cross cultural research

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• v.287(1935); 2020 Sep 30

Navigating cross-cultural research: methodological and ethical considerations

Tanya broesch.

1 Department of Psychology, Simon Fraser University, BC, Canada

Alyssa N. Crittenden

2 Department of Anthropology, University of Nevada, Las Vegas, NV, USA

Bret A. Beheim

3 Department of Human Behavior, Ecology and Culture, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany

Aaron D. Blackwell

4 Department of Anthropology, Washington State University, Pullman, WA, USA

John A. Bunce

Heidi colleran.

5 BirthRites Independent Max Planck Research Group, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany

Kristin Hagel

Michelle kline.

6 Centre for Culture and Evolution, Brunel University, London, UK

Richard McElreath

Robin g. nelson.

7 Department of Anthropology, Santa Clara University, CA, USA

Anne C. Pisor

8 Department of Anthropology, University of Missouri, MO, USA

Ilaria Pretelli

Benjamin purzycki.

9 Department of the Study of Religion, Aarhus University, Aarhus, Denmark

Elizabeth A. Quinn

10 Department of Anthropology, Washington University, Saint Louis, MO, USA

Brooke Scelza

11 Department of Anthropology, UCLA, Los Angeles, CA, USA

Kathrine Starkweather

12 Department of Anthropology, University of Illinois, Chicago, USA

Jonathan Stieglitz

13 Institute for Advanced Study, Toulouse, France

Monique Borgerhoff Mulder

14 Department of Anthropology, University of California, Davis, CA, USA

Associated Data

This article has no additional data.

The intensifying pace of research based on cross-cultural studies in the social sciences necessitates a discussion of the unique challenges of multi-sited research. Given an increasing demand for social scientists to expand their data collection beyond WEIRD (Western, educated, industrialized, rich and democratic) populations, there is an urgent need for transdisciplinary conversations on the logistical, scientific and ethical considerations inherent to this type of scholarship. As a group of social scientists engaged in cross-cultural research in psychology and anthropology, we hope to guide prospective cross-cultural researchers through some of the complex scientific and ethical challenges involved in such work: (a) study site selection, (b) community involvement and (c) culturally appropriate research methods. We aim to shed light on some of the difficult ethical quandaries of this type of research. Our recommendation emphasizes a community-centred approach, in which the desires of the community regarding research approach and methodology, community involvement, results communication and distribution, and data sharing are held in the highest regard by the researchers. We argue that such considerations are central to scientific rigour and the foundation of the study of human behaviour.

1. Introduction

The acknowledgement that most research in psychology and other adjacent fields is overwhelmingly based on so-called WEIRD (Western, educated, industrialized, rich and democratic) populations [ 1 ] has given way to intensified research funding, publication and visibility of collaborative cross-cultural studies across the social sciences that expand the geographical range of study populations (e.g. [ 2 – 10 ]). The rapid expansion of cross-cultural team science has been precipitated by the ever-increasing availability of online global data sources and the expansion of the cross-cultural enterprise into fields such as economics [ 11 ], political science [ 12 ] and other disciplines with little previous field research expectations or ethnographic focus. This expansion necessarily generates concerns regarding responsible methods and practice. For example, many of the so-called non-WEIRD communities who participate in research are Indigenous, from low- and middle-income countries in the global South, live in post-colonial contexts, and/or are marginalized within their political systems, creating power differentials between researchers and researched [ 13 , 14 ]. This creates a need for transdisciplinary discussion on the importance of community participation and the explanation and sharing of research outputs with participants.

Given increasing pressure for social scientists to expand the range of societies from which they recruit participants to test hypotheses about human behaviour, we convened a working group to discuss some of the unique scientific and ethical challenges of cross-cultural research. As a group of investigators engaged in such research in psychology and anthropology, our research objectives include testing theoretically derived hypotheses to examine general patterning and explain cultural variation in human behaviour. As such, we face challenges in how to collect systematic data, either as the primary fieldworker or in collaboration with shorter-term visitors who wish to collect their own data. The growing appetite for including diverse populations in work on demography, health, wealth, cooperation, cognition, infant and child development, and belief systems raises unique scientific and ethical issues, independent of discipline or research topic.

This paper adds to the growing dialogue on best practices when working with populations or cultural groups in low- to middle-income regions (see [ 13 – 18 ]) and touches on topics that many social scientists, particularly cultural anthropologists, have been writing about for decades [ 19 , 20 ]. Much cross-cultural research has historically been rooted in racist, capitalist ideas and motivations [ 19 ]. Scholars have long debated whether research aiming to standardize cross-cultural measurements and analysis is tacitly engaged and/or continues to be rooted in colonial and imperialist practices [ 21 , 22 ]. Given this history, it is critical that participating scientists reflect upon these issues and be accountable to their participants and colleagues for their research practices. We argue that cross-cultural research be grounded in the recognition of the historical, political, sociological and cultural forces acting on the communities and individuals of focus. These perspectives are often contrasted with ‘science’; here we argue that they are necessary as a foundation for the study of human behaviour.

Here, we present considerations that we have found to be useful in our own work. More specifically, we propose that careful scrutiny of (a) study site selection, (b) community involvement and (c) culturally appropriate research methods will begin to address some of the complex scientific and ethical challenges of cross-cultural research. Particularly for those initiating collaborative cross-cultural projects, we focus here on pragmatic and implementable steps. We stress that our goal is not to review the literature on colonial or neo-colonial research practices, to provide a comprehensive primer on decolonizing approaches to field research, nor to identify or admonish past misdemeanours in these respects—misdemeanours to which many of the authors of this piece would readily admit. Furthermore, we acknowledge that we ourselves are writing from a place of privilege as researchers educated and trained in disciplines with colonial pasts. Our goal is simply to help researchers in the future better plan and execute their projects with appropriate consideration and inclusion of study communities and culturally appropriate methodologies.

(a) Study site selection

Study site selection in cross-cultural research involves three major conceptual issues. First, the increased interest in data collected from so-called non-Western societies means that study communities outside of WEIRD contexts are prized as sites for testing theories about human behaviour. This has sometimes led to an inclusion of ‘non-WEIRD’ populations in cross-cultural research without further regard for why specific populations should be included [ 23 ]. The binning of non-Western populations as a comparative sample to the cultural West (i.e. the ‘West versus rest’ approach) is often unwittingly reinforced by researchers who heeded the call to expand study site selection beyond WEIRD societies [ 1 ]. Here, we propose that researchers identify a clear theoretical justification for inclusion of any study population—WEIRD or not—based on knowledge of the relevant cultural and/or environmental context (see [ 24 ] for a good example). Regardless of whether a research group is investigating human universals or cultural variation, including any population in a study sample without justification of their inclusion is tantamount to binning and is, therefore, theoretically problematic [ 21 ].

Second, contemporary ‘small-scale’ communities continue to be discussed in the literature as proxies of our ancestral past—to varying degrees, often based on their food economy and the degree to which it is considered to be ‘traditional’ (e.g. foraging, small-scale horticulture). While some of these groups may occupy areas that are ecologically similar to the environments in which early modern humans lived and have social systems that may inform our understanding of those lifeways, these communities differ from early human communities in key ways. Many communities engage in mixed-subsistence practices [ 25 ] and currently reside in marginal environments that may not reflect their ancestral homelands [ 26 ]. Far from the romantic notion that such populations are uncontacted and living in harmony with the natural environment, in reality, they are impacted by ecological, social and political changes from outside/globalizing forces [ 27 ]. Studying contemporary communities as referential models of ancestral lifeways not only acts to further marginalize these societies, but can also lead to erroneous scientific conclusions—for example, about ancestral patterns of diet or cooperation (see [ 28 – 31 ]).

Third, when researchers design their cross-cultural studies, it is important to be cognizant that they are (to some extent) constrained by the relatively limited number of active field sites that can generate appropriate data. As such, cross-cultural investigators are working with a potentially biased sample of global populations from which broad inferences about humanity must be cautiously drawn (see [ 23 ]). This concern parallels our call for theoretical justification of the selection of samples; it is both the diversity of samples and the match between theory and cultural context that make for improved research design (see [ 23 ] for full discussion and examples).

To address these three conceptual issues, we suggest that researchers and reviewers problematize the exoticizing of particular peoples and cultures [ 32 ]. Taking such an approach also works to minimize the inclusion of particular populations based on how popular or iconic they may be to researchers. One way to do this is to take a theoretically motivated approach to sampling communities. For example, one might select communities that vary along the specific axis of theoretical interest, such as age structure, female-biased kinship or extent of market integration (see [ 23 ]).

Intra-population sampling decisions are also important as they involve unique ethical and social challenges. For example, foreign researchers (as sources of power, information and resources) represent both opportunities for and threats to community members. These relationships are often complicated by power differentials due to unequal access to wealth, education and historical legacies of colonization [ 15 – 20 ]. As such, it is important that investigators are alert to the possible bias among individuals who initially interact with researchers, to the potential negative consequences for those excluded, and to the (often unspoken) power dynamics between the researcher and their study participants (as well as among and between study participants) [ 32 – 35 ].

We suggest that a necessary first step is to carefully consult existing resources outlining best practices for ethical principles of research. Many of these resources have been developed over years of dialogue in various academic and professional societies (e.g. American Anthropological Association, International Association for Cross Cultural Psychology, International Union of Psychological Science). Furthermore, communities themselves are developing and launching research-based codes of ethics [ 36 , 37 ] and providing carefully curated open-access materials (e.g. https://www.itk.ca ), often written in consultation with ethicists in low- to middle-income countries (see [ 38 ]).

(b) Community involvement

Too often researchers engage in ‘extractive’ research, whereby a researcher selects a study community and collects the necessary data to exclusively further their own scientific and/or professional goals without benefiting the community. This reflects a long history of colonialism in social science [ 15 – 20 , 33 – 35 ]. Extractive methods may not only lead to methodological challenges but also act to alienate participants from the scientific process and are often unethical. Many researchers are associated with institutions tainted with colonial, racist and sexist histories, sentiments and in some instances perptuating into the present. Much cross-cultural research is carried out in former or contemporary colonies, and in the colonial language. Explicit and implicit power differentials create ethical challenges that can be acknowledged by researchers and in the design of their study (see [ 39 ] for an example in which the power and politics of various roles played by researchers is discussed). To provide examples of how to do this, we draw on frameworks from cultural anthropology and development studies, including participatory research, community collaboration and grounded theory [ 40 – 43 ]. What these frameworks hold in common, and what we reiterate here, is that it is critical that communities be included in study design, implementation and presentation of research/return of results. There is no one-size-fits-all approach, yet a productive baseline may be for researchers to consider community inclusion as part of their project design from the start. Ideally, the community is not only central to the planned research, but is leading it. We realize that not all research approaches can include a research team that spans the research institution, the investigators and the community; however, we would like to note that in many instances, community-based participatory research is shifting towards this type of relationship between researchers and study communities [ 44 , 45 ].

Even if a research project does not include co-investigators from the study community, or establishing a long-term community collaboration is not an aim, the inclusion of research participants at the outset is possible. For example, in a population genetic study on the early population history of Vanuatu [ 46 ], one of the authors (H.C.) explored different approaches to explain the initial purpose of the research project before data collection. At a broad level, an analogy with linguistic family trees was most salient for discussion of population history and emerged naturally from conversations with communities about whether to carry out the research in the first place. Learning to describe the DNA itself in Indigenous idioms was far more challenging and was only possible by including the community in all stages of the project. Another co-author (A.N.C.), provided feedback on temporal changes in food and water insecurity in a foraging population in Tanzania using a different strategy: she enlisted community members as data collectors, whose feedback on interview questions was incorporated prior to data collection in order to ensure that the concepts being queried were understood by participants [ 47 ].

Context-specific knowledge is important when planning how to obtain and document informed consent in an ethical and culturally appropriate way. Most informed consent procedures were developed within the medical research community, with strict criteria for inclusion and high standards of linguistic comprehension expected. For people whose only experience of signing a formal agreement is from legal, political or medical contexts, standard consent forms can have unintended significance. Accordingly, researchers may consider an active community-level discussion as part of the consent process prior to the seeking of individual-level consent (see [ 48 ] for a full discussion). Consent is also often thought to be a one-time transaction, usually at the beginning of a study, experiment or interview. However, this is not an appropriate fit for communities where formal legal obligations carry less currency than do reciprocal social relationships. Consent should, therefore, be seen as a process and a dialogue, also referred to as ‘dynamic consent’, not merely the collection of names and signatures [ 49 – 52 ].

A new suite of challenges emerges once data collection has ended. There are ethical issues regarding the return of research results and associated data to the community. It is important that researchers discuss this with participants as part of the consent process and respect the desires of the community in this regard. It is often considered best practice for researchers to provide ample time for participants to query and discuss results, either or both in collaborative discussions with the community or private discussions with interested respondents [ 36 – 38 , 48 ]. Ideally, such community discussions provide the researcher with novel insights into data interpretation while providing participants with a satisfactory understanding of the knowledge generated by the research and an opportunity to engage with the researchers' study motivations.

We also suggest that researchers consider how communities might benefit from access to the data they provide, and how local capacity to use such data can identified as part of the research [ 44 , 45 ]. Ultimately, we suggest a participant-led rather than top-down approach in making these decisions. By having conversations with participating communities about how they would like data returned, researchers and participants may find solutions for data sharing that are meaningful to communities—often through the production of archival works. For example, co-author A.C.P. collected video footage that was returned to the community; in a project on the production of handicrafts, the resultant video footage was uploaded to the internet, where community members indicated that they (and future generations) would have better access to the footage. Researchers and communities may consider uploading digital media to community-run websites or even to YouTube. When considering data sharing, however, it is important to note that some types of data-storage facilities (e.g. computers, libraries, YouTube) may not be accessible or appropriate to their participants. One strategy used independently by three of the authors (H.C., J.A.B. and A.N.C.) is to provide SD cards to participants with project-related video, photo and audio data which can be read by mobile phones. This allows information to be either kept secret by phone owners or to be shared. Another option used by co-author M.B.M. was to draw on her research to facilitate workshops for the writing and publication of a collectively sourced cultural history; she made copies of the book freely available to local schools [ 53 ]. A two-way dialogue between researchers and participants is needed to arrive at a reasonable solution based on participants’ preferences.

Data sharing may also include shifting ownership of research outputs to participants in a more explicit manner. For example, there is a set of recommended practices for research conducted within Indigenous communities in Canada which stipulate that data remains the property of the participating communities [ 54 ]. It is important to meet the ethical standards of communities as well as those of government and research institutions (e.g. universities). For some types of data (e.g. open access data sharing), this may include carefully anonymizing results before transferring ownership in order to protect individual or community identities. However, we recognize that researchers will need to consider the ethics of publishing information from study communities alongside the requirements of funding agencies and institutional review boards, as well as the priorities of open science. We suggest that the research be designed (and budgeted) to allow time to return to the study communities to present and discuss the results and these issues, if possible, prior to publication. For example, the Wenner-Gren foundation has a grant designed to enable grantees to return to their research location (e.g. http://www.wennergren.org/programs/engaged-anthropology-grant ).

Far too often, little attention is paid to the politics of representation when disseminating research results more widely, especially in online forums (including social media). It is important that all stakeholders, including all collaborating researchers, assume responsibility for the language used to describe results, whether by press offices or journalists or by the researchers themselves, as well as for the use of photographs, videos, audio recordings, material culture and artefacts in research and public outreach efforts. The recording and use of these materials should be addressed in the process of informed consent (see above). Sensationalizing or exoticizing images or language not only demeans study communities but can also undo years of careful community-based work. These practices are unethical because they may misrepresent participants; they can also affect relationships between study communities and field researchers. All researchers can bear these issues in mind and exert more control over public dissemination of their work. One suggestion to address these potential issues is for investigators themselves to write the press releases or, minimally, to review and approve press releases and associated images prepared by third parties.

(c) Research design and methods

Data collection methods largely stemming from WEIRD intellectual traditions are being exported to a range of cultural contexts. This is often done with insufficient consideration of the translatability (e.g. equivalence or applicability) or implementation of such concepts and methods in different contexts, as already well documented [ 15 – 20 ]. It is critical that researchers translate the language, technological references and stimuli as well as examine the underlying cultural context of the original method for assumptions that rely upon WEIRD epistemologies [ 55 , 56 ]. This extends to non-complex visual aids, attempting to ensure that even scales measure what the researcher is intending (see [ 57 ] for discussion on the use of a popular economic experiment in small-scale societies).

For example, in a developmental psychology study conducted by Broesch and colleagues [ 58 ], the research team exported a task to examine the development and variability of self-recognition in children across cultures. Typically, this milestone is measured by surreptitiously placing a mark on a child's forehead and allowing them to discover their reflective image and the mark in a mirror. While self-recognition in WEIRD contexts typically manifests in children by 18 months of age, the authors tested found that only 2 out of 82 children (aged 1–6 years) ‘passed’ the test by removing the mark using the reflected image. Note that they began testing younger children and moved up the developmental trajectory, eventually testing older children who also did not ‘pass the test’ by Western standards. Their results are unexplained by existing developmental theories. The authors' interpretation of these results is that performance reflects false negatives and instead measures implicit compliance to the local authority figure who placed the mark on the child. This raises the possibility that the mirror test may lack construct validity in cross-cultural contexts—in other words, that it may not measure what it was designed to measure.

An understanding of cultural norms may ensure that experimental protocols and interview questions are culturally and linguistically salient. This can be achieved by implementing several complementary strategies. A first step may be to collaborate with members of the study community to check the relevance of the instruments being used. Incorporating perspectives from the study community from the outset can reduce the likelihood of making scientific errors in measurement and inference [ 54 ].

An additional approach is to use mixed methods in data collection, such that each method ‘checks’ the data collected using the other methods. A recent paper (see [ 59 ]) provides suggestions for a rigorous methodological approach to conducting cross-cultural comparative psychology, underscoring the importance of using multiple methods with an eye towards a convergence of evidence. A mixed-method approach can incorporate a variety of methods such as participant observation, semi-structured interviews and experiments. For example, in their study on mate choice among Himba pastoralists of Namibia, Scelza and Prall [ 60 ] first employed semi-structured discussion groups and informal conversations with study participants. After better understanding the ways in which Himba themselves express desired characteristics of formal and informal partners, the researchers incorporated these characteristics into a ranking task [ 61 ]. Similarly, in a study of contraceptive use in rural Poland [ 62 ], qualitative interviews prior to formal data collection allowed the researchers to understand that the distinction between ‘modern’ and ‘traditional’ methods elicited very different (and apparently underreported) use than when the distinction was made between ‘natural’ and ‘artificial’.

More generally, asking participants to talk aloud [ 63 ] as they complete a task or asking follow-up (debriefing) questions at the end of the experiment may allow researchers to better understand the decision-making processes at play (see [ 64 , 65 ] for recommendations and examples). Some guidelines for incorporating participant observation and qualitative interviews are available from Bernard [ 63 ] and Matsumoto & Van de Vijver [ 66 ]. For definitions, examples, and a full discussion of different kinds of bias in social science measures, see Van de Vijver & Tanzer [ 67 ]. There are also a number of Indigenous research methodologies that have been well-developed and extensively applied. For example, the Pagtatanong-tanong interview method developed and documented in the Philippines maximizes respect and equality by allowing equal time for participants and interviewers to engage in questioning (see [ 68 ]). We recommend using these resources as a guide prior to developing study methods and prioritizing the collection of baseline data, field testing instruments, and soliciting and incorporating community feedback before data collection commences.

2. Conclusion

Our aim here is to add to the growing dialogue on best practices in social science research, particularly as they relate to cross-cultural studies involving research participants from widely variable communities around the world. As research funding and publication of cross-cultural studies continues to expand across the social sciences, it is necessary to acknowledge the unique methodological and ethical challenges of this research. With scholars from a wide range of disciplines increasingly engaging in such research, often with little or no formal field training or experience working outside of post-industrialized contexts from the global North, special consideration of (a) study site selection, (b) community involvement and (c) locally appropriate implementation of research design and methods is essential. Our intention is not to discourage researchers from embarking on cross-cultural studies, but rather to alert them to the multi-dimensional considerations at play, ranging from study design to participant inclusion, and to encourage constructive exchange and collaboration with participant communities. We suggest one solution may be for researchers new to cross-cultural studies to collaborate with field researchers who have established, long-term relationships with communities. We are not proposing that long-term researchers should be considered gatekeepers to the communities where they work—that role should only be played by the communities themselves. Rather, we are suggesting that individuals with established ties to a community may be useful guides for locally relevant materials, locally appropriate ethical and practical guidelines, and local contacts.

Transdisciplinary dialogue on principles and practices are useful not only for researchers (at all career stages) but also for funding agencies and reviewers evaluating twenty-first-century cross-cultural research across multiple domains of science. In short, deeper consideration of how to select sites for comparative investigations, how to engage target communities, and how to design research protocols in culturally sensitive ways will allow researchers to address some of the ethical and logistical challenges highlighted here—issues that all of the co-authors of this piece continue to grapple with in our own research and the communities with whom we work.

Supplementary Material

Acknowledgements.

We thank the host communities with whom we have worked for their patience, collaboration and the knowledge that they have shared. We also thank Claudia Jacobi and the staff at MPI-EVA in Leipzig for their work in hosting the workshop, and Shani Msafiri Mangola, Elspeth Ready, Tim Caro and Daniel Benyshek for helpful feedback on earlier drafts of this manuscript. T.B. also thanks the Coady International Institute, particularly Allison Mathie and Gord Cunningham for hosting, teaching and supporting her transition to participant-engaged research.

Data accessibility

Authors' contributions.

All authors contributed to the idea, outline and structure of the manuscript at the MPI workshop. T.B. and A.N.C. wrote the first draft of the manuscript with edits by M.B.M. The following authors provided comments and edits on manuscript drafts: J.A.B., H.C., K.H., M.K., R.G.N., A.C.P., C.R. and B.S. The following authors contributed to discussions at the workshop: B.A.B., A.D.B., R.M., S.P., I.P., B.P., E.A.Q., K.S., J.S. All authors edited and approved the final manuscript.

The workshop that generated the basis for this manuscript was funded by the Department of Human Behaviour, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany (proposal written by authors M.K. and R.G.N. and coordinated by M.K., R.G.N., K.S.). J.S. acknowledges funding from the French National Research Agency under the Investments for the Future (Investissements d'Avenir) programme (ANR-17-EURE-0010).

Competing interests

We declare we have no competing interests.

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Cross-cultural research is a systematic and comparative study of different cultures, societies, or cultural groups to understand and analyze the similarities, differences, and interactions between them. This type of research aims to explore various aspects of human behavior, beliefs, values, customs, and practices across diverse cultural contexts. Cross-cultural research often involves the collection and analysis of data from multiple cultures or cultural groups to uncover patterns, relationships, and insights that can contribute to our understanding of how culture influences various aspects of human life.

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Human Relations Area Files

Cultural information for education and research, basic guide to cross-cultural research.

Carol R. Ember and Melvin Ember

This brief guide takes you through the basic steps of a cross-cultural study using the HRAF Collection of Ethnography– on paper, fiche, or online ( eHRAF World Cultures ).

After reviewing the history of the HRAF Collection of Ethnography, we start with a discussion of the kinds of questions cross-cultural researchers investigate, and why they want to investigate them. Then we discuss how to choose a sample, how to develop measures, and how to analyze results.

Introduction

The growing concern of students, scholars, and the general public to understand ethnic conflict, cultural diversity, and global problems has generated a demand for educational and research programs emphasizing the worldwide, comparative study of human behavior and society. The development of cross-cultural and area studies requires a large mass of readily available, organized cultural information; conventional sources of such information are widely scattered and often inaccessible, and often too expensive to assemble and utilize effectively. The HRAF Collections are designed to overcome this traditional barrier to research.

The HRAF Collection of Ethnography is a unique source of information on the cultures of the world, and as of April 2008 the complete collection contained over a million pages of indexed information on about 400 different cultural, ethnic, religious, and national groups around the world. The collection was developed by the Human Relations Area Files, Inc. (HRAF), a non-profit research organization based at Yale University. For almost fifty years, HRAF has served the educational community and contributed to an understanding of world cultures by assembling, indexing, and providing access to primary research materials relevant to the social sciences, and by stimulating and facilitating training and research in these fields.

Development of the HRAF Collections began with the belief that enduring generalizations about human behavior and culture will emerge from a wealth of knowledge about the ways in which the different peoples of the world live. In 1937 at the Institute of Human Relations, Yale University, under the direction of the Institute’s Director, Mark A. May, and Professor George Peter Murdock, a small group of researchers attempted to design a system by means of which the cultural, behavioral, and background information on a society might be organized. A fundamental part of that system was a universal topical classification scheme, the Outline of Cultural Materials–OCM (Murdock et al. 2008), which is still integral to the work HRAF does today.

In 1949, the Human Relations Area Files was incorporated in the State of Connecticut, with Harvard University, the University of Oklahoma, the University of Pennsylvania, the University of Washington, and Yale University as its founding member institutions. These five were joined within the year by the University of Chicago, the University of North Carolina, and the University of Southern California. Today, hundreds of colleges, universities, libraries, museums, and research institutions in the United States and other countries have full or partial access to the HRAF Collection of Ethnography. (See the member list for institutions that are active members of the online version, eHRAF World Cultures.)

The HRAF Collection of Ethnography contains mostly primary source materials – mainly published books and articles, but including some unpublished manuscripts and dissertations – on selected cultures or societies representing all major regions of the world. The materials are organized and indexed by a unique method designed for rapid and accurate retrieval of specific data on given cultures and topics. HRAF’s system of organization and classification of source material presents information in a manner that significantly increases the usefulness of original source materials. Researchers can use the Collection of Ethnography in four different media: the original paper files, fiche, and on the World Wide Web. Until 1958, the HRAF Collection was produced and distributed as paper files: source materials were manually reproduced on 5″ x 8″ paper slips called File pages, and then filed by subject (OCM) category and by culture. Wider distribution of the collection was facilitated in 1958 with the development of the HRAF Microfiles Program. Materials from the paper files were processed into microfiche and issued in annual installments to participating institutions; Installment 42 was the last microfiche series issued to members.

In the 1980’s, HRAF began developing an electronic publishing program with the intention of distributing the HRAF Collection of Ethnography exclusively through electronic means. The Cross-Cultural CDs were the first result of this effort, providing researchers with ten collections on such topics as old age, marriage, religion, and human sexuality, excerpted from HRAF’s 60-Culture Probability Sample Files (PSF). In 1993, the first installment of the full-text HRAF Collection of Ethnography on CD-ROM (eHRAF) was issued to members with the plan of converting the entire 60-Culture PSF, plus new files covering North American immigrant groups, by the year 1999. Additional installments are added annually. As of April 2008, there were 165 cultures online .

Using eHRAF is a relatively straightforward process. Mechanics of use and research techniques are similar in many respects to standard library practices; searching follows the same principles and techniques, such as Boolean logic, that are used for other electronic educational collections.

Organization and Classification

In the paper and fiche versions of the HRAF Collection of Ethnography all documents that contain information about a particular culture are grouped together in a collection for that culture. Each culture collection is identified by a unique alphanumeric code according to the Outline of World Cultures–OWC (Murdock 1983) . In the OWC all the cultures are classified according to geographical regions:

A – Asia E – Europe F – Africa M – Middle East N – North America O – Oceania R – Eurasia (cultures located in the former Soviet Union and Russia) S – South America

There is one exception to this system: Muslim societies in Africa are classified as being in the Middle East. In its recent literature, HRAF has begun to organize those Muslim cultures under Africa, although they retain the same OWC code.

All the cultures in the paper and microfiche version of the HRAF Collection of Ethnography are grouped into these eight regions. Thus, all the documents pertaining to African cultures are grouped together and their OWC begins with “F.” Each of the major regions is then subdivided, usually on a political basis, into sub-regions designated by the addition of a second letter: “FF” designates the country of Nigeria and its component cultural units, while “SC” indicates that the culture described is in the South American country of Colombia. Finally, within each sub-region, more specific units are defined and assigned a number; these may be country entities, such as “RD01” for Ukraine, or “cultural” units such as “FL12” for the Maasai. Each culture is therefore listed in its regional, political, and cultural context within the Collection.

In eHRAF World Cultures, the OWC number is de-emphasized and cultures are ordered by major geographical regions arranged in alphabetical order: Africa, Asia, Europe, Middle America and the Caribbean, Middle East, North America, Oceania, and South America. The OWC number is listed in the Culture Profile (Browse/Cultures).

Selection of Cultures

Several thousand cultures are listed in the OWC, but not all the cultures on the list are included in the HRAF Collection of Ethnography. The cultures in the Collection are selected mainly on the basis of the following criteria:

(a) Maximum cultural diversity : the cultures should represent, as far as possible, the known range and variety of cultural types in terms of language, history, economy, and social organization.

(b) Maximum geographical dispersal : the cultures should be geographically representative of all major world areas and all major ecological settings.

(c) Adequacy of literature : within the scope of the two preceding criteria, the cultures should have a quantitatively and qualitatively adequate literature coverage.

(d) Special initiatives:  from time to time HRAF undertook to build special kinds of collections such as: immigrant and other subcultures within North America; cities; and country files.

Source Materials

Once the decision has been reached to build a collection on a particular culture, extensive bibliographic research is undertaken to identify as thoroughly as possible all of the significant literature on that culture. HRAF also solicits the advice and expertise of specialists. As always, researchers are encouraged to inform HRAF of any salient material which might have escaped notice.

The materials processed for the Collection of Ethnography are largely descriptive rather than theoretical, with the great majority being primary documents resulting from field observation. The ideal document is one which consists of a detailed description of a culture, or of a particular community or region within that culture, written on the basis of prolonged residence among the people documented by a professional social scientist. Many documents which do not meet all the criteria are included in the Collection of Ethnography because they are still important pieces of information; in fact, it is likely that they may be the only sources available for particular time periods, regions, or subjects. Thus the collection for each culture may contain documents written by travelers, missionaries, colonial officials, traders, etc. The Collection of Ethnography provides researchers with a comprehensive picture of life in one or more communities and in one or more time periods.

Classification

Every page in each document is indexed and assigned any number of appropriate subject category codes according to the classification scheme in the Outline of Cultural Materials (OCM) (Murdock et al. 2008; online versions); the subject codes are sometimes referred to as OCMs. The OCM consists of 710 subject categories plus a category numbered “000” for unclassified materials. The 710 categories are grouped into seventy-nine major subject divisions, each assigned a three-digit code ranging from 100 (Orientation) to 880 (Adolescence, Adulthood, and Old Age). Within each major subject division, up to nine more specific categories are defined. For example, the (Family (590) division is subdivided into seven more specific subject categories as follows: Residence (591), Household (592), Family Relationships (593), Nuclear Family (594), Polygamy (595), Extended Families (596), and Adoption (597).

Each category in the OCM includes a brief descriptive statement, indicating the range of information which may be classified under that category. Beneath this statement is usually a list of cross-references to other categories under which related information may be classified.

The OCM contains a detailed index which directs the researcher to OCM numbers relevant for their search. The OCM subjects are clearly defined in the OCM, but a few are essential to effective use of the HRAF Collection of Ethnography and bear mentioning here.

Every document page has at least one OCM assigned to it. If there are no pertinent subject categories, “000” indicating non-classified data is applied. In the paper and microfiche, the OCMs are written in roughly where the subject starts. Sometimes an OCM will apply to a particular sentence, although most OCMs apply to at least a section of a paragraph. For eHRAF all OCMs are located at the paragraph level. If five consecutive paragraphs discuss categories 585, 578, and 602, all three OCMs will appear at the beginning of each of the five paragraphs until the subject changes.

What’s the Question?

Cross-cultural (worldwide comparative) researchers ask four kinds of questions. The first is descriptive and deals with the prevalence or frequency of a trait: What percentage of the world’s societies practice polygyny? Which is the most important subsistence activity among food collectors – gathering, hunting, or fishing? How common is female infanticide? A second kind of question considers the causes of a trait or custom. Questions: Why is polygyny permitted in most societies known to anthropology? Why do women (as opposed to men) do most of the agricultural work in some societies? Why is the extended family the customary form of household in many societies? The third kind of question explores the consequences or effects of a particular trait or custom. What are the effects on infant care of high involvement of women in subsistence activities? Does punitive childtraining affect the frequency of warfare? The fourth question, which is not significantly different from the second and third, is a relational question. Rather than postulating causes or consequences, a researcher may simply ask how a particular aspect of culture may be associated with some other aspect(s). For example: Is there an association between most important subsistence activity and level of political complexity?

Of these four questions, the causal question is the most challenging because it does not completely specify what the researcher needs to do. The descriptive question tells the researcher what to count. The “consequence” and “relational” questions both specify two sets of phenomena that may be related. But the causal question does not tell the researcher where to look for causes. It only specifies what scientists call the dependent variable (the thing to be explained).

Think of the causal question as analogous to the format of a detective story. After a murder is committed the detective may know a lot about the crime, but not “whodunit” or why. Finding the solution usually entails hypothesizing about suspects and their possible motives and opportunities, eliminating the implausible possibilities, and concluding who is probably the culprit.

Similarly, in science, the pursuit of causes involves the testing of alternative explanations or theories which purport to say why something is the way it is. The researcher who chooses a causal question needs to identify plausible explanations or theories to test and to decide on a strategy (for collecting and analyzing data) that could falsify or disconfirm explanations. If all theories fail, researchers must come up with new theories. Although these requirements may suggest that the researcher who searches for causes may need to act differently from other researchers, this is really not the case, as we shall see.

The basic strategy for examining relationships in cross-cultural research is the same, whether the relationship involves presumed causes, consequences, or just hypothesized association. To illustrate that strategy, let us turn to an example of a test of a causal explanation.

In the first study we did together (M. Ember and C.R. Ember 1971), our question was: Why do some societies practice matrilocal residence and others patrilocal residence? We started where most people start – with explanations found in the literature. One of the most common was the idea that the division of labor based on gender in primary subsistence activities would largely determine residence after marriage (Lippert 1931: 237; Linton 1936: 168-69; Murdock 1949: 203ff.) In other words, female dominance in subsistence should produce matrilocality; male dominance should produce patrilocality. What makes this a causal explanation are the words “determine” and “should produce,” which are equivalent to using the word “cause.” But, as philosophers of science tell us, causes cannot be directly verified. Even if we can be sure that presumed causes preceded the presumed effects, we cannot rule out the possibility that something else is the real cause.

So how do we test such a causal explanation? The simplest way is to examine a relationship that should be true if the theory is correct, and then make a statistical test to see if the predicted relationship actually occurs significantly more often than would be expected by chance. In our own study of matrilocal versus patrilocal residence, we derived the following prediction from the “division of labor” theory: if females did relatively more work than males, residence would tend to be matrilocal; if males did relatively more subsistence work, residence would tend to be patrilocal. Notice that although the prediction (or more formally the hypothesis) has almost the same form as the theory we stated above, it differs in a fundamental way: the hypothesis simply predicts an association between two variables and says nothing about causality. Still, if two things are causally related, they should be statistically associated.

In our case, when we examined the association between division of labor and residence in a worldwide sample of societies, the predicted association was not found . This led us (and later Divale [1974]) to reject the theory that division of labor largely determines residence. After rejecting the “division of labor” explanation (at least as a major cause) we went on to test other explanations. Eventually we ended up developing a new theory that internal warfare (warfare within the society) would produce partilocal residence, and purely external warfare (particularly if women do a great deal of subsistence work) would produce matrilocal residence. Note that division of labor remains a partial cause in our explanations. Note too that even if a predicted relationship is supported, it may still be open to different interpretations. Indeed, Divale (1974) offers a vary different explanation for the obtained relationship between type of warfare and residence.

The study we just discussed illustrates the fundamental assumption of worldwide cross-cultural (or holocultural) research; if a theory has merit, the presumed causes and effect should generally be associated synchronically (see J. W. M. Whiting 1954; K. F. Otterbein 1969; R. Naroll, Michik, and F. Naroll 1976). A synchronic association is one that involves data (for each sample case) from more or less the same point in time, as if we were examining a large number of “ethnographic snapshots,” each one capturing a society at a single point in time. The cross-cultural method therefore provides a way of eliminating theories that have no predictive value. Theories that postulate causes, consequences, or relationships are tested in the same way; that is, by looking to see if predicted associations obtain.

Cross-cultural researchers must decide what societies to examine. No one can examine all cultures; even if one could, the labor and time costs involved would not justify doing so. The most important operating principles in a scientific test of a hypothesis are: 1) to choose a sample that is representative of some universe of societies the researcher wants to generalize the results to; and 2) to use a large enough sample such that the results are likely to be true for the larger universe of cases. As yet, there is no complete list of the world’s cultures to sample from, so researchers cannot do what is ideal, which is to sample randomly from a complete list. Instead, cross-cultural researchers usually sample from one of a number of published cross-cultural samples. (These lists can be thought of as “sampling frames.”) The most commonly used are (from larger to smaller): the Ethnographic Atlas (Murdock 1962 ff.); the “summary” Ethnographic Atlas (Murdock 1967); the Atlas of World Cultures (Murdock 1981); the Human Relations Area Files (HRAF) Collection of Ethnography (annually distributed by the Human Relations Area Files); the Standard Cross-Cultural Sample (Murdock and White 1969); and the HRAF Probability Sample Files (Naroll 1967; Lagace 1979), which is a subset of the entire HRAF Collection of Ethnography. While none of these samples is perfect, the important point about all of these lists is that they were not designed to support any researcher’s pet idea or theory. In contrast, a set of cases chosen from a researcher’s own personal library would be scientifically suspect.

Why use the HRAF Collection of Ethnography?

Most of the samples mentioned above contain bibliography (or pointers to bibliography) and at least some coded information on traits of interest to a variety of researchers. The HRAF Collection of Ethnography is different in that it contains no precoded data, but full texts indexed by subject matter and grouped by culture for the rapid retrieval of particular kinds of information. If you want to read about a particular aspect of culture and make your own coding decisions on a sample of societies, the HRAF collection is ideal because you do not have to collect all the books and articles on each of the cultures and then search for a particular subject through all the texts. HRAF’s subject index, the Outline of Cultural Materials (Murdock et al., 2008; online version ), can be used to identify particular subject categories to look at to find the information of interest to you.

If you are working from the print version of the Outline of Cultural Materials (OCM for short), the easiest way to find a subject category is by using the extensive index in the back of the OCM. This index will point you toward a number of possible numbered subject categories. When you read about these subjects in more detail, you will find out if the subject categories are appropriate. The OCM system is mostly hierarchical in that the first two digits usually reflect the major subject category. So, for example, all the “59s” (591-597) refer to the major subject labelled “Family.” The last digit is a subcategory (e.g., 596 is “extended families”).

If you are working on eHRAF, the A-Z Index can be found under either under Browse/Subjects or in Advanced Search when you press “Add Subjects.” There is also a list of OCMs organized by Major Subject and a list in OCM or numerical order. The A-Z list has a filter so that if you start typing a word it will try to find it. If you are in Advanced Search you can execute your search once you have chosen a subject category. Often researchers will need to search for more than one subject category to ensure that they will find what they are looking for. Keep in mind that not all ethnographers discuss all topics, so some categories will be empty for some cultures. It takes trial-and-error to find what you need and tailor a search to your needs.

Sampling within the HRAF Collection

It is rarely necessary to use the entire HRAF collection for comparative studies. The only reason it might be necessary to examine all the cases is if some trait or custom occurs rarely or is only rarely described. In that case, researchers might have to scan all the societies to find enough cases of a particular type. Examples of relatively rare traits are age-set systems, cannibalism, and woman-woman marriages.

Researchers use a variety of strategies to sample the collection. If researchers want to use some already coded data (coded previously by themselves or other researchers) for their study, they usually choose to limit themselves to those sample cases for which the desired precoded data are available. Some researchers find that the HRAF Collection of Ethnography speeds up their data retrieval so much that they use it for as many cultures as they can and then look up books and articles for the remaining cultures. Others choose the overlap between the HRAF sample and another sample. The important thing to keep in mind in using information from two different samples is that the information in the different samples may pertain to different time periods and different communities. Since cultures change over time and vary from community to community, it is extremely important to make sure that the same-named cases in the overlapping samples actually are the same in time and place. Otherwise, the researcher is introducing error.

For example, suppose one is examining the possible relationship between male mortality rates in warfare and frequency of polygyny (see M. Ember 1974 for a test of the hypothesis that high male mortality in warfare should be associated with appreciable polygyny). For information on a given society with regard to male mortality in warfare, one would look in categories Mortality (165), Instigation of War (721), and Aftermath of Combat (727) and might find ethnographic material from 1890 indicating that many men died in warfare. For information on extent of polygyny (category 595) the researcher may find the best information to be from 1950. If you used these two pieces of information (one from 1890 and the other from 1950) you might very well have a case that looks like it does not support your hypothesis. This would be an error if the society had appreciable polygyny in 1890 but did not have much in 1950. Pacification by external authorities might have eliminated war, thus evening out the sex-ratio, and thereby lessening polygyny. In this instance, the data from either 1890 or 1950 might support the hypothesis (high male mortality/high polygyny or low male mortality/low polygyny), but mixing data from different time periods would have created an “error.”

The online HRAF Cross-Cultural Concordance (first published as the Computerized Cross-Cultural Concordance –see C. R. Ember 1992) was developed to help researchers see if times and places match across different samples. One of the most useful aspects of this concordance is that it gives the researcher the appropriate sources to look at in the HRAF Collection of Ethnography (in paper, microfiche or now online as eHRAF World Cultures ) to see if she or he wants to match cases in another sample. HRAF processes an extensive set of sources for each society included in the archive. Usually there are multiple time and place foci, so it is important that a researcher attend to the need to choose exactly the right focus. Researchers who want to use data already available from other samples commonly use the Ethnographic Atlas (EA–in its full form or the summary form (Murdock 1962-1971; Murdock 1967) or the Standard Cross-Cultural Sample (SCCS–Murdock and White 1969) The codes for the Standard Cross-Cultural Sample (published by many different authors) have appeared in two journals ( Ethnology and Cross-Cultural Research [formerly Behavior Science Research ] ). Many have been reprinted in Barry and Schegel’s (1980) Cross-Cultural Codes and Samples , and have been put into computer format for the World Cultures electronic journal. The codes for the Ethnographic Atlas can now be found online in D-PLACE (https://d-place.org/). A discussion of the matches between HRAF or eHRAF and the Standard Cross-Cultural Sample and the Ethnographic Atlas can be found in C. R. Ember (2007). In eHRAF you can now find the matches to the SCCS sample  and the matches to the EA sample ; we also display the lists of cases for the SCCS and the EA on our home page. However, if you want to match two or more samples and find the appropriate sources you can also use the filter function in the HRAF Cross-Cultural Concordance .  For example, if you want the documents that are in eHRAF World Cultures that are also in the SCCS and the full EA, you can click all three. A file can also be exported of the entire set of matches.

If the researcher does not need information from outside HRAF, sampling from the HRAF Collection of Ethnography can proceed differently. The HRAF Collection itself can be used as a sampling frame (a list to sample from) and researchers can randomly choose cases from that list by using a table of random numbers. (Your library might not have the complete collection. Check with your librarian for the cultures found in your library.)

We would however urge caution in using the HRAF collection as a whole because it has a mixture of unit types. As explained earlier, due to some special programs, not all collections would be considered “anthropological societies.” For example, the collection on Cuban Americans should best be compared with other North American subgroups rather than as a society. A better choice would be to use one of the subsets of eHRAF designed to be representative.

There are three samples within HRAF that can be considered representative. They are: The Probability Sample Files, the Standard Cross-Cultural Sample,  and a Simple Random Sample.

Probability Sample Files

The subset of HRAF known as the Probability Sample Files (PSF) is a special kind of random sample called a stratified random sample. The world was divided into 60 culture areas (strata) and one case from each area was randomly chosen from a list of societies that met certain criteria (such as whether one of the ethnographers stayed for more than a year; having more than 1,000 pages of ethnography). It is important to note that modern, industrialized societies were excluded, as were political empires, societies that were only described in prehistory or history, and societies that did not include specific information about the people and their culture. See Naroll (1967) for the rules used in selecting the sample cases. When you are using eHRAF World Cultures you can, after executing a search, narrow your search to the PSF.

Standard Cross-Cultural Sample

The SCCS sample, developed by Murdock and White (1969) is also a stratified sample, but it was based on dividing the world into 200 culture areas (strata). The authors tried to find one well-described society in each of 200 areas, but ended up with 186 societies. For those culture areas with multiple cases within a culture area, the authors used their judgment about what case to include. Unlike the PSF, which has multiple time periods and multilple subcultues represented, the creators chose a specific time and place focus for each society. They usually opted for the earliest time frame that had comprehensive coverage.

Simple Random Sample

The Simple Random Sample (SRS) is in the process of being built. It was drawn by choosing from C. Ember’s (1992) concordance of cross-cultural samples. The only criteria was that there had to be at least the equivalent of one ethnography. Because cultures are added randomly, the cultures in that sample can be considered  representative sample of a fairly large list of cultures around the world (from about 8 different cross-cultural samples. Since as of the present there are less than 30 cultures, most researchers would not consider this a large enough sample. However, these cultures could be added to the PSF sample.

Size of Sample

What is a large enough sample? Statisticians have worked out formulas for calculating the size of the representative (random) sample that is needed to obtain a significant result (one likely to be true). The samples needed are usually much smaller than you might imagine. If a relationship is strong, a random sample of 20-30 is sufficient. (Weak associations can be significant only in large samples.) By using the random sampling strategy, researchers can always add cases randomly to increase sample size. Random sampling also enables researchers to estimate whether a phenomenon of interest occurs frequently enough to be studied and whether the measures adopted are usable on the available data.

While most people assume that “bigger is better,” bigger samples require much more time and effort and expense. And they may not yield much more information or accuracy than a smaller random sample. Political opinion polls are a case in point. Samples of a few hundred to a few thousand people in the entire United States can often yield quite accurate predictions of elections.

The concepts in a predicted association (hypothesis) can be fairly specific, such as whether or not a culture has a ceremony for naming a newborn child, or they may be quite abstract, such as whether the community is harmonious. But whether the concept is fairly specific or not, no concept is ever measured directly. This is true in physical as well as in the social sciences. We are so used to a thermometer measuring heat that we may forget that heat is an abstract concept that refers to the energy generated when molecules are moving. A thermometer reflects the principle that as molecules move more, a substance in a confined space (alcohol, mercury) will expand. We do not see heat; we see the movement of the substance in the confined space.

The three most important principles in designing a measure are:

•  try to be as specific as possible in deciding how to measure the concept;
•  try to measure the concept as directly as possible;
•  if possible, try to measure the concept in a number of different ways.

The first principle recognizes that science depends upon replication; it is essential for other researchers to try to duplicate the findings of previous researchers, so researchers have to be quite explicit about what they mean and exactly how they measure the concept. The second recognizes that although all measurement is indirect, some measures are more direct than others. If you want to know how “rainy” an area is, you could count the number of days that it rains during December, but a better measure would be the number of rainy days on average over a number of years. The third principle is that since no measure exactly measures what it is supposed to measure, it is better, if possible, to have more than one way to tap the concept of interest.

Measures have to be specified for each variable in the hypothesis. Devising a measure involves at least four steps; 1) theoretically defining the variable of interest (in words or mathematically); 2) operationally defining the variable, which means spelling out the “scale” that the researcher has devised for measuring it; 3) telling the coder where to find the required information (in the case of research using the HRAF Collection of Ethnography, this means specifying which subject categories (OCMs) the coder should look at; in the electronic version one can also specify what words or combinations of words to look for and 4) pre-testing the measure to see if it can be applied generally to most cases. Designing a measure requires some trial-and-error. If the scale is too confusing or too hard to apply (because the required information is lacking), the measure needs to be rethought.

To illustrate the procedure, let us consider a variable that seems rather straightforward: the degree to which a society has extended family households. Although this concept may appear straightforward, it still needs to be defined. The researcher needs to state what an extended family means, what a household means, and how she or he will decide the “degree” to which a sample society has extended family households. The first thing would be to decide on what is meant by an “extended family.” The researcher may choose to define a family as a social and economic unit consisting minimally of at least one or more parents and their children; an extended family as consisting of two or more constituent families united by a blood tie; and an extended family household as an extended family living co-residentially; in one house, neighboring apartments, or in a separate compound. Having defined the concepts, the researcher must now specify how to measure the degree to which a society has extended family households.

Definitions are not so hard to arrive at. What requires work is evaluating whether an operational definition is useful or easily applied. For example, suppose by “degree” (of extended familyness) we operationally mean the percentage of households in a focal community that contain extended families. The range of possible scale scores is from 0 to 100 percent. Suppose further that we instruct our coders to rate a case only if the ethnographer specifies a percentage or we can calculate a percentage from a household census. If we are also using information from another study, we tell our coders to look at the Household (592) and Extended Family (596) for the same community specified in the other study and at the time specified in the other study (same time). (If we are not taking data from another study, we can ask our coders to pick a community and a time which is most thoroughly described with regard to household form.) If we did a pretest, we would find out that very few ethnographers tell us the percentage of extended family households. Rather they usually say things like, “Extended family households are the norm.” Or, “Extended families are typical, but younger people are beginning to live in independent households.” So our operational definition of percentage of extended family households, although perfectly worthy, may not be that useful if we cannot find enough societies with household censuses.

What can we do? There are three choices. We can stick to our insistence on the best measure and study only those societies for which a percentage is given. We may have to expand our search (enlarge our sample) to find enough cases that have such precise information. Or, we can redesign our measure to incorporate descriptions merely in words (no census material is available). Or, we can choose not to do the study because we can’t measure the concept exactly how we want to. Faced with these three choices, most cross-cultural researchers opt to redesign the measure so as to incorporate word descriptions. Word descriptions do convey information about degree, but not as precisely. If an ethnographer says “extended family households are typical,” we do not know if that means 50% or 100%, but we are very confident it does not mean 0-40%. And we can be fairly sure it does not mean 40-49%. If the relative frequency of extended families is related to something else, we should be able to see the relationship whether we measure in percentages or words.

A newly designed measure might read something like this : Code extended family households as

4) Very high in frequency if the ethnographer describes this type of household as the norm or typical in the absence of any indication of another common type of household. Phrases like “almost all households are extended” are clear indicators. Do not use discussions of the “ideal” household to measure relative frequency, unless there are indications that the ideal is also practiced. If there is a developmental cycle, such as the household splitting up when the third generation reaches a certain age, do not use this category. Use category #3 if the extended family household remains together for a substantial portion of the life-cycle and #2 if the household remains together briefly.

3) Moderately high in frequency if the ethnographer describes another fairly frequent household pattern but indicates that extended family households are still the most common.

2) Moderately low in frequency if the ethnographer describes extended family households as alternative or a second choice (another form of household is said to be typical).

1) Infrequent or Rare if another form of household is the only form of household mentioned and if the extended family form is mentioned as absent or an unusual choice. Do not infer absence of extended families from the absence of any discussion of family and household type.

don’t know if there is no information in the appropriate subject categories, or there is contradictory information for the same time and place from different sources.

The next step is to pre-test this measure. It may turn out that four distinctions are too difficult to apply, so a researcher might want to collapse the scale a little. If we decide to use the scale described above, what do we do when we do get numbers or percentages from the ethnographers for some cases? Most of the time, we can fit those numbers into the word scale. So, for instance, if 70% of the households have extended families, and 30% are independent, we would choose scale position 3. But we might decide to use two scales: a precise one based on numerical measurement (percentages), the second a vaguer one based on words (C. Ember et al. 1991 recommend that we use both types of scale when we can). The advantage of using two scales is that the more precise (quantitative scale) should be more strongly related to other variables than the less precise scale, which result would increase confidence in the relationships found.

Measuring a concept like the degree to which a society has extended families may not be easy. But it is not that difficult either, because ethnographers usually attend to basic economic, social, and political features of a society. We can think of these things as “standard cultural observables.” Of course, there are concepts which are much more difficult to operationalize using ethnographic data, because ethnographers do not conventionally attend to these subjects. For instance, few ethnographies contain information that would allow construction of an indicator of rainfall variability, pH of the soil, or number of minutes per day adults spend in housework. For these types of information, researchers may decide to alter their operational definitions to make use of the data that are available. A better research strategy may be to use other kinds of data outside of the HRAF Collection of Ethnography. Some libraries have worldwide climate records. This information can often be linked to ethnography by looking up the nearest weather station (in subject category Research and Development, 654) or longitude and latitude in subject category Location (131) of the society.

Concepts may be difficult to operationalize for other reasons. They may be quite abstract, like the concepts of community solidarity or the relative status of women. These two are not only abstract, but they deal with information which is not usually discussed in conventional ethnographic topics. Information relevant to status might be found under discussions of kin group decisions, political decision-making, relationships of people within the household, sexual rights and obligations, how marriages are arranged, etc.

Research by Martin Whyte (1978b) suggests that it is preferable to avoid rating very abstract variables such as “the status of women.” Rather researchers should probably confine ratings to more specific variables, as Whyte himself did. Whyte chose 52 very specific variables to assess the status of women. These variables included the degree to which women had political roles, the importance of female gods, how easily women could get divorced, etc. Whyte found that the various aspects of status did not relate to each other. He concluded that if a researcher wants to discuss status it would be preferable to discuss at least 10 different (and independent) dimensions of status. Furthermore, when he tested for the possible bias in reporting by male versus female ethnographers (Whyte 1978a), he found that whatever bias may exist is more likely to be found in the reporting of more abstract (versus more specific) matters. This suggests that codes should be designed to tap very specific aspects of a phenomenon.

Researchers can always use a variety of scaling procedures to make specific measures into combined or more general measures, as many have done to measure degree of cultural complexity (combining ratings of specific features such as type of subsistence, average size of communities, level of political integration, etc.)

Analysis of Results

When the researcher has measured the variables of interest for all sample cases, he or she is ready to see if the predicted relationship actually exists in the data. After all, there are likely to be exceptions to the predicted relationship. Do the exceptions invalidate the prediction? How many exceptions would compel a rejection of the hypothesis? It is precisely here that cross-cultural researchers usually resort to statistical tests of significance.

Statisticians have devised various tests that tell us how “perfect” a result has to be for us to believe that there is probably an association between the variables of interest, that one variable generally predicts the other. Essentially, every statistical result is evaluated in the same objective way. The question is asked: What is the chance that this result is purely accidental, that there is really no association at all between the two variables? Although some of the mathematical ways of answering this question are rather complicated, the answer always involves a probability value (or p-value), the likelihood that the observed result or a stronger one could have occurred by chance. So, if a result has a p-value of less than .01, this indicates that there is less than one chance in one hundred that the relationship observed is purely accidental. A p-value of less than .01 is a fairly low probability; most social scientists conventionally agree to call any result with a p-value of .05 or less (five or fewer chances in one hundred) a statistically significant or probably true result.

In a study we did with Burton Pasternak on extended family households (Pasternak, C.R. Ember and M. Ember 1976), we tested the hypothesis that incompatibility of activity requirements would generally explain why people may choose to live in extended family households. By incompatability of activity requirements we meant that an adult in the household was required to perform two activities in different places at the same time. A common example for women is childtending and agricultural work in the fields. An example for men is working away from home for wages and having to plow the fields. If the household includes two or more families, i.e., if there is an extended family household, there will likely be two adults of each gender to perform the required tasks. We decided to read and code ethnography to measure incompatibility requirements first, before we knew what the household form was, and then we subsequently looked up previously published coded data on the presence or absence of extended family households. We decided not to code both variables (incompatibility of activity requirements and extended family households) ourselves because we did not want our hypothesis to influence our judgments. The sample investigated was chosen by randomly sampling 60 cultures from the overlap between the HRAF Collection of Ethnography and the Ethnographic Atlas (Murdock 1967). Even though we were only able to code 23 of the sample societies, the statistical test of the relationship between incompatibility of activity requirements and extended family households was statistically significant. The p value was .003, which meant that the result was likely to occur by chance just 3 out of 1000 times. We were able to predict 11 out of 13 of the societies with extended family households and 8 of the 10 of the societies with independent family households.

Why should a probably true relationship have any exceptions? If a theory or hypothesis is really correct, one would presume that all the cases fit. There are many reasons that one cannot ever expect a perfect result. First, even if a theory is correct about a major cause of what one is attempting to explain, there may still be other causes that have not been investigated. Exceptions to the predicted relationship might also occur because of what has been called “cultural lag.” Cultural lag occurs when change in one aspect of culture takes time to produce change in another aspect. A sample society might be an exception to the predicted relationship, but it might fit the theory if the variables could be measured for a later time period. Measurement inaccuracy is another source of exceptions, because measurement error is usually random error and random error usually weakens statistical relationships. For example, if some cases in a straight-line relationship are inaccurately measured (either too high or too low) on even just one variable, those cases will not be located on the line of the relationship.

In addition to its statistical significance, a cross-cultural relationship should also be evaluated with regard to its strength, or the degree to which the dependent variable is predicted statistically. After all, the goal in research is to find strong predictors, not just statistically significant ones.

If confidence in an explanation is required, a single cross-cultural test is not enough. Replications by other researchers using other samples, tests against alternative explanations, and tests using other research strategies are also needed. This may seem tiresome, but good research always gives a cherished theory many chances to fail.

More Advanced Reading

For more advanced treatments of these topics, the reader is urged to peruse the articles in “Cross-Cultural and Comparative Research: Theory and Method. Special issue,” 1991. Behavior Science Research 25:1-270; and Carol R. Ember and Melvin Ember, Cross-Cultural Research Methods , 2nd eidtion.  AltaMira Press, 2009.

Barry, Herbert III, and Alice Schlegel. 1980. Cross-Cultural Samples and Codes. Pittsburgh: University of Pittsburgh Press.

Divale, William T. 1974. “Migration, External Warfare, and Matrilocal Residence.” Behavior Science Research 9:75-133.

Ember, Carol R. 2007.  Using the HRAF Collection of Ethnography in Conjunction With the Standard Cross-Cultural Sample and the Ethnographic Atlas .  Cross-Cultural Research 41:  396-427.

Ember, Carol R. with the assistance of Hugh Page, Jr., Timothy O’Leary, and M. Marlene Martin. 1992. Computerized Concordance of Cross-cultural Samples. New Haven: Human Relations Area Files.

Ember, Carol R. and Melvin Ember. 2009. Cross-Cultural Research Methods, 2nd edition. AltaMira Press.

Ember, Carol R., Marc Howard Ross, Michael Burton, and Candice Bradley. 1991. “Problems of Measurement in Cross-Cultural Research Using Secondary Data.” Behavior Science Research 25:187-216.

Ember, Melvin. 1974. “Warfare, Sex Ratio, and Polygyny.” Ethnology 13:197-206. Reprinted with afterthoughts in Marriage, Family, and Kinship: Comparative Studies of Social Organization. Melvin Ember and Carol R. Ember. 1983. New Haven: HRAF Press, pp. 109-124.

Ember, Melvin, and Carol R. Ember. 1971. “The Conditions Favoring Matrilocal Versus Patrilocal Residence.” American Anthropologist 73:571-94. Reprinted with afterthoughts in Marriage, Family, and Kinship: Comparative Studies of Social Organization. Melvin Ember and Carol R. Ember. 1983. New Haven: HRAF Press, pp. 151-198.

Lagace, Robert O. 1979. The HRAF Probability Sample: Retrospect and Prospect. Behavior Science Research, 14:211-229.

Linton, Ralph. 1936. The Study of Man. New York: Appleton-Century.

Lippert, Julius. 1931. The Evolution of Culture. George P. Murdock, trans. and ed. New York: Macmillan.

Murdock, George P. 1949. Social Structure. New York: Macmillan.

Murdock, George P. 1962-1971. Ethnographic Atlas, Installments I-XXVII, Ethnology 1-10.

Murdock, George P. 1967. Ethnographic Atlas: A Summary. University of Pittsburgh Press. Also Ethnology 6:109-236.

Murdock, George P. 1981. Atlas of World Cultures. Pittsburgh: University of Pittsburgh Press.

Murdock, George P. 1983. Outline of World Cultures, 6th ed. New Haven: Human Relations Area Files.

Murdock, George P., Clellan S. Ford, Alfred E. Hudson, Raymond Kennedy, Leo W. Simmons, John W. M. Whiting. 6th revised edition with modifications 2008. Outline of Cultural Materials. New Haven: Human Relations Area Files.

Murdock, George P., and Douglas R. White. 1969. “Standard Cross-Cultural Sample.” Ethnology 8:329-369.

Naroll, Raoul. 1967. “The Proposed HRAF Probability Sample.” Behavior Science Notes 2:70-80.

Naroll, Raoul, Gary Michik, and Frada Naroll. 1976. Worldwide Theory Testing. New Haven: Human Relations Area Files.

Otterbein, Keith F. 1969. “Basic Steps in Conducting a Cross-Cultural Study.” Behavior Science Notes 4:221-236.

Pasternak, Burton, Carol R. Ember, and Melvin Ember. 1976. On the Conditions Favoring Extended Family Households. Journal of Anthropological Research 32: 109-23. Reprinted with afterthoughts in Marriage, Family, and Kinship: Comparative Studies of Social Organization. Melvin Ember and Carol R. Ember, 1983. New Haven: HRAF Press, pp. 109-124.

Whiting, John W. M. (1954) “The Cross-Cultural Method.” In Handbook of Social Psychology, Vol. 1. Gardner Lindzey, ed. Cambridge, Mass.: Addison-Wesley, pp. 523-31. Reprinted in Readings in Cross-Cultural Methodology. Frank W. Moore, ed. New Haven: HRAF Press, pp. 287-300.

Whyte, Martin K. 1978a. “Cross-Cultural Studies of Women and the Male Bias Problem.” Behavior Science Research 13:65-80.

Whyte, Martin K. 1978b. The Status of Women in Pre-industrial Societies. Princeton: Princeton University Press.

Printable Version

A printable PDF version of A Basic Guide to Cross-Cultural Research can be found here .

Cross-Cultural Psychology

WEIRD, Culture

Reviewed by Psychology Today Staff

Cross-cultural psychology is a branch of psychology that explores the similarities and differences in thinking and behavior between individuals from different cultures.

Scientists using a cross-cultural approach focus on and compare participants from diverse cultural groups to examine ways in which cognitive styles, perception, emotional expression, personality , and other psychological features relate to cultural contexts. They also compare cultural groups on broad dimensions such as individualism and collectivism—roughly, how much a culture emphasizes its members’ individuality versus their roles in a larger group.

Psychologists who are interested in expanding psychology’s focus on diverse cultures have pointed out that the majority of research participants are, to use a popular term, WEIRD: they are from Western, Educated, Industrialized, Rich, and Democratic societies. Cross-cultural research has made it clear that what psychologists conclude about this slice of the world’s population does not always extend to people with other cultural backgrounds.

• What Is Cross-Cultural Psychology?
• Psychological Differences Across Cultures
• The WEIRDness of Psychology

Psychology’s mission to understand how humans think and behave requires studying humanity as broadly as possible—not just the humans to which researchers tend to be nearest. Psychologists who conduct cross-cultural research investigate the richness of human psychological variation across the world, including points of consistency and divergence between populations with distinct cultural backgrounds, such as those in Western and East Asian countries.

Psychological research that incorporates a more global sample of people provides insights into whether findings and models (such as those about the structure of personality or the nature of mental illness) are universal or not, the extent to which psychological phenomena and characteristics vary across cultures, and the potential reasons for these differences. Cross-cultural research demonstrates that experimental effects, correlations, or other results that are observed in one cultural context—for example, the tendency of Western participants to rate their abilities as better-than-average—do not always appear in the same way, or at all, in others.

While various definitions are used, culture can be understood as the collection of ideas and typical ways of doing things that are shared by members of a society and have been passed down through generations. These can include norms, rules, and values as well as physical creations such as tools.

Cross-cultural studies allow psychologists to make comparisons and inferences about people from different countries or from broader geographic regions (such as North America or the Western world). But psychologists also compare groups at smaller scales, such as people from culturally distinct subpopulations or areas of the same country, or immigrants and non-immigrants. 

While there is overlap between these approaches, there are also differences. Cross-cultural psychology analyzes characteristics and behavior across different cultural groups, with an interest in variation as well as human universals. Cultural psychology involves comparison as well, but has been described as more focused on psychological processes within a particular culture. In another approach, indigenous psychology, research methods, concepts, and theories are developed within the context of the culture being studied.

The inhabitants of different regions and countries have a great deal in common : They build close social relationships, follow rules established by their communities, and engage in important rituals. But globally, groups also exhibit somewhat different psychological tendencies in domains ranging from the strictness of local rules to how happiness and other emotions are conceived. Of course, within each region, nation, or community, there is plenty of individual variation; people who share a culture never think and act in exactly the same way. Cross-cultural psychology seeks to uncover how populations with shared cultures differ on average from those with other cultural backgrounds—and how those differences tie back to cultural influence.

While there are shared aspects of emotional experience across cultural groups, culture seems to influence how people describe, evaluate, and act on emotions. For example, while the experience of shame follows perceived wrongdoing across cultures, having shame may be evaluated more positively in some cultures than in others and may be more likely to prompt behavioral responses such as reaching out to others rather than withdrawing. Different emotional concepts (such as “ anxiety ,” “ fear ,” and “ grief ”) may also be thought of as more or less closely related to each other in different cultures. And cultural differences have been observed with regard to how emotions are interpreted and the “display rules” that individuals learn about appropriate emotional expression.

While happiness seems to be one of the most cross-culturally recognizable emotions in terms of individual expression, culture can influence how one thinks about happiness. Research indicates that people in different cultures vary in how much value they place on happiness and how much they focus on their own well-being. Culture may also affect how people believe happiness should be defined and achieved —whether a good life is to be found more in individual self-enhancement or through one’s role as part of a collective, for example. 

Some mental health conditions, in addition to being reported at markedly different rates in different countries, can also be defined and even experienced in different ways. The appearance of depression may depend partly on culture —with mood-related symptoms emphasized in how Americans think of depression , for example, and bodily symptoms potentially more prominent in China. Features of certain cultural groups, such as highly stable social networks, may also serve as protective factors against the risk of mental illness. Increased understanding of cultural idiosyncrasies could lead to gains in mental health treatment.

Individualism and collectivism are two of the contrasting cultural patterns described in cross-cultural psychology. People in relatively collectivist cultures are described as tending to define themselves as parts of a group and to heed the norms and goals of the group. Those in relatively individualistic cultures are thought to emphasize independence and to favor personal attitudes and preferences to a greater degree. Cross-cultural psychologists have pointed to East Asia, Latin America, and Africa as regions where collectivism is relatively prominent and much of Europe, the U.S., and Canada as among those where individualism is more pronounced. But individualism-collectivism is thought of as a continuum, with particular countries, and cultures within those countries, showing a balance of each.

Tightness and looseness are contrasting cultural patterns related to how closely people adhere to social rules. Each culture has its own rules and norms about everything from acceptable public behavior to what kinds of intimate relationships are allowed. But in some cultures, or even in particular domains within cultures (such as the workplace), the importance placed on rules and norms and the pressure on people to follow them are greater than in other cultural contexts. Relatively rule-bound cultures have been described as “tight” cultures, while more permissive cultures are called “loose.” As with individualism and collectivism, tightness and looseness are thought of as opposite ends of the same dimension.

It seems to be. In the West, the Big Five model of personality traits and related models were developed to broadly map out personality differences, and they have been tested successfully in multiple countries. But research suggests that in some cultures, the Big Five traits are not necessarily the best way to describe how people perceive individual differences. Scientists have also found that associations between personality traits and outcomes that appear in some cultures may not be universal. For example, while more extraverted North Americans appear to be happier, on average, extraverts may not have the same advantage elsewhere.

While some mating preferences, such as a desire for kindness and physical attractiveness in a partner, appear in many if not all cultures, preferences also differ in some ways across cultures—like the importance placed on humor or other traits. In the realm of physical attraction , both men’s preferences and women’s preferences seem to depend partly on the cultural context, with research suggesting that men in wealthier societies more strongly favor women of average-to-slender weight, for instance.

In cross-cultural psychology, an analytic cognitive style roughly describes a tendency to focus on a salient object, person, or piece of information (as in an image or a story) independently from the context in which it appears. A holistic cognitive style, in contrast, involves a tendency to focus more on the broader context and relationship between objects. Using a variety of tasks—such as one in which a scene with both focal objects and background elements is freely described—psychologists have reported evidence that participants from Western cultures (like the U.S.) tend to show a more analytic cognitive style, while those from East Asian cultures (like Japan) show a more holistic cognitive style. Analytic thinking and holistic thinking have been theorized to stem, respectively, from independent and interdependent cultural tendencies.

The psychological findings that get the most attention are disproportionately derived from a fraction of the world’s population. Some scientists call this relatively well-examined subgroup of human societies WEIRD: that is, Western, Educated, Industrialized, Rich, and Democratic. As long as people who live in countries that meet these descriptors are the primary subjects of psychological research—and that has long been the case—it will often be difficult for psychologists to determine whether an observation applies to people in general or only to those in certain cultural contexts. Increasing the representation of people from diverse cultures in research is therefore a goal of many psychologists.

WEIRD populations are those who are broadly part of the Western world and who live in democratic societies that feature high levels of education , wealth, and industrialization. While there is not a single agreed-upon list of WEIRD cultures—and populations within particular countries can show different levels of these characteristics—commonly cited examples of WEIRD countries include the U.S., Canada, the U.K. and other parts of Western Europe, and Australia. 

Sampling top psychology journals in the mid-2000s, psychologist Jeffrey Arnett observed that 96 percent of research subjects came from Western, industrialized countries that represented just 12 percent of the world’s population, and that about two-thirds were from the U.S. Subjects seemed to largely be sourced from the countries in which the researchers lived. In 2010, citing this finding and others, Joseph Henrich, Steven Heine, and Ara Norenzayan introduced the term WEIRD to describe this subpopulation. They expanded on the problems with focusing so exclusively on such participants and with assuming that findings from a relatively unrepresentative group generalized to the rest of the world.

In numerical terms, it seems, not much. While the problems with global underrepresentation in psychology research have gained more attention in recent years, an updated analysis of top journals in the mid-2010s found that the vast majority of samples were still from Western, industrialized countries, with about 60 percent from the U.S.

In many ways, WEIRD populations seem to be less representative of humans in general than non-WEIRD populations are. Overlapping with other findings from cross-cultural psychology, psychological differences between people from relatively WEIRD countries and those from elsewhere have been noted: WEIRD samples show higher levels of individualism and lower levels of conformity , on average, among other characteristics. One scientist behind the WEIRD concept theorizes that societal changes in the West caused by the Catholic Church, and their subsequent cultural impact, help explain these signature differences.

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Cross-Cultural Research Methodology In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Cross-cultural research allows you to identify important similarities and differences across cultures. This research approach involves comparing two or more cultural groups on psychological variables of interest to understand the links between culture and psychology better.

As Matsumoto and van de Vijver (2021) explain, cross-cultural comparisons test the boundaries of knowledge in psychology. Findings from these studies promote international cooperation and contribute to theories accommodating both cultural and individual variation.

However, there are also risks involved. Flawed methodology can produce incorrect cultural knowledge. Thus, cross-cultural scientists must address methodological issues beyond those faced in single-culture studies.

Methodology

Cross-cultural comparative research utilizes quasi-experimental designs comparing groups on target variables.

Cross-cultural research takes an etic outsider view, testing theories and standardized measurements often derived elsewhere. 

• Studies can be exploratory , aimed at increasing understanding of cultural similarities and differences by staying close to the data.
• In contrast, hypothesis-testing studies derive from pre-established frameworks predicting specific cultural differences. They substantially inform theory but may overlook unexpected findings outside researcher expectations (Matsumoto & van de Vijver, 2021).

Each approach has tradeoffs. Exploratory studies broadly uncover differences but have limited explanatory power. While good for revealing novel patterns, exploratory studies cannot address the reasons behind cross-cultural variations.

Hypothesis testing studies substantially inform theory but may overlook unexpected findings. Optimally, cross-cultural research should combine elements of both approaches.

Ideal cross-cultural research combines elements of exploratory work to uncover new phenomena and targeted hypothesis testing to isolate cultural drivers of observed differences (Matsumoto & van de Vijver, 2021).

Cross-cultural scientists should strategically intersect exploratory and theory-driven analysis while considering issues of equivalence and ecological validity.

Other distinctions include: comparing psychological structures versus absolute score levels; analysis at the individual versus cultural levels; and combining individual-level data with country indicators in multilevel modeling (Lun & Bond, 2016; Santos et al., 2017)

Methodological Considerations

Cross-cultural research brings unique methodological considerations beyond single-culture studies. Matsumoto and van de Vijver (2021) explain two key interconnected concepts – bias and equivalence.

Bias refers to systematic differences in meaning or methodology across cultures that threaten the validity of cross-cultural comparisons.

Bias signals a lack of equivalence, meaning score differences do not accurately reflect true psychological construct differences across groups.

There are three main types of bias:

• Construct bias stems from differences in the conceptual meaning of psychological concepts across cultures. This can occur due to incomplete overlap in behaviors related to the construct or differential appropriateness of certain behaviors in different cultures.
• Method bias arises from cross-cultural differences in data collection methods. This encompasses sample bias (differences in sample characteristics), administration bias (differences in procedures), and instrument bias (differences in meaning of specific test items across cultures).
• Item bias refers to specific test items functioning differently across cultural groups, even for people with the same standing on the underlying construct. This can result from issues like poor translation, item ambiguity, or differential familiarity or relevance of content.

Techniques to identify and minimize bias focus on achieving equivalence across cultures. This involves similar conceptualization, data collection methods, measurement properties, scale units and origins, and more.

Careful study design, measurement validation, data analysis, and interpretation help strengthen equivalence and reduce bias.

Equivalence

Equivalence refers to cross-cultural similarity that enables valid comparisons. There are multiple interrelated types of equivalence that researchers aim to establish:

• Conceptual/Construct Equivalence : Researchers evaluate whether the same theoretical construct is being measured across all cultural groups. This can involve literature reviews, focus groups, and pilot studies to assess construct relevance in each culture. Claims of inequivalence argue concepts can’t exist or be understood outside cultural contexts, precluding comparison.
• Functional Equivalence : Researchers test for identical patterns of correlations between the target instrument and other conceptually related and unrelated constructs across cultures. This helps evaluate whether the measure relates to other variables similarly in all groups.
• Structural Equivalence : Statistical techniques like exploratory and confirmatory factor analysis are used to check that underlying dimensions of multi-item instruments have the same structure across cultures.
• Measurement Unit Equivalence : Researchers determine if instruments have identical scale properties and meaning of quantitative score differences within and across cultural groups. This can be checked via methods like differential item functioning analysis.

Multifaceted assessment of equivalence is key for valid interpretation of score differences reflecting actual psychological variability across cultures.

Establishing equivalence requires careful translation and measurement validation using techniques like differential item functioning analysis, assessing response biases, and examining practical significance. Adaptation of instruments or procedures may be warranted to improve relevance for certain groups.

Building equivalence into the research process reduces non-equivalence biases. This avoids incorrect attribution of score differences to cultural divergence, when differences may alternatively reflect methodological inconsistencies.

Procedures to Deal With Bias

Researchers can take steps before data collection (a priori procedures) and after (a posteriori procedures) to deal with bias and equivalence threats. Using both types of procedures is optimal (Matsumoto & van de Vijver, 2021).

Designing cross-cultural studies (a priori procedure)

Simply documenting cultural differences has limited scientific value today, as differences are relatively easy to obtain between distant groups. The critical challenge facing contemporary cross-cultural researchers is isolating the cultural sources of observed differences (Matsumoto & Yoo, 2006).

This involves first defining what constitutes a cultural (vs. noncultural) explanatory variable. Studies should incorporate empirical measures of hypothesized cultural drivers of differences, not just vaguely attribute variations to overall “culture.”

Both top-down and bottom-up models of mutual influence between culture and psychology are plausible. Research designs should align with the theorized causal directionality.

Individual-level cultural factors must also be distinguished conceptually and statistically from noncultural individual differences like personality traits. Not all self-report measures automatically concern “culture.” Extensive cultural rationale is required.

Multi-level modeling can integrate data across individual, cultural, and ecological levels. However, no single study can examine all facets of culture and psychology simultaneously.

Pursuing a narrow, clearly conceptualized scope often yields greater returns than superficial breadth (Matsumoto & van de Vijver, 2021). By tackling small pieces thoroughly, researchers collectively construct an interlocking picture of how culture shapes human psychology.

Sampling (a priori procedure)

Unlike typical American psychology research drawing from student participant pools, cross-cultural work often cannot access similar convenience samples .

Groups compared across cultures frequently diverge substantially in background characteristics beyond the cultural differences of research interest (Matsumoto & van de Vijver, 2021).

Demographic variables like educational level easily become confounds making it difficult to interpret whether cultural or sampling factors drive observed differences in psychological outcomes. Boehnke et al. (2011) note samples of greater cultural distance often have more confounding influences .

Guidelines exist to promote adequate within-culture representativeness and cross-cultural matching on key demographics that cannot be dismissed as irrelevant to the research hypotheses. This allows empirically isolating effects of cultural variables over and above sample characteristics threatening equivalence.

Where perfect demographic matching is impossible across widely disparate groups, analysts should still measure and statistically control salient sample variables that may form rival explanations for group outcome differences. This unpacks whether valid cultural distinctions still exist after addressing sampling confounds.

In summary, sampling rigor in subject selection and representativeness support isolating genuine cultural differences apart from method factors, jeopardizing equivalence in cross-cultural research.

Designing questions and scales (a priori procedure)

Cross-cultural differences in response styles when using rating scales have posed persistent challenges. Once viewed as merely nuisance variables requiring statistical control, theory now conceptualizes styles like social desirability, acquiescence, and extremity as a meaningful individual and cultural variation in their own right (Smith, 2004).

For example, an agreeableness acquiescence tendency may be tracked with harmony values in East Asia. Efforts to simply “correct for” response style biases can thus discount substantive culture-linked variation in scale scores (Matsumoto & van de Vijver, 2021).

Guidelines help adapt item design, instructions, response options, scale polarity, and survey properties to mitigate certain biases and equivocal interpretations when comparing scores across groups.

It remains important to assess response biases empirically through statistical controls or secondary measures. This evaluates whether cultural score differences reflect intended psychological constructs above and beyond style artifacts.

Appropriately contextualizing different response tendencies allows judiciously retaining stylistic variation attributable to cultural factors while isolating bias-threatening equivalence. Interpreting response biases as culturally informative rather than merely as problematic noise affords richer analysis.

In summary, response styles exhibit differential prevalence across cultures and should be analyzed contextually through both control and embrace rather than simplistically dismissed as invalid nuisance factors.

A Posteriori Procedures to Deal With Bias

After data collection, analysts can evaluate measurement equivalence and probe biases threatening the validity of cross-cultural score comparisons (Matsumoto & van de Vijver, 2021).

For structure-oriented studies examining relationships among variables, techniques like exploratory factor analysis, confirmatory factor analysis, and multidimensional scaling assess similarities in conceptual dimensions across groups. This establishes structural equivalence.

For comparing group mean scores, methods like differential item functioning, logistic regression, and standardization identify biases causing specific items or scales to function differently across cultures. Addressing biases promotes equivalence (Fischer & Fontaine, 2011; Sireci, 2011).

Multilevel modeling clarifies connections between culture-level ecological factors, individual psychological outcomes, and variables at other levels simultaneously. This leverages the nested nature of cross-cultural data (Matsumoto et al., 2007).

Supplementing statistical significance with effect sizes evaluates the real-world importance of score differences. Metrics like standardized mean differences and probability of superiority prevent overinterpreting minor absolute variations between groups (Matsumoto et al., 2001).

In summary, a posteriori analytic approach evaluates equivalence at structural and measurement levels and isolates biases interfering with valid score comparisons across cultures. Quantifying practical effects also aids replication and application.

Ethical Issues

Several ethical considerations span the research process when working across cultures. In design, conscious efforts must counteract subtle perpetuation of stereotypes through poorly constructed studies or ignorance of biases.

Extensive collaboration with cultural informants and members can alert researchers to pitfalls (Matsumoto & van de Vijver, 2021).

Recruiting participants ethically becomes more complex globally, as coercion risks increase without shared assumptions about voluntary participation rights.

Securing comprehensible, properly translated informed consent also grows more demanding, though remains an ethical priority even when local guidelines seem more lax. Confidentiality protections likewise prove more intricate across legal systems, requiring extra researcher care.

Studying sensitive topics like gender, sexuality, and human rights brings additional concerns in varying cultural contexts, necessitating localized ethical insight.

Analyzing and reporting data in a culturally conscious manner provides its own challenges, as both subtle biases and consciously overgeneralizing findings can spur harm.

Above all, ethical cross-cultural research requires recognizing communities as equal partners, not mere data sources. From first consultations to disseminating final analyses, maintaining indigenous rights and perspectives proves paramount to ethical engagement.

Berry, J. W., Poortinga, Y. H., Segall, M. H., & Dasen, P. R. (2002). Cross-cultural psychology: Research and applications (2nd ed.). Cambridge University Press.

Bond, M. H., & van de Vijver, F. J. R. (2011). Making scientific sense of cultural differences in psychological outcomes: Unpackaging the magnum mysteriosum. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 75–100). Cambridge University Press.

Fischer, R., & Fontaine, J. R. J. (2011). Methods for investigating structural equivalence. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 179–215). Cambridge University Press.

Hambleton, R. K., & Zenisky, A. L. (2011). Translating and adapting tests for cross-cultural assessments. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 46–74). Cambridge University Press.

Johnson, T., Shavitt, S., & Holbrook, A. (2011). Survey response styles across cultures. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 130–176). Cambridge University Press.

Matsumoto, D., Grissom, R., & Dinnel, D. (2001). Do between-culture differences really mean that people are different? A look at some measures of cultural effect size. Journal of Cross-Cultural Psychology, 32 (4), 478–490. https://doi.org/10.1177/0022022101032004007

Matsumoto, D., & Juang, L. P. (2023). Culture and psychology (7th ed.). Cengage Learning.

Matsumoto, D., & van de Vijver, F.J.R. (2021). Cross-cultural research methods in psychology. In H. Cooper (Ed.), APA handbook of research methods in psychology (Vol. 1, pp. 97-113). American Psychological Association. https://doi.org/10.1037/0000318-005

Matsumoto, D., & Yoo, S. H. (2006). Toward a new generation of cross cultural research. Perspectives on Psychological Science, 1 (3), 234-250. https://doi.org/10.1111/j.1745-6916.2006.00014.x

Nezlek, J. (2011). Multilevel modeling. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 299–347). Cambridge University Press.

Shweder, R. A. (1999). Why cultural psychology? Ethos, 27 (1), 62–73.

Sireci, S. G. (2011). Evaluating test and survey items for bias across languages and cultures. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 216–243). Cambridge University Press.

Smith, P. B. (2004). Acquiescent response bias as an aspect of cultural communication style. Journal of Cross-Cultural Psychology, 35 (1), 50–61. https://doi.org/10.1177/0022022103260380

van de Vijver, F. J. R. (2009). Types of cross-cultural studies in cross-cultural psychology. Online Readings in Psychology and Culture, 2 (2). https://doi.org/10.9707/2307-0919.1017

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• > Performing Qualitative Cross-Cultural Research
• > Performing qualitative cross-cultural research: an introduction

Book contents

• Frontmatter
• About the author
• About the book
• 1 Performing qualitative cross-cultural research: an introduction
• 2 Moral and ethical perspectives
• 3 The research participants: accessing and reciprocity
• 4 Cultural sensitivity: a responsible researcher
• 5 Insider/Outsider perspectives and placing issues
• 6 Cross-cultural communication and language issues
• 7 Personal and collective testimony
• 8 Local knowledge, local power and collective action
• 9 Writing and disseminating in cross-cultural research
• In closing …

1 - Performing qualitative cross-cultural research: an introduction

Published online by Cambridge University Press:  05 June 2012

From the vantage point of the colonized, a position from which I write … the term ‘research’ is inextricably linked to European imperialism and colonialism. The word itself, ‘research’, is probably one of the dirtiest words in the indigenous world's vocabulary … It stirs up silence, it conjures up bad memories, it raises a smile that is knowing and distrustful.

Research by its very nature is inherently political; it is about the nature of power as well as access to power … The academy has been dominated by White middle-class and/or male researchers, whose political values and commitments have influenced social research, leading it to be predominantly Eurocentric, bourgeois and patriarchal in its agenda … This agenda has been informed primarily by the dominant groups, such that the ‘marginal’, the ‘powerless’ and the ‘oppressed’ have been the excessive object of study.

Introduction

Historically, cross-cultural research has been an important part of the anthropological discipline. Researchers within this discipline have worked with people in different social, cultural and geographical settings, using mainly ethnography as their method of data collection. They are known as ethnographers. They have tried to conduct their research with the hope that they can ‘interpret what is on the “inside”, through the voices of informants’ (Adler 2004: 107). This tradition continues. Although the ethnographers are performing cross-cultural research, in the past they have also been seen as the ‘takers and users’ who ‘exploit the hospitality and generosity of native people’ (Trask 1993: 7; see also Minh-Ha 1989, 2006).

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• Performing qualitative cross-cultural research: an introduction
• Pranee Liamputtong , La Trobe University, Victoria
• Book: Performing Qualitative Cross-Cultural Research
• Online publication: 05 June 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511812705.003

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Cross-Cultural Research Paper

New Research Shows Audiences Seek Out Authentic Cross-Cultural Content

Discerning viewers and shoppers want brands to help expand their global horizons and foster a sense of community, according to a wide-reaching Amazon Ads report

By Lesley O'Toole

Lesley O'Toole

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International audiences want brands to represent global culture by embracing diversity and depicting people in nuanced and respectful ways. As detailed in a new Amazon Ads report, “ From Ads to Zeitgeist ,” research shows that dynamic creativity in linking brands with global artists and entertainment titles, plus the involvement of fandoms to an unprecedented degree, is the way of the future.  

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Consumers enjoy their own, unique tastes, but they also relish being part of a community. Interestingly, brands themselves play a more significant role than previously realized in shaping collective experiences, with 63% of those polled agreeing that brands create communities and bring people together. 

“Everything can feel very detached today,” said one respondent from Germany. “More than ever, it’s important to show you belong to a group. Brands can really help with this: to create something for people to belong to.” 

Indeed, viewers want to be directed toward content that helps them feel part of a movement. Seventy percent of adult Gen Zers and millennials emphasized their need and desire to feel a greater sense of belonging and community, while two-thirds of all those polled agreed.  

Seeking a More Globally Integrated Culture  

Seventy percent of those surveyed said they are interested in genuine, accurate stories about nationalities different from their own. “Inclusivity is a requirement now to be able to match consumers around the world,” says Diana Pérez Ballantyne, general brand manager of La Roche-Posay and CeraVe Mexico. “Global icons, endorsements and celebrities from different nationalities have greater representation globally than 10 or 20 years ago.” 

Advertising also has an educational component when it comes to different cultures. Fifty-six percent of the respondents, and about 60% of those between ages 18 and 42, agree that brands help people learn about other cultures. “In our Brazilian market, shared culture allows us to launch products not only with technology and added value in mind, but also with authenticity and lifestyle use for the consumer,” explains Ricardo Filó, marketing and sales operations director at Logitech Brazil. “People don’t just want to consume technology. They want to be seen.” 

More than half of those surveyed confirm that they learned about other cultures via advertising, while nearly 60% of those surveyed noted representation of “mixed” and second-generation culture is important. “In Japan, a culture that values cooperation, bonds and traditions, I believe that people want to find a ‘common framework’ to confirm their relationship with other people and society — to feel like ‘I exist here,’” says Yukie Takamura, head of Amazon’s product specialists, who notes the shared excitement of key events like cherry blossom season and the 2020 Tokyo Olympics. 

The worldwide reach of cross-cultural celebrities, as well as their films, TV series and music, has created a seismic shift in how we consume media, offering more opportunities to learn about the world than ever. Younger adult Gen Z (ages 18-26) and millennial (ages 27-42) demographics most regularly engage online, and roughly 84% of the report’s participants watch streaming service content, including Prime Video, every month.  

Amid a crowded streaming media market, Prime Video has established itself by offering premium content at scale, with an average monthly ad-supported reach of 200 million global customers, 115 million of whom are in the U.S. This year, Amazon made its upfronts debut and generated buzz by showcasing its entertainment portfolio, which expands to music, gaming, devices and podcasts. 

With the increasing global appetite for streaming content, it is more important than ever to support diverse creatives. The “Progress on Inclusion” report — a three-year review of Amazon MGM Studios productions launched between July 2020 and June 2023 — reinforced understanding that diversity behind the camera strongly influences who we see on-screen. For example, across Amazon MGM Studios Original series and films in the window the report covered, when there is at least one creator who is Latino, Latino representation in main cast roles is 20% across titles, compared to 5% when no creators are Latino. 

Understanding the Customer  

Consumers also love to be surprised with unexpected collaborations and clever, lighthearted and funny material. Seventy-three percent note that they appreciate advertising that is entertaining and distinctive.  

While brands cannot control consumer interaction, authenticity and the right approach can yield dividends that extend longevity and global reach well beyond the initial outlay. As Kate McCagg, head of Brand Innovation Lab, Amazon Ads, emphasized in the report: “The brands that succeed are those that know who they are. They’re not trying to fit in with different crowds.” 

View the full “ From Ads to Zeitgeist ” report to see more insights from the research and learn how brands can join the conversation.  

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Research on Interactive Design of Guangxi Bronze Drum Tourism and Cultural Creative Products Based on AR Technology

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Bibliometrics & citations, view options, recommendations, research on the design of red cultural and creative products based on digitalization.

Vigorously promoting red culture and inheriting red spirit should focus on integrating with science and technology, and continuously promoting the forward development of digital culture industry. Based on the combination of red culture and ...

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Research on Consumers’ Decision-Making Factors of Cultural and Creative Products of the Palace Museum Under the Background of New Media

With the development of social economy, new media platform has gradually become an important channel for people to communicate. The cultural and creative industry of the Palace Museum has attracted more and more people’s attention through the ...

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https://ror.org/03cve4549Tsinghua University, Beijing, China

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Berlin, Heidelberg

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• Augmented Reality
• interaction design
• Guangxi Bronze Drum
• cultural and creative products

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