Overview
The speaker introduces a large-N dataset in international relations to examine how conflict type relates to death estimates using basic R linear modeling.
Large-N Data and Research Framing
- Large-N data: 1,848 observations; contrasts with single case studies.
- Substantive focus: US foreign policy, national security, international relations.
- Research question: Which conflict type yields more deaths on average?
- Variables:
- Independent variable: Conflict type (three categories).
- Dependent variable: Death estimate (numerical).
Conflict Types (Concepts and Examples)
- Internal: Government vs. rebel group (e.g., Colombia).
- Internationalized: Government vs. rebel group supported by an outside power (e.g., Sierra Leone; Nicaragua Contra War).
- Interstate: Conventional conflict between states (e.g., Ukraine and Russia).
Modeling Approach (R / RStudio)
- Environment: R (statistical language) with RStudio interface.
- Model: Linear model (lm) with dependent ~ independent.
- Specification: death_estimate ~ conflict_type, data = “National Security” dataset.
- Factor handling: Internal used as baseline because of alphabetical order.
Findings and Interpretation
- Overall significance: Conflict type significantly predicts death estimates.
- Coefficients (relative to internal baseline):
- Interstate: Large positive estimate; highest t-value; indicates largest increase.
- Internationalized: Positive estimate; increase relative to internal.
- Practical interpretation:
- Interstate conflicts have the highest average deaths.
- Internationalized conflicts also exceed internal in average deaths.
- All pairwise differences are statistically significant (p < .05).
Pairwise Comparisons (AMeans Function)
- All conflict-type pairs differ significantly (p < .05).
- Sign directions:
- Internal vs. Internationalized: Negative for internal baseline; fewer deaths than internationalized.
- Internal vs. Interstate: Negative for internal baseline; fewer deaths than interstate.
- Internationalized vs. Interstate: Negative for internationalized baseline; interstate has more deaths.
- Policy relevance: Interstate conflicts tend to entail markedly higher death tolls.
Model Output Highlights (Quantitative Details)
- Reported estimates (relative to internal):
- Interstate ≈ +4,346 average deaths.
- Internationalized ≈ +1,430 average deaths.
- T-values: Very high for interstate; both positive coefficients significant.
Structured Summary
| Conflict Type Comparison | Direction vs. First Listed | Relative Deaths | Statistical Significance |
|---|
| Internal vs. Internationalized | Internal lower | Internationalized > Internal | p < .05 |
| Internal vs. Interstate | Internal lower | Interstate > Internal | p < .05 |
| Internationalized vs. Interstate | Internationalized lower | Interstate > Internationalized | p < .05 |
| Internal (baseline) → Internationalized (coef) | Positive | ≈ +1,430 | Significant |
| Internal (baseline) → Interstate (coef) | Positive | ≈ +4,346 | Significant |
Key Terms & Definitions
- Large-N dataset: Data with many observations enabling statistical inference.
- Independent variable: Predictor; here, conflict type.
- Dependent variable: Outcome; here, death estimate.
- Linear model (lm): Regression estimating outcome as a function of predictors.
- Baseline (reference) category: Factor level against which others are compared.
- T-value: Test statistic assessing coefficient significance.
- Standard error: Measure of estimate precision.
- p-value: Probability indicating statistical significance (commonly p < .05).
Action Items / Next Steps
- Replicate lm model with death_estimate ~ conflict_type using the provided dataset.
- Review factor levels to confirm baseline and interpret coefficients correctly.
- Examine model diagnostics for assumptions and robustness.
- Extend analysis: Control for region, time, or conflict duration if available.
- Translate findings to policy memos on expected lethality by conflict type.