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 critical1,2,3, 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 nature4,5,6,7,8. During the same period, there has been increasing interest in psychological differences across cultures2,9,10,11. These approaches are, of course, complementary12,13. 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 Inventory14). 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 challenges15,16.

Table 1 Brief descriptions of each of the seven fundamental social motives, and two sample items from each of the 11 six-item subscales of the Fundamental Social Motives Inventory (FSMI).
Full size table

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 attention17,18, perception of others’ emotions19, conformity20, economic decision-making21,22, aggression23, responses to persuasion24, and detection of threat-cues in potential enemies versus allies25. Activation of parenting (Kin Care) motives has also been linked to a number of functionally sensible outcomes26.

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 women20,21. 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 children14,27. In addition, at the individual level, fundamental social motives appear sensibly correlated with personality traits such as the Big Five factors. For example, agreeableness is correlated with the motive to affiliate with groups, and neuroticism is correlated with the Self-Protection motive. At the same time, the motives demonstrate discriminant validity from these and other dimensions14. Thus, fundamental social motives affect a wide range of social, cognitive, affective, and behavioral processes and are systematically linked to demographic and individual differences.

Might the fundamental social motives also vary systematically across human societies? Although these dimensions are high in both fitness relevance and everyday salience, they have been largely missing from the study of human cultural variation. How might these fundamental social motives map onto previously studied dimensions of cultural differences? How might the picture of human cultural variation look if we took these motives into account? How might a consideration of fundamental social motives influence our understanding of cultural similarity or distance among the world’s societies? We have not yet found existing data capable of answering questions such as these. Here, therefore, a team of international collaborators gathered data on the fundamental social motives across 42 societies in two waves.

Methods

This research was approved by the Institutional Review Board (IRB) at Arizona State University (ASU).

Participants

Data were gathered in two waves, from a total of 42 countries (total N = 15,915). Nineteen countries were represented in both waves (N = 10,907) (see Fig. 1).

Fig. 1
figure 1

Countries in which data were collected in Waves 1 and 2. Countries in which data were collected only during Wave 1 are indicated in blue (n = 13), data collection only in Wave 2 is indicated in yellow (n = 10), and data collection in both Waves 1 and 2 is indicated in green (n = 19).

Full size image

The first wave of data was collected from mid-2016 through late 2019 in 32 countries from all inhabited continents (Supplementary Table 1 provides data collection timeframes by country; Table 2 provides full list of countries in each wave). Data were collected assessing fundamental social motives of 8,998 individuals (3,302 male, 5,585 female, 111 “other” or declined to answer; Mage = 24.43, SDage = 7.91, minage = 18). Supplementary Table 2 provides detailed demographic information by country.

Table 2 Data were collected in a total of 42 countries across two waves.
Full size table

The second wave of data was collected from April 2020 through November 2020, during the first year of the COVID-19 global pandemic, in 29 countries (Supplementary Table 1 provides data collection timeframes by country; Table 2 provides full list of countries in each wave). Data were collected assessing fundamental social motives of 6,917 individuals (2,249 male, 4,218 female, 450 “other” or declined to answer; Mage = 28.59, SDage = 11.31, minage = 18). Supplementary Table 2 provides detailed demographic information by country.

Data were collected via convenience sampling, including from university populations, community samples, and paid online workforces (e.g., Prolific, Amazon’s Mechanical Turk). Supplementary Table 1 provides sample type details by country, as well as city or region of data collection, if applicable. The target sample size was 200 participants per country, but this target was not reached in some countries due to limitations in data collection. Wave 1 sample Ns ranged from 84 participants (Russia) to 769 participants (Senegal). Wave 2 sample Ns ranged from 67 participants (Serbia) to 612 participants (Senegal). Supplementary Table 2 provides sample size by country in each wave. In some countries, multiple teams of researchers collected data during the same wave (see Supplementary Table 1 for details on countries with “subsamples”). Surveys were administered either via paper-and-pencil or computer/tablet.

Procedures

Parallel survey procedures were used in Waves 1 and 2. For societies in which English is not primarily spoken, collaborators collecting the data translated the survey materials into local languages. Supplementary Table 1 provides survey language and translation procedure details by country.

Fundamental Social Motives Inventory

After providing informed consent, participants completed the Fundamental Social Motives Inventory (FSMI)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 and Mate Retention items if they did not have children or a relationship, respectively. For means and standard deviations of fundamental social motives by country in Wave 1 see Table 3, and for Wave 2 see Table 4.

Table 3 Fundamental Social Motive means (and standard deviations) by country in Wave 1.
Full size table
Table 4 Fundamental Social Motive means (and standard deviations) by country in Wave 2.
Full size table

Life satisfaction

Participants’ life satisfaction was assessed via the Satisfaction with Life Scale (SWLS)28 in a subset of countries (see Supplementary Table 2 for SWLS means and standard deviations by country). SWLS was measured in 15 countries in Wave 1 and 28 countries in Wave 2. SWLS was measured in both waves in 10 countries.

Basic need fulfilment

In a subset of countries, we also assessed the degree to which participants felt their basic needs (i.e., food availability, water availability, safety, livable temperature/climate, and adequate housing/shelter) were being fulfilled. Basic needs fulfilment was measured in 12 countries in Wave 1 and 28 countries in Wave 2. Basic needs were measured in both waves in 7 countries.

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 ladder29, 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.

Data Records

Datasets30 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 project30.

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 Nunder18 = 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, 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).

Fundamental social motive variables

The eleven Fundamental Social Motive Inventory subscale abbreviations are as follows: Affiliation (Group) = AFG, Affiliation (Independence) = AFI, Affiliation (Exclusion Concern) = AFX, Disease Avoidance = DIS, Kin Care (Children) = KCC, Kin Care (Family) = KCF, Mate Seeking = MAT, Breakup Concern = MRB, Mate Retention = MRT, Self-Protection = SPO, and Status = STA.

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).

Individual difference and demographic variables

Demographics and other individual difference variables that were collected in most countries are included in the master dataset. These variables include the participant’s sex (Gender, where 1 = Male, 2 = Female, and, in some datasets 3 = Other) and age in years (Age). Participants indicated their subjective SES by rating whether they are 10 = Best off (in money, education, and respected jobs) to 1 = Worst off (SubjSES). Participants self-reported their relationship status (Relationship), coded 1 = Single and not currently dating, 2 = Single and currently dating, 3 = In a committed relationship, 4 = Married, and 5 = Divorced/Widowed. Participants self-reported their number of children (N.Children) from 0 to 4 or greater.

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).

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 Inventory14, as well as the Satisfaction with Life Scale28 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:

  • How do fundamental social motives vary around the world?

  • How might ecological variables, such as pathogen prevalence, rates of violence, and income inequality affect people’s fundamental social motives?

  • Do demographic variables such as age, gender, number of children, and SES affect people’s fundamental social motives in the same ways around the world?

  • How do people’s fundamental social motives relate to cross-cultural dimensions such as individualism/collectivism, relational mobility, and tightness/looseness? How do fundamental social motives relate to values, such as egalitarianism and harmony (Wetzel’s values)?

  • How does variation in fundamental social motives around the world relate to variation in other individual difference dimensions, such as Big 5 personality traits?

  • How might fundamental social motives predict important societal outcomes, such as a society’s level of innovation or democratic policies?

  • How do people’s fundamental social motives affect their happiness around the world31 and across time?

  • What effect might the COVID-19 pandemic have had on people’s fundamental social motives32?

This non-exhaustive list provides examples of the range of important questions this dataset can help us address, to better understand how and why people’s motivations vary across cultures and across time.

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.

Fig. 2
figure 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 are Self-Protection (SPO), Disease Avoidance (DIS), Affiliation (Group) (AFG), Affiliation (Independence) (AFI), Affiliation (Exclusion Concern) (AFX), Status (STA), Mate Seeking (MAT), Breakup Concern (MRB), Mate Retention (MRT), Kin Care (Family) (KCF).

Full size image
Fig. 3
figure 3

Hierarchical clustering of societies based on fundamental social motives measured in Wave 2. The dendrogram illustrates societies’ similarity on overall fundamental social motive (FSM) profiles (N = 6,917 participants) in the Wave 2 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 are Self-Protection (SPO), Disease Avoidance (DIS), Affiliation (Group) (AFG), Affiliation (Independence) (AFI), Affiliation (Exclusion Concern) (AFX), Status (STA), Mate Seeking (MAT), Breakup Concern (MRB), Mate Retention (MRT), Kin Care (Family) (KCF).

Full size image

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 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 implementation33 of hierarchical agglomerative clustering (HAC), using Euclidean distance and Ward variance minimization linkage criterion34,35 to create each dendrogram.