Semester Fall 2019

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

                        MWF  1:00-2:00pm                            PHR 2.114      LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7^th  Ed. Prentice Hall.

Semester Fall 2019

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

                        TTH    2:00-3:30pm                            PHR 2.116      LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7^th  Ed. Prentice Hall.

Semester Fall 2019

GOV 385L – Title Advanced Statistical Analysis

SDS 385.14 – Title Maximum Likelihood Estimation

Substantial Writing Component: NO

Unique                       Days   Time                           Bldg/Room                 Instructor

                                    TW      5:00-6:30p                   MEZ 1.120                  LIN

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA and/or R for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* J. S. Long and J. Freese. 2014. Regression Models for Categorical Dependent

   Variables Using Stata. 3^nd ed. Stata Press.

Recommended Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Please check back for updates.

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

38490                        TTH    2:00-3:30pm                            PHR 2.114      LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7^th  Ed. Prentice Hall.

GOV 385L – Title Advanced Statistical Analysis

SDS 385.14 – Title Maximum Likelihood Estimation

Substantial Writing Component: NO

Unique                          Days   Time                           Bldg/Room              Instructor

38740                            TTH    5:00-6:30p                   MEZ 1.210                  LIN

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA and/or R for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* J. S. Long and J. Freese. 2014. Regression Models for Categorical Dependent

   Variables Using Stata. 3^nd ed. Stata Press.

Recommended Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

GOV 355M : Human Behavior as Rational Action

Writing Flag & Quantitative Reasoning Flag

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                   4. Presentation: 10%

5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

GOV 380R: Mathematical Methods for Political Analysis 

This course introduces the mathematical concepts and methods essential for multivariate statistical analysis and formal political theory. Mathematical topics include reviews of basic calculus and linear algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and difference and differential equations. Applications in multivariate statistical analysis include multiple regression, principal component analysis, factor analysis, etc., and their estimation in matrix form. Applications in formal political theory include equilibrium analysis and spatial analysis.

Course Requirements

Statistical Analysis in Political Science I&II (or, equivalently, Basic Statistics & Regression)

Grading Policy

1. Homework Assignments: 40%       2. Final Project: 60%

Texts

1. Alpha C. Chiang and Kevin Wainwright. 2004. Fundamental Methods of Mathematical Economics, 4th ed.  McGraw-Hill/Irwin.

2. J. Douglas Carroll and Paul E. Green. 1997. Mathematical Tools for Applied Multivariate Analysis.  Revised ed. Academic Press.

3. (Optional) Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge.

4. (Optional) John Fox. 2009. A Mathematical Primer for Social Statistics. Sage.

5. (Optional) Will H. Moore and David A. Siegel. 2013. A Mathematics Course for Political & Social Research. Princeton.

Semester Fall 2017

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

38715              TTH    2:00-3:30pm                            BUR 220         LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7^th  Ed. Prentice Hall.

Semester Fall 2017

GOV 385L – Title Advanced Statistical Analysis

SDS 385.14 – Title Maximum Likelihood Estimation

Substantial Writing Component: NO

Unique                       Days   Time                           Bldg/Room                 Instructor

38925/57080               MW     5:00-6:30p                   BAT 1.104                  LIN

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* J. S. Long and J. Freese. 2014. Regression Models for Categorical Dependent

   Variables Using Stata. 3^nd ed. Stata Press.

Recommended Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Semester Spring 2017

GOV 355M – Title: Human Behavior as Rational Action

Writing Flag & Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

38685              TTH    11:00-12:30pm                        BEN 1.126      LIN

Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                   4. Presentation: 10%

5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

The University of Texas at Austin

Government 385L

Panel and Multilevel Data Analysis

Spring 2017

Tse-Min Lin and Christopher Wlezien

Tuesdays 3:30-6:30

tml@austin.utexas.edu and Wlezien@austin.utexas.edu

Course Description

The course provides a detailed introduction to pooled time series cross section (TSCS) data analysis and multilevel data analysis.  To begin with, it focuses on the techniques for analysis of various TSCS data sets, from those where the number of time series observations exceeds the number of cross sectional units to those where the number of cross sectional units exceeds the number of time serial observations.  The course then turns to multilevel (ML) data, in which analysis explicitly models the effects of units within which observations are contained.  In both parts of the course, there will be an emphasis both on both the logic of analysis and estimation, the latter of which will involve use of Stata software.  

Course Format

All students are required to do a research paper.  This should contribute to political science knowledge, not (necessarily) TSCS or ML methodology per se. The paper needs to be in the form of a journal article and ready for submission.  Since this is a methods course, you are encouraged to choose a topic that relates to another class or even a paper that that could use a more sophisticated re-analysis.  As it will be prepared for journal submission, the paper should be written for a general audience and need not delve too deeply into methodological issues. 

Grades

The main assignment for this class is the preparation of an original research paper, about which more detailed information will be provided in class.

Performance in the class likely will be assessed as follows:

25%  General class performance

75%  Research paper

•   5%   Hypothesis
• 20%  Proposal
• 50%  Final Paper.

NOTE: Although we do not plan on having a final examination, because we think it distracts from the research paper, we reserve the right to add one if necessary—it depends entirely on student performance in class.

Readings

The course readings will include two books:

Hsiao, Cheng. 2014. Analysis of Panel Data, 3^rd Ed. Cambridge University Press.

D. A. Luke. 2004. Multilevel Modeling. Sage Publications.

Numerous articles and book chapters also will be required. 

Semester Fall 2016

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

38450              MWF  2:00-3:00pm                           PHR 2.114      LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7th  Ed. Prentice Hall.

Semester Fall 2016

GOV 385L – Title Advanced Statistical Analysis

SDS 385.14 – Title Maximum Likelihood Estimation

Substantial Writing Component: NO

Unique                       Days   Time                           Bldg/Room                 Instructor

38680/56970               MW    5:00-6:30p                  BAT 1.104                  LIN

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* J. S. Long and J. Freese. 2014. Regression Models for Categorical Dependent

   Variables Using Stata. 3nd ed. Stata Press.

Recommended Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Semester Spring 2016

GOV 355M – Title: Human Behavior as Rational Action

Writing Flag & Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

37940              TTH    2:00-3:30pm                           CLA 1.102      LIN

Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                  4. Presentation: 10%

5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

Semester

Spring 2016

GOV 380R – Title: Mathematical Methods for Political Analysis

Unique           Days   Time                                       Bldg/Room     Instructor

38110              TTH    5:00PM - 6:30 PM                 BAT 1.104      LIN

Course Description

This course introduces the mathematical concepts and methods essential for multivariate statistical analysis and formal political theory. Mathematical topics include reviews of basic calculus and linear algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and difference and differential equations. Applications in multivariate statistical analysis include multiple regression, principal component analysis, factor analysis, etc., and their estimation in matrix form. Applications in formal political theory include equilibrium analysis and spatial analysis.

Course Requirements

Statistical Analysis in Political Science I&II (or, equivalently, Basic Statistics & Regression)

Grading Policy

1. Homework Assignments: 40%       2. Final Project: 60%

Texts

1. Alpha C. Chiang and Kevin Wainwright. 2004. Fundamental Methods of Mathematical Economics, 4th ed.  McGraw-Hill/Irwin.

2. J. Douglas Carroll and Paul E. Green. 1997. Mathematical Tools for Applied Multivariate Analysis.  Revised ed. Academic Press.

3. (Optional) Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge.

4. (Optional) John Fox. 2009. A Mathematical Primer for Social Statistics. Sage.

5. (Optional) Will H. Moore and David A. Siegel. 2013. A Mathematics Course for Political & Social Research. Princeton.

Semester Fall 2015

GOV 350K – Statistical Analysis in Political Science

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

37744                                                                                                  LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2013. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 7th  Ed. Prentice Hall.

Semester Fall 2015

GOV 385L – Title Advanced Statistical Analysis

SDS 385.14 – Title Maximum Likelihood Estimation

Substantial Writing Component: NO

Unique                       Days   Time                           Bldg/Room                 Instructor

37945  (56290)           TTH    5:00-6:30p                  MEZ 1.204                 LIN

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* J. S. Long and J. Freese. 2014. Regression Models for Categorical Dependent

   Variables Using Stata. 3nd ed. Stata Press.

Recommended Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* P. H. Pollock, III. 2010. A STATA Companion to Political Analysis. 2nd ed. CQ Press.

* A packet of journal articles and book chapters.

Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

37950              TTH    11:00am-12:30pm                   BUR 112         LIN

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Prerequisites

None

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2011. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6th  Ed. Prentice Hall.

Writing Flag & Quantitative Reasoning Flag

Unique           Days   Time                                       Bldg/Room     Instructor

37990              TTH    3:30-5:00pm                           MEZ 1.216     LIN

Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                  4. Presentation: 10%

5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

6. Clay Shirky (2009), Here Comes Everybody (Penguin Books).

Semester Fall 2014

GOV 385L – Title Advanced Statistical Analysis

Substantial Writing Component: NO

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal (5-10 pages) laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Recommended Texts:

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* J. S. Long and J. Freese. 2006. Regression Models for Categorical Dependent

   Variables Using Stata. 2nd ed. Stata Press.

Writing Flag: NO

Course Description

This course is designed to examine the formal and statistical structure of techniques useful for analyzing dynamic processes. Subtopics include difference equations; stationary ARMA processes; persistent and/or nonstationary processes including integrated, fractionally integrated, and near-integrated processes; the estimation and forecasting of time series single equation regression; cointegration and error correction; Granger causality and vector autoregression; time-varying parameter regression, and time-series cross-section models.

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Grading Policy

• Homework Assignments (20%)
• Project/Paper Proposal (20%)
• Final Project/Paper (60%)

Required Texts:

• Walter Enders. 2009. Applied Econometric Time Series, 3e. Wiley.
• Patrick T. Brandt and John T. Williams. 2007. Multiple Time Series. Sage.
• A packet of journal articles and book chapters.

Recommended Texts

• C. Chatfield. 1996. The Analysis of Time Series: An Introduction, 5e. Chapman and Hall.
• Samuel Goldberg. 2010. Introduction to Difference Equations: With Illustrative Examples from Economics, Psychology, and Sociology. Dover.
• John M. Gottman. 1981. Time-Series Analysis: A Comprehensive Introduction for Social Scientists. Cambridge
• Damodar N. Gujarati. 2003. Basic Econometrics, 4e. McGraw-Hill & Irwin.
• G. S. Maddala and In-Moo Kim. 1998. Unit Roots, Cointegration, and Structural Change. Cambridge.
• Richard McCleary and Richard A. Hay, Jr. 1980. Applied Time Series Analysis for the Social Sciences. Sage.
• Robert S. Pindyck and Daniel L. Rubinfeld. 1997. Econometric Models and Economic Forecasts, 4e. McGraw-Hill.

 Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                  4. Presentation: 10%

5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

6. Clay Shirky (2009), Here Comes Everybody (Penguin Books).

FLAG: Writing

Course Description

This course introduces the mathematical concepts and methods essential for multivariate statistical analysis and formal political theory. Mathematical topics include reviews of basic calculus and linear algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and difference and differential equations. Applications in multivariate statistical analysis include multiple regression, principal component analysis, factor analysis, etc., and their estimation in matrix form. Applications in formal political theory include equilibrium analysis, dynamic analysis, and spatial analysis.

Course Requirements

Statistical Analysis in Political Science I

Grading Policy

1. Homework Assignments: 40%       2. Final Project: 60%

Texts

1. Alpha C. Chiang and Kevin Wainwright. 2004. Fundamental Methods of Mathematical Economics, 4th ed.  McGraw-Hill/Irwin.

2. J. Douglas Carroll and Paul E. Green. 1997. Mathematical Tools for Applied Multivariate Analysis.  Revised ed. Academic Press.

3. (Optional) Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge.

4. (Optional) Will H. Moore and David A. Siegel. 2013. A Mathematics Course for Political & Social Research. Princeton.

4. (Optional) John Fox. 2009. A Mathematical Primer for Social Statistics. Sage.

Prerequisites

None

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

* S. B. Green and N. J. Salkind, 2011. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6th  Ed. Prentice Hall.

Prerequisites 

Statistical Analysis in Political Science II or its equivalent

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal (5-10 pages) laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Recommended Texts

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* J. S. Long and J. Freese. 2006. Regression Models for Categorical Dependent

   Variables Using Stata. 2nd ed. Stata Press.

Prerequisites

Upper-division standing required.

6 semester hours of lower-division coursework in government.

Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Grading Policy 

1. First Paper (6-8 pages): 25%                      2. Second Paper (7-9 pages): 25%

3. Third Paper (8-10 pages): 30%                   4. Presentation: 10%

5. Attendance: 10%

Texts 

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books).

6. Clay Shirky (2009), Here Comes Everybody (Penguin Books).

Prerequisites

Statistical Analysis in Political Science II or its equivalent

Course Description

This course is designed to examine the formal and statistical structure of techniques useful for analyzing dynamic processes. Subtopics include difference equations; stationary ARMA processes; persistent and/or nonstationary processes including integrated, fractionally integrated, and near-integrated processes; the estimation and forecasting of time series single equation regression; cointegration and error correction; Granger causality and vector autoregression; time-varying parameter regression, and time-series cross-section models.

Grading Policy

• Homework Assignments (20%)
• Project/Paper Proposal (20%)
• Final Project/Paper (60%)

Required Texts:

• Walter Enders. 2009. Applied Econometric Time Series, 3e. Wiley.
• Patrick T. Brandt and John T. Williams. 2007. Multiple Time Series. Sage.
• A packet of journal articles and book chapters. 

Recommended Texts

• C. Chatfield. 1996. The Analysis of Time Series: An Introduction, 5e. Chapman and Hall.
• Samuel Goldberg. 2010. Introduction to Difference Equations: With Illustrative Examples from Economics, Psychology, and Sociology. Dover.
• John M. Gottman. 1981. Time-Series Analysis: A Comprehensive Introduction for Social Scientists. Cambridge
• Damodar N. Gujarati. 2003. Basic Econometrics, 4e. McGraw-Hill & Irwin.
• G. S. Maddala and In-Moo Kim. 1998. Unit Roots, Cointegration, and Structural Change. Cambridge.
• Richard McCleary and Richard A. Hay, Jr. 1980. Applied Time Series Analysis for the Social Sciences. Sage.
• Robert S. Pindyck and Daniel L. Rubinfeld. 1997. Econometric Models and Economic Forecasts, 4e. McGraw-Hill.

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Grading Policy

Homework Assignments (6-7 sets): 30%

In-Class Midterm Exam: 30%

In-Class Final Exam: 30%

Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional Texts

 * S. B. Green and N. J. Salkind, 2011. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6th  Ed. Prentice Hall.

Prerequisites

Statistical Analysis in Political Science II or its equivalent.

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, and other models depending on your interests. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MATHEMATICA for mathematical analysis.

Grading Policy 

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By the end of October, you should turn in a paper proposal (5-10 pages) laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.

* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.

   Michigan.

* T. F. Liao. 1994. Interpreting Probability Models. Sage.

* A packet of journal articles and book chapters.

Strongly Recommended Texts

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.

* J. S. Long and J. Freese. 2006. Regression Models for Categorical Dependent

   Variables Using Stata. 2nd ed. Stata Press.

Course Description

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.

In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.

So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.

Course Requirements

Upper-division standing required.  6 semester hours of lower-division coursework in government.

Grading Policy

1. First Paper (6-8 pages): 25%        2. Second Paper (7-9 pages): 25%3. Third Paper (8-10 pages): 30%        4. Presentation: 10%5. Attendance: 10%

Texts

1. Thomas C. Schelling (1978), Micromotives and Macrobehavior (Norton).

2. Robert Axelrod (1984), The Evolution of Cooperation (Basic Books).

3. Dennis Chong (1991), Collective Action and the Civil Rights Movement (Chicago).

4. Elinor Ostrom (1990), Governing the Commons (Cambridge).

5. Howard Rheingold (2002), Smart Mobs: The Next Social Revolution (Basic Books)

Course Description

This course introduces the mathematical concepts and methods essential for multivariate statistical analysis and formal political theory. Mathematical topics include reviews of basic calculus and linear algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and difference and differential equations. Applications in multivariate statistical analysis include multiple regression, principal component analysis, factor analysis, etc., and their estimation in matrix form. Applications in formal political theory include equilibrium analysis, dynamic analysis, and spatial analysis.

Course Requirements

None

Grading Policy

1. Homework Assignments: 40% 

2. Final Project: 60%

Texts

1. Alpha C. Chiang and Kevin Wainwright. 2004. Fundamental Methods of Mathematical Economics, 4th ed.  McGraw-Hill/Irwin.

2. J. Douglas Carroll and Paul E. Green. 1997. Mathematical Tools for Applied Multivariate Analysis.  Revised ed. Academic Press.

3. (Optional) Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge.

4. (Optional) John Fox. 2009. A Mathematical Primer for Social Statistics. Sage.

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, and other statistical procedures. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Grading Policy

Homework Assignments (6-7 sets): 30% In-Class Midterm Exam: 30% In-Class Final Exam: 30%Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional

* S. B. Green and N. J. Salkind, 2011. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6th  Ed. Prentice Hall.

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, models for time-series cross-section data, and models for hierarchical data. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MAPLE or MATHEMATICA for symbolic algebra.

Course Requirements

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By Week 8, you should turn in a paper proposal (5-10 pages) laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.   Michigan.* T. F. Liao. 1994. Interpreting Probability Models. Sage.* A packet of journal articles and book chapters.

Strongly Recommended:

* W. H. Greene. 2012. Econometric Analysis. 7th ed. Pearson  & Prentice Hall.* J. S. Long and J. Freese. 2006. Regression Models for Categorical Dependent   Variables Using Stata. 2nd ed. Stata Press.

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statisticalcourses you may have taken, the emphasis here will be on applications in political science. The objective ofthis course is to help students acquire the literacy for understanding political science literatures based on thescientific approach, as well as to prepare interested students for more advanced methods courses.Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution,point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation,and simple regression. Computing will be an integral part of this course. You will use SPSS to analyze datafrom Gallup Survey, General Social Survey, and/or National Election Study in homework assignments. Inparticular, you will be asked to replicate results reported in journal articles and book chapters. You are alsoencouraged to develop and work out your own research problems.

The term “rational action” as used in the economic approach is generally equated with maximizing behavior. Individual human agents are assumed to have consistent and stable preferences over alternatives each of which is assigned some “utility.” Maximization entails choosing the course of action that yields the highest expected utility. One is rational to the extent one uses the best means to achieve one’s goals.In this course we will learn a variety of social and political models based on such a notion of individual rationality and to investigate the collective consequences that can be logically inferred from its assumptions. In particular, we will find through the “Prisoner’s Dilemma,” the “Tragedy of the Commons,” and the “Free-Rider Problem” a contrast between rational man and irrational society. Self-serving behavior of individuals does not usually lead to collectively satisfactory results.So this course is about the stories of the Prisoners, the Herdsmen, and the Free-Riders. As a matter of fact, we will show that the Dilemma, the Tragedy, and the Problem share essentially the same mathematical structure, and hence they are essentially the same story - a story about human destiny. We will also introduce the various approaches that have been proposed for the escape from such a destiny.Course RequirementsUpper-division standing required.6 semester hours of lower-division coursework in government.

Course Description

In this course we will study some advanced statistical analyses, including models with categorical or limited dependent variable, event count models, event history models, models for time-series cross-section data, and models for hierarchical data. Most of these models rely on the maximum likelihood method of estimation, and hence we will first discuss probability distributions and statistical estimation theory, with an emphasis on the MLE. We will use STATA for statistical analysis and MAPLE for symbolic algebra.

Course Requirements

Statistical Analysis in Political Science II or its equivalent

Grading Policy

You are required to write a research paper based on a statistical procedure introduced in this class. The topic of the paper is your own choice, but you should discuss your ideas with the instructor early in the semester to obtain his approval. Depending on substantive merits, topics based “simplistic” methods may not be acceptable. By Week 8, you should turn in a paper proposal (5-10 pages) laying out your theoretical arguments, describing your data, and presenting your research design. You should work closely with the instructor in developing ideas, formulating models, acquiring data, and carrying out the analyses. Your grade will be based on the end result as well as your interaction with the instructor while working on this paper. There will be homework assignments and/or exams as the instructor deems necessary.

Required Texts:

* S. R. Eliason. 1993. Maximum Likelihood Estimation: Logic and Practice. Sage.
* G. King.1998. Political Methodology: The Likelihood Theory of Statistical Inference.
   Michigan.
* T. F. Liao. 1994. Interpreting Probability Models. Sage.
* A packet of journal articles and book chapters.

Strongly Recommended:

* J. M. Box-Steffensmeier and B. S. Jones. 2004. Event History Analysis. Cambridge.
* W. H. Greene. 2008. Econometric Analysis. 6th ed. Pearson  & Prentice Hall.
* J. S. Long and J. Freese. 2006. Regression Models for Categorical Dependent
   Variables Using Stata. 2nd ed. Stata Press.

Course Description

This course introduces basic concepts and methods of statistics. Unlike the typical elementary statistical courses you may have taken, the emphasis here will be on applications in political science. The objective of this course is to help students acquire the literacy for understanding political science literatures based on the scientific approach, as well as to prepare interested students for more advanced methods courses.

Topics include descriptive statistics, probability and probability distributions, sampling, sampling distribution, point estimation, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, and simple regression. Computing will be an integral part of this course. You will use SPSS to analyze data from Gallup Survey, General Social Survey, and National Election Study in homework assignments. In particular, you will be asked to replicate results reported in journal articles and book chapters. You are also encouraged to develop and work out your own research problems.

Course Requirements


Grading Policy

Homework Assignments (5-7 sets): 30%
In-Class Midterm Exam: 30%
In-Class Final Exam: 30%
Instructor Discretion (Attendance, Participation, etc.): 10%

Required Texts

* T. H. Wonnacott and R. J. Wannacott. 1990. Introductory Statistics, 5th Ed. Wiley. (Or 4th Ed., Introductory Statistics for Buisness and Economics, 1990, which is the same as the 5th Ed.)

Optional

* S. B. Green and N. J. Salkind, 2008. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 5th  Ed. Prentice Hall.

Please check back for updates.

Please check back for updates.

Please check back for updates.

Please check back for updates.

Please check back for updates.

Please check back for updates.