OPOSSEM OPOSSEM

Winter 2012 Instructor: Dr. Michelle Dion, dionm@mcmaster.ca Office: KTH 533, 905-525-9140, ext. 24029 Office hours: Mondays, 10:30-12:20 (no appt nec’y) & other times by appointment (email for an appt)

Course introduction and objectives

This is an introductory graduate course in empirical research and statistical methods. For MA students, the intention is to provide you with basic statistical skills and familiarity for use on the job market. For PhD students, the goal is to provide a foundation for more advanced coursework or applications in your research. For some of you, the material presented in this course will be the beginning of a radically new way to approach research. To be successful in the course, you will NOT need to be a mathematician or statistician, but you will need a desire to learn, to think analytically, to solve problems, and be open to new ways of thinking. You will also need some basic algebra skills.

After a brief overview of qualitative comparative methods, the course will provide an introduction to basic statistical methods in the social sciences through linear and logistic regression. The emphasis will be on successful application of statistical methods and understanding the uses of such methods for public policy and political science. To gain experience in applying statistical analysis, you will complete a series of homework assignments and an independent research project employing linear regression. Student attendance and participation in class is required and will constitute a significant portion of final grades.

A copy of this syllabus and a list of related web-based resources for the course can be found at the Avenue site (http://avenue.mcmaster.ca), which will be expanded throughout the course.

Readings

The required textbooks for this course can be purchased at Titles. Copies have been ordered for the reserves of the library, though there is no guarantee that they will be available by the beginning of the semester. Additional readings assigned will be available through the library’s journal subscriptions or Avenue.

Required textbooks:

Joseph F. Healey and Steven Prus. 2010. Statistics: A Tool for Social Research, 1st Canadian Edition. Nelson Education (ISBN: 0176442537) William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press. (ISBN: 0813399718)

Reference or alternative textbooks on reserve at the library (in order from basic to advanced)

Neil J. Salkind. 2004. Statistics for People who (Think They) Hate Statistics. 2nd edition. Sage. ISBN 076192776 (covers most of material in class) Alan Agresti and Barbara Finlay. 1997. Statistical Methods for the Social Sciences, 3rd edition. Upper Saddle River, NJ: Prentice Hall. (ISBN: 0135265266) Earl Babbie. The Practice of Social Research, 9th ed. Wadsworth. ISBN: 0534574742. (covers class material and qualitative methods, like survey design) Janet Buttolph Johnson and H.T. Reynolds. Political Science Research Methods, 5th ed.CQPress. ISBN: 1568028741. (covers class material, qualitative methods, and general research strategies) Damodar N. Gujarati. Basic Econometrics. 4th ed. ISBN: 0072478527 (solid basic introduction to regression and some advanced topics) William H. Greene. Econometric Analysis. 4th ed. Prentice Hall. ISBN: 0130132977. (advanced econometric text that covers most advanced methods in more detail than Gujarati)

Recommended software

Statistical software will be required for several homework assignments and your final projects. The lab in the basement of KTH has SPSS available. SPSS is one of the most widely used software packages outside of academia (second only to SAS) and has a very intuitive and user-friendly interface. The textbook includes information on using SPSS. A student version of SPSS is available at Titles bookstore. There are some limitations of the student version, but it will be suitable for the homework assignments and nearly everyone’s project. [Limitations will be discussed at the first class meeting.]

Student evaluation

Class participation, attendance, and pop quizzes, 15%. To get the most out of our class meetings and to be able to participate actively, you must have done the reading prior to class and you must attend class regularly. (Indeed, the norm in graduate school is that you attend every class.) Asking thoughtful or insightful questions is just as important as answering questions posed by others in the class. I also reserve the right to give in class pop quizzes on the assigned readings. Participation through Avenue will also count positively toward your participation grade. This includes asking questions and posting answers to others’ questions. Absences, tardiness, and cell phone disruptions will adversely affect your participation grade. All electronic devices should be turned off during class (including laptops, cell phones, etc.).

Homework assignments, 25%. Homework assignments will be assigned throughout the semester to be reviewed during class. Students are allowed to discuss and work together on homework assignments. However, each student must turn in their own work and generate their own software outputs/results. The relative weights of each homework assignment are listed in the table below. No late homework assignments will be accepted.

Final research project, 60% total. Half of your course grade in this course will be based upon your completion of an original research project using quantitative data and linear regression. The project will proceed in phases to give you guidance and feedback throughout the research process. The final product of your research project will be presented in a poster session the week after the end of classes. Other graduate students and faculty from the department will be invited to attend. Students are strongly encouraged to discuss their research projects with me early and often to make sure the projects meet the assignment’s requirements and are feasible. Your final research project will proceed in phases:

1. Statement of research question with clear identification of dependent variable (5%)
2. Description of research hypotheses and bibliography (10%)
3. Diagram of research design (5%)
4. Description of data and sources bibliography (10%)
5. Description of analysis and results (10%)
6. Final poster with results (20%)

Timetable of ALL due dates:

(note: there is no Homework #2)

January 12	Homework 1 (Article) (1x)
January 19	Statement of research question w/clear identification of dependent variable (5%)
February 9	Description of research hypotheses and bibliography (10%)
February 16	Homework 3 (Descriptive stats and SPSS) (2x)
February 16	Diagram of research design (5%)
March 1	Homework 4 (Tests of means and proportions, and SPSS) (2x)
March 8	Description of data and sources bibliography (10%)
March 15	Homework 5 (Association, regress, and SPSS) (2x)
March 22	Description of analysis and results (10%)
March 29	Homework 6 (Critique of regression article) (2x)
April 9	Final poster with results (20%) in Student Center

Course Policies

This syllabus is tentative and subject to change. Students are responsible for finding out about announced changes if they miss class.

Late project assignments.

Assignments related to the final project are due at the beginning of class on the dates outlined above; no late homework assignments will be accepted. Project assignments turned in at the end of class or within one hour of the end of class will only be eligible for 95% of the total value. Assignments turned after class but within 24 hours of class will be eligible for a maximum grade of B+. Assignments received after 24 hours of the due date will be eligible for a maximum grade of C+. Late project assignments will not be accepted after 48 hours after the original due date. If you anticipate having problems meeting these deadlines, please contact me before the assignment is due to discuss your situation. To avoid late penalties and ensure fairness, written documentation of your emergency will be required.

Honor Code.

By submitting written homework assignments and your final paper, you are pledging that you have not received unauthorized aid on the paper or exam. While you may discuss homework assignments with other students, you must generate your own output and write up your own answers. If computer analysis is required for an assignment, you must analyze your own data separately from your peers. While you are encouraged to discuss your papers with peers and the instructor, you must be the only author of your paper and written assignments. This means that though you may discuss an assignment with peers, the write-up should be done alone and separate from them. Meet to discuss the assignment, then go your separate ways to write up your answers. All references to or paraphrasing of course readings or outside readings must be properly documented to avoid plagiarism. If you have any doubts, please ask me before turning in the assignment.

Special needs.

Students with documented special needs will be accommodated as much as possible. Please see me in the first few weeks of the semester if you anticipate needing special accommodations.

Missed classes.

Regular attendance is crucial to your success in this course and is expected of all graduate students. Attendance is incorporated into your participation grade, which is a substantial portion of your final grade (15%). In the past, students who have missed even one class have had trouble catching up with the material, and students who have missed more than one class usually have had significant trouble completing the final project to their satisfaction. Students will not have access to my class notes for missed classes.

McMaster statement on academic integrity.

Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. It is your responsibility to understand what constitutes academic dishonesty. However, if you have questions regarding a particular assignment, it is always best to ask me prior to completing the assignment. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at http://www.mcmaster.ca/academicintegrity The following illustrates only three forms of academic dishonesty: 1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained. 2. Improper collaboration in group work. 3. Copying or using unauthorized aids in tests and examinations.

McMaster statement on electronic resources.

In this course, we will be using the Avenue2Learn site (avenue.mcmaster.ca). Students should be aware that, when they access the electronic components of this course, private information such as first and last names, user names for the McMaster e-mail accounts, and program affiliation may become apparent to all other students in the same course. The available information is dependent on the technology used. Continuation in this course will be deemed consent to this disclosure. If you have any questions or concerns about such disclosure please discuss this with me.

McMaster statement on course modification.

The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all courses in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check his/her McMaster email and course websites weekly during the term and to note any changes.

McMaster Faculty of Social Sciences statement on email.

Effective September 1, 2010, it is the policy of the Faculty of Social Sciences that all e-mail communication sent from students to instructors (including TAs), and from students to staff, must originate from the student’s own McMaster University e-mail account. This policy protects confidentiality and confirms the identity of the student. It is the student’s responsibility to ensure that communication is sent to the university from a McMaster account. If an instructor becomes aware that a communication has come from an alternate address, the instructor may not reply at his or her discretion. Email Forwarding in MUGSI: http://www.mcmaster.ca/uts/support/email/emailforward.html See also: http://mail.google.com/support/bin/answer.py?answer=21289 and http://mail.google.com/support/bin/answer.py?answer=22370

Course Outline

• • Reading available online through Avenue

$$ Reading available online through the library

January 5—Introduction Introduction to the course and each other.

Review of syllabus. Avenue & online resources. Discuss distributed articles. Assign Homework 1. Christopher H. Achen. “Advice for Students Taking a First Political Science Graduate Course in Statistical Methods” Recommended:

• • Freeman, Donald M. “The Making of a Discipline.” In Political Science Volume 1: Theory and Practice of Political Science, William Crotty, editor. Northwestern University Press.
  • Kuhn, Thomas. “A Role for History and Progress through Revolutions” from Methods for Political Inquiry, Stella Z. Theodoulou and Rory O’Brien, editors.
  • New York Observer Staff. 2002. “How Cult Internet Character Mr. Perestroika Divided N.Y.U.'s Political Science Department.” The New York Observer, January 6. http://www.observer.com/print/45441. Accessed November 7, 2010.

$$ Laitin, David D. 2003. “The Perestroikan Challenge to Social Science.” Politics & Society 31, 1 (March): 163-184. $$ Flyvbjerg, Bent. 2004. “A Perestroikan Straw Man Answers Back: David Laitin and Phronetic Political Science.” Politics & Society 32, 3 (September): 389-416.

January 12—Qualitative comparative method

• • Lieberson, Stanley. 1992. “Small N’s and big conclusions: an examination of the reasoning in comparative studies based on a small number of cases.” In What is a case?, Ragin and Becker, eds.

$$ Geddes, Barbara. 1990. “How the Cases you Choose Affect the Answers You Get.” Political Analysis, 2: 131-150.

• • Collier, David, James Mahoney, and Jason Seawright. 2004. “Claiming Too Much: Warnings about Selection Bias.” In Rethinking Social Inquiry: Diverse Tools, Shared Standards, Henry E. Brady and David Collier, eds. New York: Rowman and Littlefield Publishers, Inc.

$$ Kay, Stephen. 1999. “Unexpected Privatizations: Politics and Social Security Reform in the Southern Cone.” Comparative Politics, 31, 4 (July). Homework 1 due. Recommended: King, Gary, Robert Keohane, and Sidney Verba. 1994. Designing Social Inquiry. Princeton UP. Brady, Henry E. and David Collier, eds. 2004. Rethinking Social Inquiry. Rowman and Littlefield. Ragin, Charles C. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. Berkeley, CA: University of California Press. (Chapters 1-5)

January 19—Quantitative inferential statistics (Where we are going)

Chapter 1 in Healey and Prus. Chapter 1 (Introduction) in William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press. 

• • Taylor, Mark Zachary. 2007. “Bivariate & Multivariate Regressions: A Primer.” Sam Nunn School of International Affairs, Georgia Institute of Technology, unpublished paper.
  • excerpt from Freakanomics

$$ Michelle Dion and Catherine Russler. 2008. “Eradication Efforts, the State, Displacement and Poverty: Explaining Coca Cultivation in Colombia during Plan Colombia.” Journal of Latin American Studies, 40, 3, August . $$ Darrin W. Davis and Brian D. Silver. 2004. “Civil Liberties vs. Security: Public Opinion in the Context of the Terrorist Attacks on America.” American Journal of Political Science, 48, 1. Statement of research question w/dependent variable.

January 26—Statistics for the social sciences

Chapters 2-5 in Healey and Prus. Assign Homework 3 (Descriptive stats and SPSS)

February 2

Chapters 6-7 (estimation/confidence intervals) in Healey and Prus.

February 9

Chapters 8-9 (Tests of Significance) in Healey and Prus. Description of research hypotheses and bibliography due. 

February 16

Chapters 10-11 (Tests of Significance and Measures of Association) in Healey and Prus. Homework 3 due Diagram of research design due Assign Homework 4 (tests of means and proportions; book and SPSS) 

February 23

reading week, no class

March 1

Chapters 12 [skim]; 14 [focus on overview & Gamma]; and 15 [skip Ch 13] in Healey and Prus. Chapter 2 (Bivariate regression model) [and review Chapter 1 if necessary] in William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press. Homework 4 due. Assign Homework 5 (association and regression; SPSS)

March 8

Bivariate correlation and multivariate regression Chapter 16-17 (Correlation and Linear Regression) in Healey and Prus. Chapter 3 (The multivariate regression model) in William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press.

• • Chapter 5 (Nonlinear and logarithmic models and dummy and interaction variables) in Leo Kahane. 2007. Basic Regression, Sage.

Description of data and sources bibliography due.

March 15

Chapters 4-5 (Evaluating regression results, Some illustrations of multiple regression) in William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press.

• • Chapter 7 (Some common problems in regression analysis) in Leo Kahane. 2007. Basic Regression, Sage.

$$ Dion and Russler article (repeat) In-class critique of regression article. Discussion of model choice/data issues for projects. Homework 5 due

March 22

Chapter 6 (Advanced topics) in William D. Berry and Mitchell S. Sanders. 2000. Understanding Multivariate Research: A Primer for Beginning Social Scientists. Boulder: Westview Press.

• • Chapter 15 (Logistic regression) in Agresti and Finlay Statistical Methods for the Social Sciences.

Data results due Assign Homework 6 (article critique)

March 29

Return project results. Discuss revisions/problems. Homework 6 due

Monday, April 09

Poster Session McMaster Student Center