Cross-Cultural Research

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. This matters because psychology has historically drawn conclusions from Western samples and treated them as universal, a bias known as ethnocentrism.

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. 

1. Studies can be exploratory, aimed at increasing understanding of cultural similarities and differences by staying close to the data.
2. 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 uncover novel patterns but cannot explain the reasons behind them. Hypothesis-testing studies inform theory but may miss findings outside the researcher’s expectations.

Ideal research combines both: exploratory work to uncover new phenomena, and hypothesis testing to isolate which cultural driver explains the difference (Matsumoto & van de Vijver, 2021). Level of analysis matters too.

Cross-cultural scientists also weigh other methodological distinctions, such as comparing psychological structures rather than absolute score levels. These include the individual versus cultural level of analysis, and multilevel modeling that combines individual-level data with country indicators (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. Equivalence means a measure works the same way and means the same thing across the cultures being compared.

Bias

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:

1. 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.
2. 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).
3. 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:

1. 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.
2. 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.
3. 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.
4. 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. Description alone will not do. The critical challenge facing contemporary cross-cultural researchers is isolating the cultural sources of observed differences (Matsumoto & van de Vijver, 2021).

This involves first defining what constitutes a cultural (vs. noncultural) explanatory variable. Vague labels will not do. 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. Scope still matters. 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). This threatens equivalence.

Demographic variables like educational level easily become confounds, making it difficult to interpret whether cultural or sampling factors drive observed differences in psychological outcomes.

Cultural distance itself predicts more confounds (Boehnke et al., 2011).

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 isolates genuine cultural differences.

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. These are not just statistical noise.

Theory once treated styles like social desirability, acquiescence, and extremity as nuisance variables requiring statistical control. It now conceptualizes them as 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. The link runs both ways. 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. Numbers need context. 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.

Context always matters.

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 (Nezlek, 2011).

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, beyond those faced in single-culture studies.

Study Design and Consent

Ethical study design starts before data collection. Researchers should work with cultural informants from the community, since an outsider’s framing can embed stereotypes into the research question without anyone noticing (Matsumoto & van de Vijver, 2021).

Recruitment and consent then carry their own complexity. Consent must be genuinely voluntary and understood in the local language and cultural frame of reference, which is harder than it sounds.

A translated consent form is not automatically an equivalent one, and assumptions about who must consent (an individual, a family, or a whole community) also vary by culture.

Confidentiality, Sensitive Topics, and Reporting

Confidentiality is not guaranteed just because a protocol works at home. Protections that are legally robust in one jurisdiction may be unenforceable, or simply meaningless, in another.

Sensitive topics carry their own risks. Gender, sexuality, and political or human-rights-adjacent questions carry different risks to participants depending on the legal and social context. Ethical judgement has to be localized, not imported wholesale from the researcher’s home institution.

Reporting is the final ethical test. A finding can cause harm two opposite ways. Understating genuine, replicated variation erases differences that matter to the groups studied.

Overgeneralizing a small, poorly controlled difference turns it into a sweeping claim about how a whole group of people supposedly behaves.

Cross-cultural research is on strongest ethical ground when it treats the communities studied as partners, not merely as sources of data (Matsumoto & van de Vijver, 2021).

Critical Evaluation

Cross-cultural research delivers real scientific value, but it also carries real methodological risk. Both deserve equal weight.

Strengths: Reducing Attribution Bias

Separating behaviour from its home cultural context gives researchers real protection against two common errors: the fundamental attribution error, and the “deficit model” of minority-group performance (Miller, 1984).

This is not just theory.

Aim: Bond and Smith (1996) tested whether the individualism–collectivism dimension could explain cross-national differences in conformity itself.

Method: They pooled 133 studies using the Asch line-judgment task from 17 countries, coding each country’s individualism score against its measured conformity rate.

That is a genuinely large sample.

Results: National individualism score significantly predicted conformity: participants from more collectivist countries conformed to an incorrect majority more than participants from more individualist countries.

Conclusion: This gives the field’s core “variability” claim strong meta-analytic support, not just a single study’s word for it.

The finding is not new, though.

Berry (1967) had already found the same pattern in a single classic comparison. The Temne of Sierra Leone conformed to a false group norm far more than the Inuit of Baffin Island. The Inuit’s hunting-and-fishing economy rewards individual judgement; the Temne’s cooperative rice farming rewards group harmony instead.

Limitations: Imposed Etic and the Nation-As-Culture Problem

Cross-cultural psychology can still smuggle in Western bias, and the risk is internal, not external. As an outgrowth of mainstream psychology rather than a rejection of it, the field has largely tested Western-built instruments against norms it treats as a neutral baseline (Berry, 1969).

Berry’s own Temne–Inuit study is the textbook case.

The Asch task, the instructions, even the idea of being tested by a stranger were all artefacts of Western experimental psychology. They were adapted for use elsewhere, not developed from within Temne or Inuit life.

A second weakness runs deeper.

Much cross-cultural research treats an entire nation as one cultural unit. Yet most nations contain religious, linguistic, or ethnic subcultures that can differ from each other as much as two separate countries do (Smith & Bond, 1998).

One study puts numbers on this.

Van IJzendoorn and Kroonenberg’s (1988) meta-analysis of 32 Strange Situation studies across 8 countries makes this concrete. Attachment-type distributions did vary by country, but variation within a single country exceeded variation between countries.

National culture, in other words, is a poor stand-in for the child-rearing practices that actually drive the difference.

Contemporary Research

The past decade has tested cross-cultural psychology’s own evidence base in two ways: how representative it actually is, and whether its classic findings hold up at scale.

How Representative Is Psychology’s Evidence Base?

Henrich, Heine, and Norenzayan’s (2010) influential critique found that psychology’s participant pool is overwhelmingly Western, Educated, Industrialized, Rich, and Democratic (WEIRD). These groups are themselves statistical outliers on many measured psychological dimensions.

The gap has not closed.

Aim: Rad, Martingano, and Ginges (2018) set out to measure just how representative psychological science’s published evidence base really is.

Method: They reviewed sampling practices across leading psychology journals and checked published claims of “universal” human behaviour against the diversity of the samples behind them.

The pattern was stark.

Results: Most participants in top-tier journals still came from a narrow WEIRD band, and papers routinely generalised findings to “humans” from that narrow slice alone.

Conclusion: The authors called for explicit sample-description requirements and active recruitment of diverse, cross-cultural samples as a default, not a specialist concern. The paper has since been cited over 500 times.

Quantitative tools have matured alongside this critique. Muthukrishna et al. (2020) built a cultural-distance metric confirming American samples sit at an extreme end of the global distribution of cultural values.

Santos, Varnum, and Grossmann (2017) found individualist values rising in most of 78 countries studied over 51 years.

Do Classic Findings Replicate Across Cultures?

Representativeness is only half the question.

Aim: Klein and colleagues’ (2018) “Many Labs 2” project tested whether a broad set of classic and contemporary psychological effects would replicate consistently across cultural settings.

Method: Twenty-eight previously published effects were tested in 125 samples from 36 countries and territories, spanning six continents, using one pre-registered protocol per effect.

The protocol was identical everywhere.

Results: Most effects that did replicate varied little across samples, but a minority failed to replicate at all, and effect sizes were consistently smaller than the original published estimates.

Conclusion: Sample and setting explained comparatively little of the variation the project found, suggesting replicability itself, not cultural difference, is the bigger threat to many classic findings.

That is a useful corrective.

Read together, the two studies point the same way. Representativeness in psychology’s evidence base is a real, only slowly improving problem. But not every failure to replicate abroad reflects a genuine cultural difference rather than an ordinary replication problem.

References

Berry, J. W. (1967). Independence and conformity in subsistence-level societies. Journal of Personality and Social Psychology, 7(4), 415–418. https://doi.org/10.1037/h0025231

Berry, J. W. (1969). On cross-cultural comparability. International Journal of Psychology, 4(2), 119–128. https://doi.org/10.1080/00207596908247261

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.

Bond, R., & Smith, P. B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1), 111–137. https://doi.org/10.1037/0033-2909.119.1.111

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.

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. https://doi.org/10.1017/S0140525X0999152X

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.

Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Jr., Alper, S., et al. (2018). Many Labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490. https://doi.org/10.1177/2515245918810225

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

Miller, J. G. (1984). Culture and the development of everyday social explanation. Journal of Personality and Social Psychology, 46(5), 961–978. https://doi.org/10.1037/0022-3514.46.5.961

Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C. M., Gedranovich, A., McInerney, J., & Thue, B. (2020). Beyond Western, educated, industrial, rich, and democratic (WEIRD) psychology: Measuring and mapping scales of cultural and psychological distance. Psychological Science, 31(6), 678–701. https://doi.org/10.1177/0956797620916782

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.

Rad, M. S., Martingano, A. J., & Ginges, J. (2018). Toward a psychology of Homo sapiens: Making psychological science more representative of the human population. Proceedings of the National Academy of Sciences, 115(45), 11401–11405. https://doi.org/10.1073/pnas.1721165115

Santos, H. C., Varnum, M. E. W., & Grossmann, I. (2017). Global increases in individualism. Psychological Science, 28(9), 1228–1239. https://doi.org/10.1177/0956797617700622

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

Smith, P. B., & Bond, M. H. (1998). Social psychology across cultures (2nd ed.). Prentice Hall.

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

Van IJzendoorn, M. H., & Kroonenberg, P. M. (1988). Cross-cultural patterns of attachment: A meta-analysis of the strange situation. Child Development, 59(1), 147–156. https://doi.org/10.2307/1130396