Network Autocorrelation Regression With Binary and Ordinal Dependent Variables

Abstract

Dow and Eff recently reported high levels of network autocorrelation for over eleven hundred and fifty variables coded for the Standard Cross-Cultural Sample (SCCS) with respect to five distinct measures of network proximity: distance, language, cultural complexity, religion, and ecological niche. Most of the variables were discrete, either binary or ordinal, as are the vast majority of variables in all cross-cultural samples. A large percentage showed significant levels of autocorrelation with respect to two or more networks simultaneously. One implication of these findings is that there is a need in comparative research for statistical models that can deal with binary and ordinal dependent variables while simultaneously controlling for one or more autocorrelation process. The current paper suggests one such regression modeling procedure and applies it in a reanalyses of the causes of polygyny. Both spatial autocorrelation by itself, and spatial and language autocorrelation in combination, were found to be significant predictors of polygyny.