Methods and Research Design in International Relations

Course Objectives:

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.

Course Books:

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.

Course Requirements:

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.

COURSE SCHEDULE

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

Congruence Procedures

Process-tracing

Crucial Cases

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)

Range

Frequency Distributions

Histograms

Other Charts

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

Printing Datasets

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

Scatterplots

Histograms

Box Plots and Other Charts

Cross-tabulations

Printing and Saving Output

Seminar 8: Probabilities and Sampling (Healey, pp. 116-124; 138-147)

Binomial and Normal Random Variables

The Meaning of “Normal”

Z-scores

Using the Normal Table

Other distributions

Methods of Sampling

Systematic Sampling

Random Sampling

Sampling Error

Seminar 9: Hypothesis-testing (Healey, pp.152-168; 173-195)

Confidence Intervals

Estimation Procedures

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)

Correlations

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

Control Variables

Goodness of Fit (R-squared statistic)

Interpreting the Statistics