**POLS 543**

**Advanced
Political Data Analysis**

*Spring 2005* Tuesdays
3:00-5:40

DuSable 252 and
SOCQRL (DU 222)

Professor Paul
Culhane Professor
Charles Cappell

828 N. Forrest
Avenue Social
Science Research Institute Bldg.

Arlington
Heights, Illinois 60004 148 N. 3^{rd}
St.

(847) 392‑1233
[until 10 PM] DeKalb, Illinois

pculhane828@cs.com (815) 753-1123

IASBO 204,
753-0311, M Tu 1:00-2:30
ccappell@niu.edu

The
advanced quantitative analysis course in political science satisfies one course
requirement for the high proficiency research tool requirement. The course is modularized, so that students
may retake POLS 543 as the module topics change. This semester, the course consists of two modules. First, Professor Cappell will cover maximum
likelihood estimation methods for binary and other categorical dependent
variables, that is, Logit, Probit, and their extensions. In the second module, Professor will cover
ARIMA methods for time-series analysis.

__Readings__

The
required texts are:

J. Scott Long, *Regression Models for
Categorical and Limited Dependant Variables*, Sage, 1997;

Tim
Futing Lao, *Interpreting Probability Models: Logit, Probit, and Other
Generalized Linear*

* Models*, Sage, QASS 101, 1994;

Richard
McCleary & R. Hay, *Applied Time Series Analysis for the Social Sciences*,
Sage, 1980;

David
McDowell, R. McCleary, E. Meidinger & R. Hay, *Interrupted Time Series
Analysis,*

Sage, QASS 21, 1980;

*SPSS
Trends*
manual (for your version of SPSS).

The
Long book and the Lao and McDowell et al. QASS monographs are available at VCB
and the university bookstore. The
McCleary & Hay text is out of print; it is on one-day Reserve at the
university library, and copies are available on the internet.

It
is cost-effective for doctoral students to purchase major software packages
while they have graduate student status.
We will arrange for special orders of SPSS GradPack, for those who do
not yet own this software and wish to obtain it this semester. In particular, for both those who already
have GradPack and those who obtain it this semester, we can special-order a licence
to activate SPSS Trends, which will be used for the ARIMA course module. In addition, we will also assist special
orders for students who wish to obtain the state-of-the-art, in-print, though
expensive Enders (2003) time-series book.

__Course
Requirements__

Both
modules will focus on practical applications of the analysis techniques, that
is, construction of analysis models and interpretation of results. Each module will include specific practice
exercises, described on assignments passed out at the end of specific segments
of each module, and a capstone project.
For the capstone project, students are encouraged to use problems from
their own research, e.g. a preliminary (or actual) dissertation project in the
case of doctoral students; if students’s current research is not applicable
(i.e, no categorical dependent variables or no time series data) the professors
will assist you in identifying an appropriate test data set.

__ Material Covered __ __ Date __ __Worth__

Categorical:
Weekly Exercises Weekly 30%

Categorical:
Capstone Paper March
22 20%

Time
Series: Weekly Exercises Weekly 30%

Time
Series: Capstone Paper May
10 20%

Attendance
and participation are required. The course will be fast-paced and absences from
the lecture or lab sessions will break the flow of learning. Attendance and
participation may count for ±5%; in particular, absences may incur up to a 1%
penalty. Auditing students may forgo
one practice assignment in each module.

This
is a SOCQRL fee-assessed course, and both modules will make extensive use of
the Sociology Quantitative Research Lab (SOCQRL): software, data archives,
workstations with reserved class time each Tuesday. Weekly or semi-weekly
assignments will require the use of the lab in addition to that reserved during
class time to complete the assignments which require lab resources.

Students
should be familiar with the University's and Department's policies regarding
withdrawal from courses and academic honesty.
While cooperation among students, especially in a lab setting, is
traditional and usually useful, all work submitted for a grade (both practice
assignments and module projects) must represent the individual student's
work. The Department allows
"incomplete" grades only in extraordinary circumstances.

__Course
Schedule__

January
18. Course plan; introduction to
SOCQRL; begin regression review.

January
23. Review of regression foundations.

Read: Long Ch. 1-2; review covariance and regression in your intermediate text.

February 1. Nonlinear
Models: Categorical Distributions and Maximum Likelihood Estimation;
Introduction to Logistic & Probit Models. Notation and Functions.

Read: Liao
Ch.1-2, pp. 1-10; Long Ch. 2-3, pp. 25-54; Cappell, “Notation; Mathematical
Operators; Logistic Regression” handout.

February 8. Estimation
and Interpretation of Logistic Models.

Read: Long Ch. 3, pp. 59-83; Liao Ch.3, pp. 10-25; Cappell, “Logistic
Regression” handout. Recommended: Long Ch. 3, pp. 54-58.

February 15. Hypothesis
Testing and Test Statistics.

Read: Long Ch. 4, pp. 85-113;
Cappell, “Logistic Regression” handout;

Long Ch. 3 pp. 59-83.

February 22. Ordinal
Dependent Variables – The Proportional Odds Model.

Read: Long Ch.
5, pp. 114-147; Liao Ch. 5, pp. 37-47; Cappell, “Proportional Odds Model”
handout.

March 1. Multinomial
Logit Models.

Read: Long Ch. 6: pp. 148-186;
Liao Ch. 6 pp. 48-59; Cappell, “Multinomial Logit” handout.

March 8. Recapitulation
and Preview of Advanced Extensions: Loglinear Analysis, Poisson, Latent Class,
Generalized Linear Models, and GLLAMM. Capstone Topics.

Recommended: Long Ch.
7-9; Cappell, “Loglinear” handouts.

March 15. Spring
break.

March 22. Time
series introduction, the OLS model, and the limitations of D.

Reference: Charles Ostrom,
*Time Series Analysis*, Sage, QASS #9, 1978.

March 29. ARIMA
math foundations; univariate estimation.

Read: McCleary & Hay ch. 1, 2.1-2.8;
McDowell et al. §1-2.7.

April 5. Debriefing of first module capstone
projects; ARIMA lab.

April 12. Seasonality.

Read: McCleary & Hay ch. 2.9-2.13; McDowell et
al. §2.8.

April 19. Intervention estimation.

Read: McCleary & Hay ch. 3-4; McDowell et al.
§3.

Recommended: McCleary & Hay ch. 6.

April 26. Multivariate
ARIMA.

Read: McCleary & Hay ch. 5.

May 3. Complete
time series module.

References: Walter Enders, *Applied
Econometric Time Series,* Wiley, 2nd., 2004.

Lois Sayrs, *Pooled Time Series
Analysis*, Sage, QASS #70, 1989.

May 10. Final
exam period: Debrief second module
projects.