KENNESAW STATE UNIVERSITY
GRADUATE COURSE PROPOSAL OR REVISION,
Cover Sheet (10/02/2002)
Course Number/Program Name INCM 9210/International Conflict Management
Department College of Humanities and Social Sciences
Degree Title (if applicable) Ph.D. in International Conflict Management
Proposed Effective Date Fall 2010
Check one or more of the following and complete the appropriate sections:
X New Course Proposal
Course Title Change
Course Number Change
Course Credit Change
Course Prerequisite Change
Course Description Change
Sections to be Completed
II, III, IV, V, VII
I, II, III
I, II, III
I, II, III
I, II, III
I, II, III
Notes:
If proposed changes to an existing course are substantial (credit hours, title, and description), a new course with a
new number should be proposed.
A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as part of a new
program. Current catalog information (Section I) is required for each existing course incorporated into the
program.
Minor changes to a course can use the simplified E-Z Course Change Form.
Submitted by:
Approved
Volker Franke Ph.D.
Faculty Member
_____
Date
Not Approved
Department Curriculum Committee Date
Approved
Approved
Approved
Approved
Approved
Approved
Not Approved
Department Chair
Date
School Curriculum Committee
Date
School Dean
Date
GPCC Chair
Date
Dean, Graduate College
Date
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Vice President for Academic Affairs Date
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President
Date
KENNESAW STATE UNIVERSITY
GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE
I.
Current Information (Fill in for changes)
Page Number in Current Catalog
Course Prefix and Number
Course Title
Credit Hours
Prerequisites_
Description (or Current Degree Requirements)
II.
Proposed Information (Fill in for changes and new courses)
Course Prefix and Number INCM 9210_____________________________
Course Title Advanced Quantitative Methods
Credit Hours 3-2-4
Prerequisites INCM 9102 Quantitative Methods
Description (or Proposed Degree Requirements)
This course will cover advanced topics beyond those covered in INCM
9102: Quantitative Methods such as nonlinear statistical methods, game
theory, social networking analysis, spatial statistical analysis, and
aggregate data analysis. The lab component will involve advanced use of
statistical packages in the analysis of international conflicts.
III.
Justification
This course provides the student with advanced knowledge to excel in
research methods after completion of Quantitative Methods.
IV.
Additional Information (for New Courses only)
Instructor:_ Richard Engstrom, Ph.D.
Text:
Prerequisites: INCM 9102 Quantitative Methods
Objectives:
 Identify research questions and data characteristics that require the use of
maximum likelihood estimation techniques.
 Explain key concepts associated with MLE approaches to statistical analysis.
 Formulate a research question, theory, and a hypothesis that can be tested with
appropriate data.
 Collect original data in a manner appropriate for use in statistical analysis using
MLE techniques.
 Use STATA statistical software to analyze data.
 Relate statistical results to the research question guiding the research project.
 Present, in oral and written form, the results of an MLE analysis.
Instructional Method
-Class discussion, lectures, projects
Method of Evaluation
-Tests, presentation, research paper and class participation
V.
Resources and Funding Required (New Courses only)
Resource
Amount
Faculty
Other Personnel
Equipment
Supplies
Travel
New Books
New Journals
Other (Specify)
TOTAL
Funding Required Beyond
Normal Departmental Growth
The costs are included in the overall cost for the new Ph.D. program and are not separate.
VI. COURSE MASTER FORM
This form will be completed by the requesting department and will be sent to the Office of the
Registrar once the course has been approved by the Office of the President.
The form is required for all new courses.
DISCIPLINE
COURSE NUMBER
COURSE TITLE FOR LABEL
(Note: Limit 16 spaces)
CLASS-LAB-CREDIT HOURS
Approval, Effective Term
Grades Allowed (Regular or S/U)
If course used to satisfy CPC, what areas?
Learning Support Programs courses which are
required as prerequisites
INCM
9210
Advanced Quantitative Methods
3-2-4
Fall 2010
Regular
APPROVED:
________________________________________________
Vice President for Academic Affairs or Designee __
VII Attach Syllabus
INCM 9210: Advanced Quantitative Methods
Ph.D. Program in International Conflict Management
Kennesaw State University
I. Professor Contact Information:
Dr. Richard N. Engstrom
Room 2040B, Social Sciences Building
rengstro@kennesaw.edu
678-797-2930
II. Course Pre-requisites, Co-requisites, and/or Other Restrictions
INCM 9102
III. Course Description
This course moves beyond OLS regression models, and focuses on Maximum Likelihood Estimation; a set of
nonlinear approaches to applying statistical methods to discrete choice data. We will examine methods for handling
binary, multichotomous, ordered, and count dependent variables. Students are expected to participate in class, will
take a midterm and a final exam, will write a final research paper and present the results of their paper in class.
Students will use the STATA statistical package to conduct the analysis for their research papers.
IV. Student Learning Objectives/Outcomes
After successfully completing this class, students will be able to:
 Identify research questions and data characteristics that require the use of maximum likelihood estimation
techniques.
 Explain key concepts associated with MLE approaches to statistical analysis.
 Formulate a research question, theory, and a hypothesis that can be tested with appropriate data.
 Collect original data in a manner appropriate for use in statistical analysis using MLE techniques.
 Use STATA statistical software to analyze data.
 Relate statistical results to the research question guiding the research project.
 Present, in oral and written form, the results of an MLE analysis.
V. Textbooks and Materials
Greene, William H. 2008. Econometric Analysis, 6th Edition. Prentice Hall.
Hamilton, Lawrence C. 2009. Statistics With STATA. Cengage.
King, Gary. 1995. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. University of
Michigan Press.
Long, Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Sage Publications.
Several published research articles as identified in the Course Outline.
VI. Course Outline
Week 1: Introduction to MLE
Assignment: Locate a research article that uses maximum likelihood estimation, read and
summarize it, and bring it to class.
Week 1 Lab: Intro to STATA
Hamilton: Chapter 1
Week 2: Theory and Examples of MLE research
Greene: Chapter 16 and Appendix E.1-E.4.
King: Chapters 1-3.
Week 2 Lab: Datasets and Data Management
Hamilton: Chapter 2
Week 3: Binary Dependent Variables I: Logit and Probit
Greene: Chapter 23.1-23.4
King: Chapter 4
Long: Chapter 3
Week 3 Lab: Displaying Data Using Graphs
Hamilton: Chapter 3
Week 4: Binary Dependent Variables II: Scobit, Heteroskedastic Probit, Rare Events Logit
Alvarez, R. Michael. 1995. “American Ambivalence Towards Abortion Policy.” American
Journal of Political Science 39:1055-1082
Beck, Nathaniel, Gary King, and Langche Zeng. 2004. “Theory and Evidence in International
Conflict: A Response to de Marchi, Gelpi, and Grynaviski.” American Political Science Review
98:379-389.
Golder, Sona. 2006. “Pre-Electoral Coalition Formation in Parliamentary Democracies.” British
Journal of Political Science 36:193-212.
King, Gary, and Langche Zeng. 2001. “Explaining Rare Events in International Relations.”
International Organizations 55:693-715.
Nagler, Jonathan. 1991. “The Effect of Registration Laws and Education on U.S. Voter Turnout.”
American Journal of Political Science 85:1393-1405.
Nagler, Jonathan. 1994. Scobit: An Alternative Estimator to Logit and Probit.” American
Journal of Political Science 38:230-255.
Week 4 Lab: Displaying Data Using Tables
Hamilton: Chapter 4
Week 5: Multichotomous Dependent Variables I
Greene: Chapters 17 and 23.11
Long: Chapter 6
Week 5 Lab: Comparison Methods in STATA
Hamilton: Chapter 5
Week 6: Multichotomous Dependent Variables II
Alvarez, R. Michael, Jonathan Nagler, and Shaun Bowler. 2000. “Issues, Economics, and the
Dynamics of Multiparty Elections: The British 1987 General Election.” American Political
Science Review 94: 131-149.
Martin, Lanny and Randolph T. Stevenson. 2001. “Government Formation in Parliamentary
Democracies.” American Journal of Political Science 45:33-50.
Quinn, Kevin, Andrew Martin, and Andrew Whitford. “Voter Choice in Multi-Party
Democracies: A Test of Competing Theories and Models.” American Journal of Political Science
43:1231-1247.
Week 6 Lab: Regression Procedures
Hamilton: Chapter 6
Week 7: Midterm Exam
Week 8: Paper Workshop
Nagler, Jonathan. 1995. “Coding Style and Good Computing Practices.” The Political
Methodologist 6:2.
Students should come to class with a research question that they intend to use for their final paper.
We will discuss the paper ideas, and the practical issues of data collection and analysis, in class.
Week 8 Lab: Diagnostic Procedures
Hamilton: Chapter 7
Week 9: Ordered Response Models
Greene: Chapter 23.10
Long: Chapter 5
Gelpi, Christopher. 1997. “Crime and Punishment: The Role of Norms in Crisis Bargaining.”
American Political Science Review 91:339-360.
Week 9 Lab: Nonlinear Regression
Hamilton: Chapter 8
Week 10: Event Counts
Greene: Chapter 25.1-25.5
Long: Chapter 8
Gowa, Joanne. 1998. “Politics at the Water’s Edge: Parties, Voters, and the Use of Force
Abroad.” International Organization 52:307-324.
King, Gary. 1989. “Event Count Models for International Relations: Generalizations and
Applications.” International Studies Quarterly 33:123-147.
Week 10 Lab: Robust Models and Logistic Regression
Hamilton: Chapters 9 and 10
Week 11: Paper Workshop II
King, Gary, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of Statistical
Analyses: Improving Interpretation and Presentation.” American Journal of Political Science
44:341-355.
Students should come to class ready to discuss their STATA output.
Week 11 Lab: Event Count Models
Hamilton: Chapter 11
Week 12: Student Presentations I
Week 12 Lab: Principal Component, Factor, and Cluster Analysis
Hamilton: Chapter 12
Week 13: Student Presentations II
Week 13 Lab: Time Series
Hamilton: Chapter 13
Week 14: Extensions I: Time Series and Multiple Equation Models
King: Chapters 6-8
Week 14 Lab: Survey Data
Hamilton: Chapter 14
Week 15: Extensions II: Nonrandom Selection Models
King: Chapters 9-11
Papers Due
Week 15 Lab: Multilevel and Fixed Effects Models
Hamilton: Chapter 15
TBD: Final Exam
VII. Grading Policy
Grades will be calculated as follows:
Mid-term test: 25%
Final test: 25%
Oral Presentation: 15%
Research Paper: 25%
Class Participation: 10%
Grading scale: A: 90-100; B: 80-89; C: 70-79; D: 60-69; F: < 60
VIII. Academic Integrity
Every KSU student is responsible for upholding the provisions of the Student Code of Conduct, as published in the
Undergraduate and Graduate Catalogs. Section II of the Student Code of Conduct addresses the University's policy
on academic honesty, including provisions regarding plagiarism and cheating, unauthorized access to University
materials, misrepresentation/falsification of University records or academic work, malicious removal, retention, or
destruction of library materials, malicious/intentional misuse of computer facilities and/or services, and misuse of
student identification cards. Incidents of alleged academic misconduct will be handled through the established
procedures of the University Judiciary Program, which includes either an "informal" resolution by a faculty member,
resulting in a grade adjustment, or a formal hearing procedure, which may subject a student to the Code of Conduct's
minimum one semester suspension requirement.
IX. ADA Statement
Any student who, because of a disabling condition, may require some special arrangements in order to meet the
course requirements should contact the instructor as soon as possible to arrange the necessary accommodations.
Students should present appropriate verification from KSU disAbled Student Support Services. No requirement
exists that accommodations be made prior to completion of this approved University process. Accommodations are
arranged on an individualized, as-needed basis after the needs and circumstances have been evaluated. The
following individuals have been designated by the President of the University to provide assistance and ensure
compliance with the ADA. Should you require assistance or have further questions about the ADA, please contact:
Carol Pope, Asst. Dir. for disAbled Student Support Services
770-423-6443, 770-423-6667F, 770-423-6480TTY
cpope@kennesaw.edu
disAbled Student Support Services Website
http://www.kennesaw.edu/stu_dev/dsss/dsss.html