Data analysis for cultural multilevel selection research: a tutorial
This R script is an analytical supplement to: Designing cultural multilevel selection research for sustainability science. Kline, Waring & Salerno. (2018) Sustainability Science which describes how and why to use a cultural multilevel selection investigation to study the emergence of sustainability-related behaviors and cultural traits. The theory presented in the article, and the analysis we demonstrate below are not limited to environmental behavior, and may be applied to any appropriate behavioral dataset. Here we provide a practical, reusable, and open-source analysis example for researchers interested in using cultural multilevel selection methods to analyze their data. If you use or build on the code, please cite the paper. We demonstrate two simple methods which will help researchers gain intuition about the statistical features of their datasets and whether group-level cultural selection may be at play. Because this code generates random data, running the code multiple times will produce different datasets, and different results.
SSI Carbon Emissions Study
This file is intended to carefully and publically document the stages of data analysis and visualization that contributed to this research project. The following R code, output and annotations accompany a study of the University of Maine's Sustainability Solutions Initiative's carbon emissions between 2009 and 2011. This project was the result of work by Mark Anderson, Mario Teisl, and Eva Manandhar in School of Economics at the University of Maine. The published research is available at timwaring.wordpress.com.