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The many tools that social and behavioral scientists use to gather data from their fellow humans have, in most cases, been honed on a rarefied subset of humanity: highly educated participants with unique capacities, experiences, motivations, and social expectations. Through this honing process, researchers have developed protocols that extract information from these participants with great efficiency. However, as researchers reach out to broader populations, it is unclear whether these highly refined protocols are robust to cultural differences in skills, motivations, and expected modes of social interaction. In this paper, we illustrate the kinds of mismatches that can arise when using these highly refined protocols with nontypical populations by describing our experience translating an apparently simple social discounting protocol to work in rural Bangladesh. Multiple rounds of piloting and revision revealed a number of tacit assumptions about how participants should perceive, understand, and respond to key elements of the protocol. These included facility with numbers, letters, abstract number lines, and 2D geometric shapes, and the treatment of decisions as a series of isolated events. Through onthe-ground observation and a collaborative refinement process, we developed a protocol that worked both in Bangladesh and among US college students. More systematic study of the process of adapting common protocols to new contexts will provide valuable information about the range of skills, motivations, and modes of interaction that participants bring to studies as we develop a more diverse and inclusive social and behavioral science. generalizability | diversity | cross-cultural | social discounting | Bangladesh I n 1932, the psychologist Rensis Likert (1) published his dissertation on a novel method for measuring attitudes. After giving university students printed statements about race relations, Likert asked them to check one of five options (strongly approve, approve, undecided, disapprove, and strongly disapprove) indicating how much they endorsed each of these statements. Likert then assigned numbers to these levels of approval and took an average across all statements. The simplicity of both the response format and construction of the scale soon spurred researchers to adopt elements of the technique to assess not only attitudes (Likert's original interest) but also subjective judgments along many dimensions, including likelihood, desirability, difficulty, and happiness (2). Today, after decades of testing and refinement on generations of participants, Likert's simple format has become a reliable mainstay of social and behavioral research.Given its ubiquity in the social and behavioral sciences, one might guess that a five-or seven-item Likert format is a natural way of asking humans about their subjective judgments. However, in the rare cases when researchers have described their experience using Likert items outside of formally educated populations, they have been met with mixed success (3,4). It turns out tha...

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Several scholars have proposed that behavioral immune responses can account for worldwide human diversity in several behavioral and cognitive domains. Testing such claims generally relies on observational, cross-population data sets, posing challenges for causal inference. In this paper we describe four key pitfalls to using such data to test hypotheses for cross-population diversity based on a behavioral immune system. These issues are associated with (a) representativeness of sampling populations, (b) statistical artifacts of aggregation, (c) clustered data, and (d) spurious associations through unmeasured variables. We argue that these issues can be mitigated through careful attention to research design, analytic strategies, and serious treatments of alternative hypotheses.

Current scientific reforms focus more on solutions to the problem of reliability (e.g. direct replications) than generalizability. Here, we use a cross-cultural study of social discounting to illustrate the utility of a complementary focus on generalizability across diverse human populations. Social discounting is the tendency to sacrifice more for socially close individuals—a phenomenon replicated across countries and laboratories. Yet, when adapting a typical protocol to low-literacy, resource-scarce settings in Bangladesh and Indonesia, we find no independent effect of social distance on generosity, despite still documenting this effect among US participants. Several reliability and validity checks suggest that methodological issues alone cannot explain this finding. These results illustrate why we must complement replication efforts with investment in strong checks on generalizability. By failing to do so, we risk developing theories of human nature that reliably explain behaviour among only a thin slice of humanity.

Material wealth is a key factor shaping human development and well-being. Every year, hundreds of studies in social science and policy fields assess material wealth in low- and middle-income countries assuming that there is a single dimension by which households can move from poverty to prosperity. However, a one-dimensional model may miss important kinds of prosperity, particularly in countries where traditional subsistence-based livelihoods coexist with modern cash economies. Using multiple correspondence analysis to analyze representative household data from six countries—Nepal, Bangladesh, Ethiopia, Kenya, Tanzania and Guatemala—across three world regions, we identify a number of independent dimension of wealth, each with a clear link to locally relevant pathways to success in cash and agricultural economies. In all cases, the first dimension identified by this approach replicates standard one-dimensional estimates and captures success in cash economies. The novel dimensions we identify reflect success in different agricultural sectors and are independently associated with key benchmarks of food security and human growth, such as adult body mass index and child height. The multidimensional models of wealth we describe here provide new opportunities for examining the causes and consequences of wealth inequality that go beyond success in cash economies, for tracing the emergence of hybrid pathways to prosperity, and for assessing how these different pathways to economic success carry different health risks and social opportunities.

Anthropologists have long been interested in the reasons humans choose to help some individuals and not others. Early research considered psychological mediators, such as feelings of cohesion or closeness, but more recent work, largely in the tradition of human behavioral ecology, shifted attention away from psychological measures to clearer observables, such as past behavior, genetic relatedness, affinal ties, and geographic proximity. In this paper, we assess the value of reintegrating psychological measures-perceived social closeness-into the anthropological study of altruism. Specifically, analyzing social network data from four communities in rural Bangladesh (N = 516), we show that perceived closeness has a strong independent effect on helping, which cannot be accounted for by other factors. These results illustrate the potential value of reintegrating proximate psychological measures into anthropological studies of human cooperation.

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