A Causal Framework for Cross-Cultural Generalizability

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