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The stigma associated with substance use is well known, but little research has examined stigma attribution, or the tendency to stigmatize, nor is there much cross-cultural research on the topic. We examine cultural models for the risk of substance use associated with stigma attribution in two settings: the United States and Brazil. Study populations of young adults are the focus in each setting. Using methods of cultural domain analysis, cultural consensus analysis, and the analysis of residual agreement, we find similar models in each society. There is a continuum from viewing substance use risk as a biopsychosocial problem to viewing it as a moral issue. In the United States, viewing substance use as a biopsychosocial issue is associated with lower attributed stigma; in Brazil, viewing substance use as a biopsychosocial issue is associated with higher attributed stigma. We argue that social patterns of drug use in each society underlie this difference.

As large-scale collaborative, cross-cultural ethnographic research becomes easier and easier to realize, certain ethnographic methods and analyses should be correspondingly more available, inviting, and accommodating. We have therefore created AnthroTools, a package for the free, open-source language R, with a variety of tools and functions suitable for both multi-factor free-list analysis and Bayesian cultural consensus modeling. Free-list data elicitation is a simple technique for ethnographic research. However, especially for cross-cultural free-list data, background preparation is considerable and often requires specific software. In addition, although current cultural consensus analysis tools offer very sophisticated analyses, they also either require specialized software or have computationally taxing methods. AnthroTools expedites these techniques, rapidly performs diagnostics, and prepares data for further analysis. In this article, we briefly discuss what this package offers cross-cultural researchers and provide basic examples of some of its functions.

In a comparative study of evil eye ( mal de ojo), we demonstrate a methodology appropriate for the study of cultural transmission of beliefs. We studied four diverse populations with historical links to Spain: Puerto Ricans in Connecticut, Mexican Americans in south Texas, Mexicans in Guadalajara, and rural Guatemalans. Using agreement on ideas or themes about evil eye within and across sites, we identify specific ideas that may have persisted through time. The relevance of specific themes was estimated with a cultural consensus analysis. Mal de ojo was widely recognized in each community and higher community prevalence was associated with higher agreement on reported causes, symptoms, and treatments. Each community exhibited a distinct model for ojo, although models were highly similar between sites. Agreement among individuals and across communities suggests a pan-regional description for mal de ojo and possible content of older versions of these beliefs in Latin America.

While the topic of generalization is well documented for quantitative research, it is less well documented for qualitative research. Addressing this issue, Williams (2000) proposes the concept of ‘moderatum generalization’ in which generalization is based on the presence of shared culture, and Gobo (2009) proposes idiographic sampling theory via three modes of inference. This article builds on Williams (2000) by considering two mixed-method approaches, cultural consensus analysis combined with cultural modelling, and Q methodology. Both approaches provide a basis for qualitative generalization by explicitly identifying shared culture. In doing this they are consistent with the emblematic case mode of inference described by Gobo (2009).

This article highlights the benefits of jointly using cultural modeling and cultural consensus analysis for the purpose of triangulation in research in which a cultural group’s point of view is desired. Cultural models are those presupposed, taken-for-granted models of knowledge and thought used in the course of everyday life to guide a person’s understanding of the world. These models can be identified by discourse analysis. Cultural consensus analysis is a quantitative-based methodological tool that asks if shared knowledge of a specific cultural domain exists within a group of informants. In presenting their results, the authors recommend the joint use of these two methods in research in which a broad understanding of a cultural group’s perspective on a given topic is needed.