Discrete mathematics

Sociology

Inference

Equivalence (formal languages)

Mathematics

Epistemology

Machine learning

Developmental psychology

Philosophy

Econometrics

Cognitive psychology

Artificial intelligence

Psychology

Causal inference

Economics

Cross-cultural

Generalization

Generalizability theory

Anthropology

Data science

Computer science

Management science

Generative grammar

Advances in Methods and Practices in Psychological Science

Behavioral researchers increasingly recognize the need for more diverse samples that capture the breadth of human experience. Current attempts to establish generalizability across populations focus on threats to validity, constraints on generalization, and the accumulation of large, cross-cultural data sets. But for continued progress, we also require a framework that lets us determine which inferences can be drawn and how to make informative cross-cultural comparisons. We describe a generative causal-modeling framework and outline simple graphical criteria to derive analytic strategies and implied generalizations. Using both simulated and real data, we demonstrate how to project and compare estimates across populations and further show how to formally represent measurement equivalence or inequivalence across societies. We conclude with a discussion of how a formal framework for generalizability can assist researchers in designing more informative cross-cultural studies and thus provides a more solid foundation for cumulative and generalizable behavioral research.

A Causal Framework for Cross-Cultural Generalizability