Rental Markets in Rural Markets.” Journal of Comparative Economics 30-395-414 [Tobit
model]
7. Kung, James Kai-sing and Yiu-fai
Lee. 2001. “So What If There is Income Inequality? The
Distributive Consequence of Nonfarm Employment in
Rural
8. Zhou, Xueguang, Nancy Tuma, and Phyllis Moen 1997 “Institutional Change and
Job-Shift Patterns in Urban
9. Wu, Xiaogang, and Donald Treiman. 2004 “The Household Registration System and Social
Stratification in
10. Peng, Yusheng 2001. “
11. Hao, Lingxin
and Guihua Xie. 2001. “The
Complexity and Endogeneity of Family Structure in
Explaining Children’s Misbehavior.” Social
Science Research 31:1-28 [Fixed effect model]
12. Li, Hongbin 2003.
“Government’s Budget Constraint, Competition, and Privatization: Evidence from