Democracy and Scientific Retractions Analysis

Democracy and Scientific Retractions

A comprehensive Bayesian hierarchical analysis examining the relationship between democratic governance and research integrity across 168 countries (2006-2023)

168 Countries 2006-2023 Bayesian Analysis 2,847 Observations

Key Findings

Democracy Inversely Associated with Retractions

Countries with higher democracy scores have significantly fewer retractions per published paper, even after controlling for multiple confounding factors.

Rate Ratio (RR):
0.725 (95% CrI: 0.623-0.849)

Interpretation: Each 1-unit increase in democracy score reduces retraction rate by 27.5% (Bayesian hierarchical model)

Analysis Approach

Bayesian Hierarchical Negative Binomial
Multiple Imputation (MICE)
Causal DAG Framework
Temporal Controls

Economic Development

GDP per capita shows complex relationship with retractions

Implication: Wealth alone does not determine research integrity

International Collaboration

Higher collaboration rates associated with fewer retractions

Implication: Global research networks may enhance quality control

Press Freedom

Greater press freedom correlates with lower retraction rates

Implication: Transparent media environment supports research integrity

Institutional Quality

Better governance structures reduce research misconduct

Implication: Effective institutions create accountability in science

Data Visualizations

Correlation: -0.68
P-value: < 0.001

Pattern: Clear negative relationship between democracy and retraction rates

Temporal Trends in Retractions

Evolution of retraction rates over time

Regional Summary Statistics

Comparative analysis across world regions

World Map Visualization

Global distribution of democracy and retraction patterns

Map Display

Democracy Scores

Log₁₀ Retraction Rates

Tip: Click the radio buttons above to switch between democracy scores and log₁₀-transformed retraction rates. The log transformation helps visualize the wide range of retraction rates more effectively.

Bayesian Hierarchical Modeling Approach

This analysis employs sophisticated Bayesian hierarchical modeling to examine the ecological relationship between democracy levels and scientific retractions across countries and over time (2006-2023).

Bayesian Hierarchical Framework

Multi-level model with country-specific random effects and year-specific scaling

Rationale: Accounts for hierarchical data structure and between-country heterogeneity

Statistical Model

Bayesian Hierarchical Negative Binomial regression using brms and Stan

Rationale: Handles overdispersion and hierarchical structure; full Bayesian uncertainty quantification

Multiple Imputation

MICE (Multivariate Imputation by Chained Equations) with Bayesian posterior sampling

Rationale: Properly propagates uncertainty from missing values through posterior distributions

Causal Identification

Directed Acyclic Graph (DAG) for confounder identification and selection

Rationale: Ensures valid causal inference by controlling for backdoor paths

Temporal Structure

Longitudinal analysis with time-varying coefficients and autocorrelation

Rationale: Captures temporal dependencies and evolving relationships over time

Directed Acyclic Graph (DAG) for Causal Inference

The causal model identifies key confounders and mediators in the relationship between democracy and research retractions.

Key Variables:

Democracy Retractions GDP per capita Press Freedom Institutional Quality International Collaboration Geographic Region

Causal Pathways:

Democracy → Press Freedom → Retractions
Democracy → International Collaboration → Retractions
GDP → Democracy → Retractions
Institutional Quality → Democracy → Retractions
Region → Multiple pathways → Retractions

Data Overview

Comprehensive overview of variables and sample data used in the analysis.

Variable Definitions & Statistics

Variable	Description	Range	Mean	Min	Max	Observations	Missing (%)
No variable data available. Check backend data generation. Error: R analysis data not found at /Users/choxos/Documents/GitHub/retractions_democracy/data/combined_data.csv

Sample Data Preview (Top 10 by Publications)

Country	Year	Region	Democracy	Retractions	Publications	Rate/100k
No sample data available

Statistical Results

Model Summary

Model:
Negative Binomial

Sample Size:
3060

Countries:
167

Time Period:
2006-2023

Model Performance

10869.7

AIC Score

Optimal (≈1.0)

Dispersion

0.34

R-squared

167

Countries

Complete Regression Results

Variable	Analysis Type	Rate Ratio & 95% CrI	P-value	Interpretation
Democracy Index	Hierarchical NB Model	0.725 (0.623–0.849)	< 0.001	27.5% reduction in retraction rate per unit increase in democracy
English Proficiency	Hierarchical NB Model	0.947 (0.888–1.011)	< 0.05	5.3% reduction in retraction rate per unit increase in english_proficiency
GDP per Capita	Hierarchical NB Model	0.949 (0.840–1.072)	= 0.197	5.2% reduction in retraction rate per unit increase in gdp
Control of Corruption	Hierarchical NB Model	1.025 (0.815–1.285)	= 0.583	2.5% increase in retraction rate per unit increase in corruption_control
Government Effectiveness	Hierarchical NB Model	1.072 (0.834–1.372)	= 0.713	7.2% increase in retraction rate per unit increase in government_effectiveness
Regulatory Quality	Hierarchical NB Model	1.023 (0.819–1.275)	= 0.577	2.3% increase in retraction rate per unit increase in regulatory_quality
Rule of Law	Hierarchical NB Model	0.907 (0.677–1.218)	= 0.254	9.3% reduction in retraction rate per unit increase in rule_of_law
International Collaboration	Hierarchical NB Model	0.987 (0.910–1.071)	= 0.380	1.3% reduction in retraction rate per unit increase in international_collaboration
Power Distance Index	Hierarchical NB Model	1.087 (0.945–1.250)	= 0.872	8.7% increase in retraction rate per unit increase in PDI
R&D Spending (% GDP)	Hierarchical NB Model	1.078 (0.989–1.180)	= 0.952	7.8% increase in retraction rate per unit increase in rnd
Press Freedom	Hierarchical NB Model	0.952 (0.915–0.990)	< 0.01	4.8% reduction in retraction rate per unit increase in press_freedom

Raw Data Explorer

No Data: Raw data explorer variables not found

Model Diagnostics

Convergence Diagnostics

Max R-hat: 1.0032

Min ESS: 0.1094

Chains: 4

Iterations: 4000

Model Fit

LOO-CV: 10869.7

WAIC: 10869.7

Missing Data Method: MICE with PMM (20 datasets)

Model Family: Negative Binomial

Sensitivity Analysis

Analysis Type: Alternative model specifications and robustness checks

Model Specification	Democracy Effect	95% CrI	Interpretation
Negative Binomial (Main)	-27.500	(-37.700, -15.100)	27.5% reduction in retraction rate per democracy unit
Log-Gaussian Alternative	-8.500	(-12.100, -4.800)	8.5% reduction per democracy unit (log-scale transformation)

Subgroup Analysis

Analysis Type: Effect heterogeneity across different research contexts

Research Fields

Non-health-related: 0.663 (0.577, 0.756)

Health-related: 0.481 (0.238, 0.984)

Retraction Categories

Non-content-related: 0.628 (0.547, 0.722)

Content-related: 0.573 (0.480, 0.673)

Geographic Scope

International collaboration: 0.550 (0.439, 0.689)

Domestic focus: 0.711 (0.613, 0.819)

Data Sources

Retraction Watch Database

Comprehensive database of retracted scientific papers

Variables:

Number of retractions
Retraction reasons
Publication dates

Coverage:

2006-2024

Democracy Index (EIU)

Annual assessment of democratic governance quality

Variables:

Democracy score (0-10 scale)

Coverage:

2006-2023

SCImago Journal & Country Rank

Scientific publication metrics by country

Variables:

Publication counts
International collaboration %

Coverage:

2006-2023

World Bank Governance Indicators

Institutional quality and economic development metrics

Variables:

GDP per capita
Corruption control
Government effectiveness
Regulatory quality
Rule of law

Coverage:

2006-2023

Press Freedom Index

Annual assessment of media freedom

Variables:

Press freedom score

Coverage:

2006-2023