Methods and Research Design in International Relations
This is an introductory course that aims to familiarize students with the basics of research design and quantitative (large-N) analysis. This involves analyzing and evaluating statistical data with a view toward addressing contemporary social science questions. For those who do not intend to use statistics in their own research, this course is also useful for people who desire a basic literacy so that they can read books and articles that utilize statistical techniques. The first several sessions will cover the basics of theory and research design, including how to (1) identify a viable research question; (2) formulate a theory and derive hypotheses; and (3) conceptualize and measure variables. One seminar will be devoted to an overview of qualitative (small-N) methods. The remainder of the course will cover the fundamentals of statistical analysis in order to give students an understanding of how statistics can be used in connection with political research. Four of the sessions will be held in a computer lab, where students will practice working with and analyzing an actual dataset using SPSS (Statistical Package for the Social Sciences) software for Windows. By the end of the course, students should be able to distinguish between theories and hypotheses; analyze and interpret statistical results; present data in graphical form; and perform basic statistical analysis using SPSS.
Joseph F. Healey, Statistics—A Tool for Social Research (Belmont, CA: Wadsworth Publishing, 1996).
Jane Fielding and Nigel Gilbert, Understanding Social Statistics, (London: Sage Publications, 2000).
Stephen Van Evera, Guide to Methods for Students of Political Research (Ithaca, NY: Cornell University Press, 1997).
Zina O’Leary, The Essential Guide to Doing Research (London, Thousand Oaks, New Delhi: Sage Publications, 2004).
Laurence F. Jones and Edward C. Olson, Researching the Polity: A Handbook of Scope and Methods (Cincinnati, OH: Atomic Dog Publishing, 2005).
SPSS Instruction Manual, Department of Statistics and Actuarial Science, University of Waterloo, September 1, 1998.
Short readings will be assigned for each class. The course grade is broken down as follows:
(1) Problem Sets (80%). Students will be expected to complete problem sets every week, which involve simple numeric computations and (in some cases) very short essays. Students are asked to show their work in these assignments—partial credit will be given to incorrect answers if the basic work is correct. Please note that it is important to do your own work: there are strict penalties for copying problem sets.
(2) Participation (20%). Students are expected to attend all seminars and actively contribute to class discussions.
Seminar 1: Research Question and Literature Review (O’Leary, pp. 28-41; 66-84)
Seminar 2: Elements of Research Design (Buttolph and Johnson, pp. 33-60)
Hypotheses and Theories
Independent, Intervening, Dependent Variables
Data, Variables, Values
Examples from International Relations
Seminar 3: Overview of Qualitative Research (Van Evera, pp. 49-70)
Qualitative versus Quantitative Analysis
The Comparative Method
Seminar 4: The Basics of Data Analysis (Healey, pp. 7-9; Jones and Olson, pp. 181-194
Descriptive v. Inferential Data Analysis
Measuring Variables (validity, reliability, replicability)
Types of Variables (nominal, ordinal, interval)
Common Terms (dataset, population sample, parameter, statistic)
Misuses of Data (examples)
Seminar 5: Univariate (Descriptive) Statistics (Jones and Olson, pp. 247-267)
Sample Size (N)
Measures of Central Tendency and Dispersion
Means, medians, modes
Variance, standard deviation
Seminar 6 (LAB): Introduction to SPSS for Windows (Jones and Olson Workbook, pp. 1-9, 139-148)
Starting an SPSS Session
Creating a New Dataset
Using an Existing Dataset
Manipulating and Merging Datasets
Importing and Exporting Data
Descriptive Statistics in SPSS (mean, standard deviation, variance, range, frequencies)
Seminar 7 (LAB): Manipulating Data in SPSS (Jones and Olson Workbook, pp. 11-17)
Recoding and Transforming Variables
Graphs and Charts
Box Plots and Other Charts
Printing and Saving Output
Seminar 8: Probabilities and Sampling (Healey, pp. 116-124; 138-147)
Binomial and Normal Random Variables
The Meaning of “Normal”
Using the Normal Table
Methods of Sampling
Seminar 9: Hypothesis-testing (Healey, pp.152-168; 173-195)
Null and Alternative Hypotheses
One and Two-Tailed Tests
Seminar 10: Bivariate Correlation and Regression (Fielding and Gilbert, pp. 161-182)
Introduction to Bivariate Analysis
Govariance and the Correlation Coefficient
Graphing the Function
Regression and the Method of Ordinary Least Squares (OLS)
Interpreting Regression Statistics (Beta Coefficient and R-Squared)
Seminar 11 (LAB): Bivariate Regression Analysis in SPSS (Fielding and Gilbert, pp. 182-197)
Bivariate Regression Analysis
Interpreting the Statistics
Presenting the Data
Seminar 12 (LAB): Multivariate Regression Analysis in SPSS (Jones and Olson, pp. 307-321)
Introduction to Multivariate Regression Analysis
When to use Multivariate Regression
Goodness of Fit (R-squared statistic)
Interpreting the Statistics