Abstract

This article presents a general statistical approach suitable for the analysis of time-resolved (time-series) cross-cultural data. The goal is to test theories about the evolutionary processes that generate cultural change. This approach allows us to investigate the effects of predictor variables (proxying for theory-suggested mechanisms), while controlling for spatial diffusion and autocorrelations due to shared cultural history (known as Galton’s Problem). It also fits autoregressive terms to account for serial correlations in the data and tests for nonlinear effects. I illustrate these ideas and methods with an analysis of processes that may influence the evolution of one component of social complexity, information systems, using the Seshat: Global History Databank. Editor's note: the link to the R scripts in the footnote on p.1 of the Supplemental Materials is now broken. Please use the following link instead: https://datadryad.org/stash/dataset/doi:10.17916/P6159W.

Highlights

  • This article presents a general statistical approach suitable for the analysis of time-resolved cross-cultural data

  • I submitted each dataset to an R-script, which finds the combinations of predictors that yield the best AIC (Dynamic Regression: Methods)

  • I first report the regression results for a dataset with averaged predictors

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Summary

Introduction

This article presents a general statistical approach suitable for the analysis of time-resolved (time-series) cross-cultural data. The goal is to test theories about the evolutionary processes that generate cultural change. This approach allows us to investigate the effects of predictor variables (proxying for theory-suggested mechanisms), while controlling for spatial diffusion and autocorrelations due to shared cultural history (known as Galton’s Problem). It Cits autoregressive terms to account for serial correlations in the data and tests for nonlinear effects. I illustrate these ideas and methods with an analysis of processes that may inCluence the evolution of one component of social complexity, information systems, using the Seshat: Global History Databan

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