# How to compare the sensitivity of many countries?

My thesis question is asking whether biodiversity sensitivity differs geographically. I will assess all 5 pressures individually, but take 1 (pollution) as an example.

My data contains time series for how biodiversity and pollution change in each country, over time.

I ran a linear model, lm(Biodiversity~Pollution), for each of 180 countries, and for the countries demonstrating a significant effect (158 countries), pulled out the coefficient of the gradient as a 'sensitivity score', representing how sensitive that country's biodiversity is, to pollution. I want to compare the countries and see whether they are different. I'm thinking about grouping the sensitivity scores (coefficients of gradient) into continents, then using an ANOVA to test whether the sensitivity scores (Gradients) are different between continents. Would the use of gradients in an ANOVA this way be viable?

Upon looking online, I saw some other threads suggesting that instead of doing a model for each country, to do one model and include country as a dummy variable. Would this work for my situation?

• Yes, it would be much better to put all of this in a single model - not just all countries, but all 5 pressures too. And to account for spatial autocorrelation in this single model. If you provide more detail about your pollution model (and perhaps some data?), you may get a more useful answer here.
– mkt
Jul 29 at 5:18
• Just to provide more information on the model - I'm using a very basic model for the pressures, just a linear model with one independent variable (pollution) because the purpose isn't to be able to use pollution to predict biodiversity, but rather to compare how sensitive the biodiversity of each country is, to pollution, and see how this varies geographically - eg are the continents' sensitivities statistically different. Jul 29 at 7:35
• Please edit such information into your question, because most readers won't check comments. Also, by making separate models for each pressure, you are making large (and I would say unjustified) assumptions about the nature of their covariance. That you want to do inference rather than prediction does not change this.
– mkt
Jul 29 at 7:37
• Okay, thank you Jul 29 at 7:39
• "because the purpose isn't to be able to use pollution to predict biodiversity, but rather to compare how sensitive" The purpose doesn't matter here. You might be still better of by adding all variables together in a single model. In that way you obtain a sensitivity that is corrected for the presence of other pressures. Jul 29 at 8:01