Cases, Numbers, Models: International Relations Research Methods(Ch.6-9)

Summary

Quantitative Approaches to International Relations

Case Study of Research Design in the International Political Economy

Case Study of Research Design in International Environmental Policy

Case Study of Research Design in International Security Studies

Empirical-Quantitative Approaches to the Study of International Relations

Why Quantitative Analysis? Allows inferences about reality using the law of probability.

How? Through large aggregate of cases your able to draw relationships between elements and check if the relationship is by chance or purposeful.

Basic Statistical Definitions & Tools

Linear Correlation- r Multiple Regression- R Squared P-Value Analysis of Variance- ANOVA

Linear Correlation

The Correlation Coefficient: Definition Bruce Ratner, Ph.D.

The correlation coefficient, denoted by r, is a measure of the strength of the straight-line or linear relationship between two variables. The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient: 

0 indicates no linear relationship. +1 indicates a perfect positive linear relationship: as one variable increases in its

values, the other variable also increases in its values via an exact linear rule. -1 indicates a perfect negative linear relationship: as one variable increases in its

values, the other variable decreases in its values via an exact linear rule. Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear

relationship via a shaky linear rule. Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative)

linear relationship via a fuzzy-firm linear rule. Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative)

linear relationship via a firm linear rule.

Multiple Linear Regression or R squared

The value of r squared is typically taken as “the percent of variation in one variable explained by the other variable,” or “the percent of variation shared between the two variables.”

Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.

Reliability: Probability Value or P-Value

A p-value is a statistical value that details how much evidence there is to reject the most common explanation for the data set. It can be considered to be the probability of obtaining a result at least as extreme as the one observed, given that the null hypothesis is true.

Theory First!

Theory should determine the research design, not vice versa.

The Hypothesis and the operationalization of variables should drive the methodology

Advantages

The Ability not just to describe association among phenomena but to calculate the probabilities that such associations are the product of chance

The ability to gain a better understanding of the sources of human behavior in international affairs

Disadvantages: Error of Specification and Error of Inference

Errors of Specification: 3 Types of Errors 1. Too much effort calculating

correlations with little or no attention to theory

2. Theory itself often is weak and difficult to test because it is too imprecise or too shallow

3. Empirical researchers often impose a statistical model on the theory instead of crafting a model to test the theory

Disadvantages: Error of Specification and Error of Inference

Errors of Inference: 1. Overemphasis in statistical significance while neglecting

substantive significance 2.Small Sample Size 3. Single Test Bias rather than multiple testing for reliability 4. Lakatos View: Your it till I find something better vs. Bayesian View-Cumulation of results 5.Garbage Can Models: Too many variables, attempt to

limit the variables 6.Computer Error

Case Study of Quantitative Approaches to the International Political Economy

The Effects of Hegemony on TradeThe Effects of Alliances, PTA, and TradeThe Effects of Political Conflict on Trade

Increase of Quantitative Studies in the International Political Economy Subfield

1970: 20% of Re-search in the IPE used Quantitative

Methodology

Other Research MethodsQuant.

1980: 25% of Re-search in the IPE used Quantitative

Methodology

Other Research Methods

Quant.

Increase of Quantitative Studies in the International Political Economy Subfield

1990: 45% of all research in the IPE used Quantitative Methodology

Other Research MethodsQuant.

Case Study of Hegemony on Trade

Problem: How do you define, and operationalize Hegemony?

Many have tried and failed to reject the Null Hypothesis: There is no relationship between Hegemony and Trade

Until the definition of Hegemony was operationalized by viewing Benevolent and Malign Hegemony, and viewing the effect of alliances in Bi-polar and Multi-polar environment

Reaffirming that Theory leads the Research Method

Case Study of Alliances, PTA, and Trade

PTA/Alliance

Yes PTA/No Ally

No PTA/ Yes Ally

0 20 40 60 80 100

120

140

Increased Trade with Non-Major Powers in Percentage

Increased Trade with Non-Major Powers in Percentage

Case Study of Alliances, PTA, and Trade Cont.d

PTA/Alliance

Yes PTA/No Ally

No PTA/ Yes Ally

0 20 40 60 80 100

120

140

Increased Trade with Major Powers in Percentage

Increased Trade with Major Powers in Per-centage

The Effects of Conflict and Trade

Gravity Model of Distance and Trade with added variable for Diplomatic Relations

Results: Cooperation stimulates trade; Threats had no statistical significance; War hampers trade

Case Study of Research Design in Int’l Environmental Policy

5 Central Themes of Research:

The effect of economic development(IV), abatement costs(IV), and democracy(IV) on the pollutions patterns(DV)

The effect of growing trade(IV) on environmental degradation(DV)

The effect of regulatory issues(IV) on the environment(DV)

The relationship between environmental factors(IV) and violent conflict(DV)

The formation of effectiveness of international regimes(IV) and environmental degradation(DV)

Kuznet’s Curve

Common Methodological Challenges

Larger and more comprehensive datasets relevant to International Environmental Policy are needed

Small Sample Sizes making it difficult to ascertain reliability of studies

Problem of conceptual consolidation: How do you unify different concepts of resource expenditures and problem-solving models

Measuring Effectiveness

Measuring Regime Effectiveness: Helm & Sprinz

Case Study of International Conflict

Four Stages of International Disputes: Dispute Initiation Stage Challenge the Status Quo Stage Negotiation Stage Military Escalation Stage

4 Stages of International Disputes

Stage 1: Dispute Initiation

Stage 2: Challenge the Status Quo

Stage 3: Negotiations Stage

Stage 4: Military Escalation Stage

Problems with quantitative analysis of Int’l Conflict

Appropriate Measurements, which unit of analysis to use, and mode of analysis: Cross-sectional time series

Selection Bias: one solution stratified random sampling using both conflict and non-conflict variables

Non-Independent observations Inadequate Measurements-Solutions by Stage: Military Balance measure Dyadic Analysis

Resources

https://controls.engin.umich.edu/wiki/index.php/Basic_statistics:_mean,_median,_average,_standard_deviation,_z-scores,_and_p-value