PSC 200: Applied Data Analysis                                         

Fall 2008

Lecture: 10:00-10:50 Mon, Wed, Gavet 301

Labs: Thursdays 2:00 – 3:15 (Hylan 307) and Fridays 10:00-10:50 (Gavet 244)

Class website: http://mail.rochester.edu/~mksr/PSC200.htm

 

 


Prof. Mark Kayser

mark.kayser@rochester.edu

Harkness 320B

OH: Wednesdays 2:30-4:30

Adam Ramey (TA)

adam.ramey@rochester.edu

Harkness 334

OH: Tuesdays 1:45-2:45


 

Overview: This course offers an introduction to empirical research methods in political science.  By the end of the semester, students should have a better acquaintance with the type of empirical work done by most political scientists (and other social scientist) and the ability to understand and critique it.  

 

Readings are from the following books:

 

Required:

 

On library reserve for reference:

 

Grading: Grades will be based on three in-class exams, multiple data analysis exercises, several brief homework assignments, and a final data analysis/paper.  Unless otherwise indicated, each assignment is due one week from the day it is assigned.  Assignments must be turned in on paper (not emailed) and properly formatted.  Grades will be weighted as follows: exams 15% each; data analyses and homework assignments 30% (collectively); final data analysis/paper 15%; participation in weekly labs 10%. The lowest grade on a homework or data analysis assignment will be dropped with no questions asked.  Use this privilege wisely; it is intended for unforeseen circumstances, accidents, and illnesses. Late assignments are downgraded by one grade level for each day they are late, e.g. B+ to B and will not be accepted after seven days.  Students are responsible for delivering their homework, in hardcopy, to the TA.  Emailed attachments will not be accepted.  Grade appeals should be submitted to the appropriate TA as a type-written memo specifying the question(s) in doubt and grounds for the appeal. 

 

 

Make-up Exams:  Students are expected to take all exams at the announced times.  A single make-up exam will be scheduled for students with documented extenuating circumstances such as personal illness requiring medical attention.  Athletes whose competition schedule prevents them from taking an exam should arrange for their coaches to administer the exam.  Undocumented absences earn a zero.

 

Recitations: The teaching assistant will hold weekly recitations in computer labs to reinforce concepts from the class, assist with software questions, review homework, and provide general guidance.  Attendance is mandatory.

 

Readings: The readings are shown below.  Readings not from the class texts are available on JSTOR (www.jstor.org) or via hyperlink from the online version of this syllabus (http://mail.rochester.edu/~mksr/PSC200.html).  They are provided as an example of how the methods you are learning are used in actual research.  Please be sure to read them before class.  The review problems at the end of each chapter in Pollock are very helpful and may occasionally be assigned as homework.  You may wish to do them regardless of whether they are assigned.  The Agresti and Finlay book on reserve in the library is a good supplementary resource.

 

Software: We use SPSS for the most of our computer based analysis in this class.  The menu-driven interface of this package makes it appealing for students gaining their first introduction to data analysis.  If you have data analytic ambitions that extend beyond this class, you will probably notice many of the shortcomings of SPSS for intensive users.  In such a case, you might want to consider programs such as STATA (fast) and R (flexible and free).  Both your professor and TA are happy to support you in your use of these other packages.

 

Academic Honesty:  None of the assignments in this class are collaborative.  I encourage you to study together and learn to use the software together.  Assignments, however, are expected to represent your individual effort.  Copied or plagiarized work will incur penalties consistent with the College’s policy on academic honesty (http://www.rochester.edu/College/honesty/).

 

Syllabus: This syllabus may be altered during the semester to accommodate the learning pace of the class.  It is the students’ responsibility to keep abreast of assignments and due dates by attending class monitoring the class website (http://mail.rochester.edu/~mksr/PSC200.html).  I often post lecture notes online.

 

 

 

COURSE SCHEDULE:

 

Week 1.  Introduction

 

 

Week 2.  Concepts and Measurement

 

Readings:

Supplemental:

 

Homework 1 (assigned in class)

 

Week 3-1.  Descriptive Statistics

 

Readings:

 

 

Supplemental:

 

 

 

Week 3-2.  Constructing Variables

 

Readings:

Supplemental:

 

1st Data Analysis.  This assignment will be described in detail in class. 

 

 

Week 4-1.  Forming Hypotheses

 

Readings:

Supplemental:

 

 

 

Week 4-2.  Research Design: Experiments and Controlled Comparisons

 

Readings:

Supplemental:

 

Homework 2 (assigned in class)

 

 

 

Week 5-1.  In-Class Review

 

Week 5-2.  Exam 1

 

 

Week 6-1.  Assessing Hypotheses:  Crosstabs & Means

 

Readings: 

Supplemental:

 

Week 6-2.  Controlling for a Third Variable

                                                  

Readings:

Supplemental:

 

2nd Data Analysis assigned in class. 

 

 

 

Week 7-1.  z distributions, Confidence Intervals and Inference

 

Readings:

Supplemental:

 

 

Week 7-2.  t distributions and Inference with Sample Proportions

 

Readings:

Supplemental:

 

Homework 2 assigned in class

 

 

Week 8-1.  Review of Statistical Inference & Probability Distributions

 

 

Week 8-2.  Tests of Significance

 

Readings:

Supplemental:

 

 

Week 9-1.   In-Class Review

 

Week 9-2.   Exam 2

 

 

10-1.  Proportional Reduction of Error & Chi-sq tests 

 

Readings:

Supplemental:

 

 

Week 10-2. Correlation and Bivariate Regression

 

Readings: 

 

3rd Data Analysis:  assigned in class.

 

Week 11-1.  Measures of Fit

 

Readings:

Supplemental:

 

Week 11-2. No Class

 

Week 12-1: Introduction to Multiple Regression

 

Readings:

Supplemental:

 

Final Data Analysis: Multiple Regression.  Assignment will be handed out in class.

 

Week 12-2: OLS Assumptions

 

Readings:

 

Week 13-1.  Influential Observations

Readings:

Supplemental:

 

 

Week 13-2.  Multicollinearity

 

Readings:

 

 

Week 14-1.  Model Specification

 

 

Week 14-2.   Non-linear Regression

Supplemental:

 

Week 15-1.   In Class Review

 

Week 15-2.   Exam 3

 

Final Data Analysis Due after Exam 3.  Due date will be announced in class.

Does globalization shrink or expand the welfare state?  You will evaluate the empirical support for both the efficiency (shrinking state) and compensation (expanding state) hypotheses.