Fundamental social motives measured across forty-two cultures in two waves

H﻿ow does psychology vary across human societies? The fundamental social motives framework adopts an evolutionary approach to capture the broad range of human social goals within a taxonomy of ancestrally recurring threats and opportunities. These motives—self-protection, disease avoidance, affiliation, status, mate acquisition, mate retention, and kin care—are high in fitness relevance and everyday salience, yet understudied cross-culturally. Here, we gathered data on these motives in 42 countries (N = 15,915) in two cross-sectional waves, including 19 countries (N = 10,907) for which data were gathered in both waves. Wave 1 was collected from mid-2016 through late 2019 (32 countries, N = 8,998; 3,302 male, 5,585 female; Mage = 24.43, SD = 7.91). Wave 2 was collected from April through November 2020, during the COVID-19 pandemic (29 countries, N = 6,917; 2,249 male, 4,218 female; Mage = 28.59, SD = 11.31). These data can be used to assess differences and similarities in people’s fundamental social motives both across and within cultures, at different time points, and in relation to other commonly studied cultural indicators and outcomes. Measurement(s) Motivation • Emotional Well-being • Socioeconomic Indicator • Culture • Cultural Diversity Technology Type(s) survey method • digital curation Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Australia • Austria • Bolivia • Brazil • Bulgaria • Canada • Chile • China • Colombia • Czech Republic • Germany • Hong Kong • India • Israel • Italy • Japan • Kenya • Lebanon • Mexico • The Netherlands • New Zealand • Nigeria • Pakistan • Peru • The Philippines • Portuguese Republic • Romania • Russia • Saudi Arabia • Senegal • Serbia • Singapore • Slovak Republic • South Korea • Spain • Sweden • Thailand • Turkey • Uganda • Ukraine • United Kingdom • United States of America Measurement(s) Motivation • Emotional Well-being • Socioeconomic Indicator • Culture • Cultural Diversity Technology Type(s) survey method • digital curation Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Australia • Austria • Bolivia • Brazil • Bulgaria • Canada • Chile • China • Colombia • Czech Republic • Germany • Hong Kong • India • Israel • Italy • Japan • Kenya • Lebanon • Mexico • The Netherlands • New Zealand • Nigeria • Pakistan • Peru • The Philippines • Portuguese Republic • Romania • Russia • Saudi Arabia • Senegal • Serbia • Singapore • Slovak Republic • South Korea • Spain • Sweden • Thailand • Turkey • Uganda • Ukraine • United Kingdom • United States of America


Background & Summary
As human beings have come into increasing contact with people from other parts of the globe, understanding the psychological differences and similarities between people of different cultures has become increasingly critical [1][2][3] , with broad-reaching economic and political implications. Over the last few decades, researchers in fields including anthropology, evolutionary biology, and cognitive science have investigated questions about universals in human nature [4][5][6][7][8] . During the same period, there has been increasing interest in psychological differences across cultures 2,[9][10][11] . These approaches are, of course, complementary 12,13 . We suggest a new way of thinking about cultural variation, in terms of a set of fundamental motivational systems evolved to deal with the universal problems and opportunities that human beings have regularly confronted in their social relationships-involving self-protection, disease avoidance, affiliation, status, mate acquisition, mate retention, and kin care (see Table 1 for a brief description of each motive and sample items from the Fundamental Social Motives Inventory 14 ). In the face of these recurring challenges and opportunities, humans are presumed to have evolved a set of fundamental social motives-systems of perception, cognition, and affect that direct behavior in ways that help address these challenges 15,16 .
This framework has generated a number of interesting findings. Overall, these studies have found that cognitive processes, affect, and behaviors vary, in adaptively functional ways, as different fundamental social motives are activated. Activating Self-Protection versus Mate Seeking versus Disease Avoidance concerns, for example, has very different, yet functionally sensible, effects on attention 17,18 , perception of others' emotions 19 , conformity 20 , economic decision-making 21,22 , aggression 23 , responses to persuasion 24 , and detection of threat-cues in potential enemies versus allies 25 . Activation of parenting (Kin Care) motives has also been linked to a number of functionally sensible outcomes 26 .
Furthermore, the fundamental social motives are linked to individual differences in functionally relevant ways. For example, consistent with principles of differential parental investment and sexual selection, which have been linked to male competition for more selective female mating partners across species, Mate Seeking leads to more risk-taking behaviors in men, but more conforming and group-oriented behavior in women 20,21 . Other research finds that chronic activation of motives (e.g., Status, Mate Seeking, Self-Protection) links in sensible ways to life-history-relevant demographic variables, such as one's sex, age, and number of children 14,27 . In addition, at the individual level, fundamental social motives appear sensibly correlated with personality traits # A full list of authors and their affiliations appears at the end of the paper.   14 , a 66-item instrument assessing 11 motive subdimensions: Self-Protection, Disease Avoidance, Affiliation (Exclusion Concern), Affiliation (Group), Affiliation (Independence), Status, Mate Seeking, Breakup Concern, Mate Retention, Kin Care (Family), and Kin Care (Children). Participants who did not have children were instructed not to complete the Kin Care (Children) items, and those not in romantic relationships were instructed not to complete the Breakup Concern and Mate Retention items. In some samples, participants indicated their relationship status and whether they had children before completing the FSMI, and they were subsequently not shown Kin Care (Children) or Breakup Concern

Fundamental Social Motives
Subscale Sample Items

Self-Protection
Archeological and anthropological studies of ancestral societies suggest homicide and assault rates many times greater than those found in modern societies 36,37 .
• I think about how to protect myself from dangerous people • I am motivated to protect myself from dangerous others.

Disease Avoidance
Ancestrally, contagious illnesses were responsible for the deaths of a substantial portion of infants and for a substantial number of deaths among adults, as well 37 . Increased population density after the onset of agriculture exacerbated this problem 38 .
• I avoid places and people that might carry diseases.
• When someone near me is sick, it doesn't bother me very much. (R)

Affiliation
Anthropological evidence suggests that individuals living under ancestral conditions would not have produced sufficient calories to feed themselves or their offspring without the existence of cooperative risk-pooling alliances 39 .

Group subscale
• I enjoy working with a group to accomplish a goal.
• Getting along with the people around me is a high priority.

Independence subscale
• Being apart from my friends for long periods of time does not bother me.
• Having time alone is extremely important to me.

Exclusion Concern subscale
• I would be extremely hurt if a friend excluded me.
• It bothers me when groups of people I know do things without me.

Status
Individuals achieving positions of respect in ancestral groups likely had increased access to resources and desirable mates 40 .
• It's important to me that others respect my rank or position.
• I do things to ensure that I don't lose the status I have.

Mate Acquisition
All ancestors of currently existing sexually reproducing organisms, including Homo sapiens, were successful in attracting at least one mate.
• I spend a lot of time thinking about ways to meet possible dating partners.
• I am interested in finding a new romantic/sexual partner.

Mate Retention
Because humans are altricial, our helpless offspring benefit greatly from resources and care provided by two parents 41 .

Mate Retention (General) subscale
• It is important to me that my partner is sexually loyal to me.
• It would not be that big a deal to me if my partner and I broke up. (R)

Breakup Concern subscale
• I often think about whether my partner will leave me.
• I worry about others stealing my romantic/sexual partner.

Kin Care
Beyond caring for their direct descendants, human beings also traditionally shared essential resources and protection within wider kin groups 39 . Humans are a relatively slow life history species 42 and human psychology is shaped by inclusive fitness 43 .

Family subscale
• Caring for family members is important to me.
• It is extremely important to me to have good relationships with my family members.

Children subscale
• I often think about how I could stop bad things from happening to my children.
• Providing for my children is important to me.  www.nature.com/scientificdata www.nature.com/scientificdata/ Demographic variables. Demographic information on age, gender, relationship status, and number of children was collected in each country. Race/ethnicity was measured using country-appropriate categories as indicated by local collaborators. Participants also indicated where they would place their own subjective socioeconomic status (SES) on a 10-rung subjective social status ladder 29 , in which the lowest rung (1) corresponds to those in society who are worst off in terms of money, education, and respected jobs, and the highest rung (10) corresponds to those who are best off. Supplementary Table 2 provides sample size and participants' gender, age, and subjective SES by country in each wave.
Additional variables. Participants in a small subset of countries were asked additional questions, such as their religion. In Wave 2, participants in some English-speaking countries were asked questions such as how successful they believed themselves to be at accomplishing each of the fundamental social motives. Some were asked how much they would like to know, upon meeting a person for the first time, how important each of the fundamental social motives was to that person.   www.nature.com/scientificdata www.nature.com/scientificdata/

Data Records
Datasets 30 are available as .sav files (for direct use in SPSS) and .csv files on the Open Science Framework (OSF) platform. We provide three types of datasets.
First, we provide a "master" dataset containing sample variables (details below), fundamental social motives, and participant demographics and other individual difference variables for each participant across countries and waves.
Second, we provide "individual country" data files for each sample collected in each of the two waves. Many of these datasets contain additional variables collected in only a subset of countries, or only in one country (e.g., country-specific ethnicity or religion questions, as determined by local collaborators).
Third, we provide a "country-level" data file containing country-level mean values for each fundamental social motive, country-level values for commonly studied cross-cultural variables compiled from published research (e.g., individualism, relational mobility, tightness-looseness), and country-level economic indicators (e.g., GDP and GINI). A complete reference list for these variables is provided in the OSF project 30 .
Due to ethical considerations, raw individual country datasets were cleaned to remove participants who indicated that they were 15-, 16-, or 17-years old (total excluded N under18 = 81), and to remove potentially identifying information and metadata. Variables to be included in the master dataset (e.g., fundamental motives, gender, age) were renamed and recoded to match the standardized coding of the master dataset and then compiled. Missing data in the fundamental motives items and the Gender, Age, Relationship, N.Children, SubjSES,  www.nature.com/scientificdata www.nature.com/scientificdata/ BirthCountry, and RaceEthnicity variables are indicated by blanks spaces or values of -999, -99, -77, or -66 in various individual country datasets and in the master dataset.

Sample variables. Sample variables include identifiers for each participant in the master dataset (masterID)
and for each participant within their individual country dataset (pID). Each participant's country is indicated by the country name (country.N), the country's three-letter ISO 3166 alpha-3 country code (ISO3), and a numeric code assigned to each country based on ISO3 (country.ID). In countries where more than one sample was collected during the same wave, these datasets are distinguished via the subsample variable. The wave in which a participant's data were collected is indicated by the wave variable.
We also provide a variable indicating which participants we recommend excluding from analyses (filter_ exclude, where 1 = include and 0 = exclude). A second variable (ExcludeWhy) indicates the reason why a participant's data is recommended for exclusion. These reasons include: 1 = Invalid response on Fundamental Motive item, 2 = Invalid response on Age, 3 = One or more Fundamental Motive subscale scores (except Mate Retention, Breakup Concern, or Kin Care (Children)) is entirely missing, 4 = Other invalid response (e.g., "9" on a 7-point scale SWLS item). This filter (excluding 2807 cases across all samples) was applied when calculating all descriptive statistics and creating all figures included here.
Each of the above sample variables are included in both the master dataset and the individual country datasets (with the exception of the masterID variable, only included in the master dataset). Each Fundamental Social Motive Inventory subscale comprises six items named according to the subscale abbreviation and a number, 1 through 6 (e.g., AFG1, AFG2, AFG3, etc.). Items are measured from 1 = Strongly disagree (indicating low levels of the motive, except for reverse-scored items) to 7 = Strongly agree (indicating high levels of the motive, except for reverse-scored items). Certain subscale items need to be reverse-scored for subscale score calculation-these items end in "R" and are not yet reverse-scored in the datasets (e.g., AFG4R).
For each participant, the six items of each subscale were reverse-scored as appropriate and then averaged together to form a subscale score variable. These variables are indicated by fundamental social motive subscale abbreviations followed by no numbers (e.g., AFG). Participants in many countries in each wave responded to the five-item Satisfaction with Life scale (swls1 through swls5), on a 7-point scale with higher scores indicating greater satisfaction with life. These items were then averaged for each participant to form their satisfaction with life score variable (SWLS).

Individual difference and demographic variables.
Participants in many countries in each wave indicated whether their basic needs (i.e., enough food, enough water, a reliable place to sleep, a livable temperature, and feeling safe) were being met, from 1 = Strongly disagree to 7 = Strongly agree.
Finally, participants in several countries in Wave 2 rated how interested they would be, upon meeting a new person, to learn how important each fundamental social motive was to that person (learn variables, each with a corresponding fundamental social motive suffix, e.g., learnAFG), from 1 = Very uninterested to 7 = Very interested.

Technical Validation
The main instrument used, the Fundamental Social Motives Inventory 14 , as well as the Satisfaction with Life Scale 28 are published scales with established reliability and validity indicators. These scale and subscale scores were calculated by reverse-scoring items (as appropriate) and averaging subscale items according to the published scale calculation procedures.
English survey materials were translated by native speakers for use across countries. Information regarding the language in which the study was conducted for each sample, as well as information on translation procedures for each language can be found in Supplementary Table 1.

Usage Notes
This dataset provides numerous opportunities to explore how people's fundamental social motives vary around the world across two timepoints, as well as to explore factors that may be associated with these cross-cultural variations. By analyzing this dataset alone and in combination with other cross-cultural datasets that include further indicators of culture, values, personality, etc., researchers can explore the following types of scientific questions: Illustrative exploratory analysis. To help illustrate the potential of the dataset, we present a straightforward exploration of the variation in fundamental social motives around the world in each wave. One way to think about cross-societal similarities and differences is to consider how different societies cluster, based on similarities and differences in their overall fundamental social motive profiles. Figure 2 presents a dendrogram of Wave 1 countries, and Fig. 3 presents a dendrogram of Wave 2 countries. In each dendrogram, countries branch into two main clusters and five subclusters, based on similarity of motive profiles (indicated by different colors). The average motive profile of each subcluster is illustrated by a radar chart.
In Fig. 2, Wave 1 countries branch into two main clusters and five subclusters. These branches reveal that countries do not cluster into traditional West vs. Rest or Rich vs. Poor clusters. Yet, the clusters are hardly arbitrary. For example, New Zealand, Australia, Canada, and the United Kingdom are on one branch. Austria   Fig. 2 Hierarchical clustering of societies based on fundamental social motives measured in Wave 1. The dendrogram illustrates societies' similarity on overall fundamental social motive (FSM) profiles (N = 8,998 participants) in the Wave 1 data collection. Two countries that branch apart farther from the center are more similar than two countries that branched apart closer to the center. The color of a country's link represents its membership to a main cluster (two clusters: red and blue), whereas the color of its name represents its membership to a sub-cluster (five sub-clusters). The radar chart next to each cluster of the dendrogram shows average z-scores of each FSM subscale for all countries in that cluster. Subscales www.nature.com/scientificdata www.nature.com/scientificdata/ is closest to Germany, as is Spain to Italy. Likewise, Bolivia, Mexico, Brazil, Chile, and Peru are in the same subcluster. However, clusters also deviate from previous categorizations of the world's cultures. For example, most English-speaking countries cluster with wealthy East Asian democracies. The United States, though, clusters with several Latin American countries. Further, Senegal and Uganda form a subcluster with Italy, Spain, Thailand, and China. These results suggest that fundamental social motives not only capture sensible patterns of cultural clustering that have previously been posited, but also reveal new and sometimes surprising similarities between societies (e.g., between South Korea and Canada, between the United States and Peru).
In Fig. 3, Wave 2 (mid-pandemic) countries branch in two main clusters and five subclusters. These branches reveal some familiar patterns. For example, all post-communist societies belong to one subcluster, all but one West European country belong to one subcluster, and the two East African societies in Wave 2 cluster closest to each other. The clusters also reveal some surprising patterns, however. For example, Colombia and Lebanon cluster with post-communist European societies, and South Korea again clusters closest to Canada.
Dendrograms. Separately for each wave (across the 32 societies in the Wave 1 sample, and across the 29 societies in the Wave 2 sample), we standardized each of ten fundamental social motive subscales (excluding the Kin Care (Children) subscale because 10 participants or fewer completed this scale in several countries; these countries are indicated in Tables 3 and 4). We then utilized a Python implementation 33 of hierarchical agglomerative clustering (HAC), using Euclidean distance and Ward variance minimization linkage criterion 34,35 to create each dendrogram.

Code availability
All code used to process and visualize the data, including information on software packages used, is freely available in the OSF project 30 .