Fig. 3: False positive rates for different methods of controlling for spatial non-independence in our simulation study.
From: Cross-national analyses require additional controls to account for the non-independence of nations
For simulated outcome and predictor variables, we systematically varied the strength of spatial autocorrelation from weak (0.2) to moderate (0.5) to strong (0.8). We simulated 100 datasets per parameter combination and fitted different models to each dataset. False positive rates were operationalised as the proportion of models that estimated a slope with a two-tailed 95% confidence/credible interval excluding zero. Points represent raw proportions of false-positive models, ranges represent two-tailed 95% bootstrap confidence intervals (n = 1000 bootstrap samples), and dashed lines indicate the 5% false positive rate that is expected due to chance. Colours indicate whether the strength of autocorrelation for the predictor variable is 0.2 (red), 0.5 (green) or 0.8 (blue). SEs standard errors.
