Parasites and politics: why cross-cultural studies must control for relatedness, proximity and covariation

Abstract

A growing number of studies seek to identify predictors of broad-scale patterns in human cultural diversity, but three sources of non-independence in human cultural variables can bias the results of cross-cultural studies. First, related cultures tend to have many traits in common, regardless of whether those traits are functionally linked. Second, societies in geographical proximity will share many aspects of culture, environment and demography. Third, many cultural traits covary, leading to spurious relationships between traits. Here, we demonstrate tractable methods for dealing with all three sources of bias. We use cross-cultural analyses of proposed associations between human cultural traits and parasite load to illustrate the potential problems of failing to correct for these three forms of statistical non-independence. Associations between parasite stress and sociosexuality, authoritarianism, democracy and language diversity are weak or absent once relatedness and proximity are taken into account, and parasite load has no more power to explain variation in traditionalism, religiosity and collectivism than other measures of biodiversity, climate or population size do. Without correction for statistical non-independence and covariation in cross-cultural analyses, we risk misinterpreting associations between culture and environment.

Dual Evolutionary Foundations of Political Ideology Predict Divergent Responses to COVID-19

“…In addition, perceived threat and uncertainty in the environment is expected to act as a trigger, increasing norm-enforcing conformity 38 and, in turn, social conservatism 19 . This is consistent with evidence showing (1) increased conformity 39,40 , norm enforcement 41,42 , and social conservatism 43,44 in response to increased experimentally induced or real threat (e.g., of group conflict or disease), as well as (2) population-level correlations between levels of threat (e.g., warfare or disease) and social conservatism and related variables [45][46][47][48][49][50][51][52][53][54] (but see 55,56 ).…”

Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

“…If interdependence of countries is ignored, observed estimates of betweencountry effect can be biased (e.g. [33]) or entirely driven by particular regions. To account for interdependence of countries within our multi-level framework, we group countries into 20 geopolitical regions based on the United Nations [35] and treat region as the highest-level unit of analysis at level 3.…”

The ecological drivers of variation in global language diversity

“…In order to untangle causal connections from incidental associations, we need to account for sources of covariation in our data. In particular, we need to address spatial autocorrelation and phylogenetic non-independence 32,33 . Grid cells that are located near each other are likely to have similar values of climatic and landscape variables, contain related human cultures and languages, and share much of their flora and fauna.…”

“…This hierarchy is a proxy for the expected patterns of similarity due to relatedness and does not represent a phylogenetic history of descent. It represents the best available estimate of the relationships within language families and therefore provides a way to generate a matrix of expected similarity due to descent 33 . The global language taxonomy is only resolved to the language family level, so we assume that any pair of languages from different families represent the maximum distance from each other.…”