At the heart of this question is avoiding ecological fallacy—drawing conclusions about individuals from group-level data. In cross-cultural research, relying only on aggregate data (like country averages) can create misleading inferences about how individuals think or behave within those cultures. Within-group analyses look at relationships inside each culture, using individual data from that culture. This keeps the focus on how variables relate for people, not for cultures as whole units, so you see the genuine pattern that exists among individuals rather than an artifact of grouping. Multilevel analyses take this a step further by modeling data that are nested—people within cultures. This approach separates variation that happens within cultures from variation between cultures, allowing you to test whether the same relationships hold at the individual level and whether they differ across cultures. It also lets you include both individual-level and culture-level factors to see how they interact, all while reducing bias that comes from relying on aggregated data alone. Other approaches—like using only aggregate cross-cultural comparisons, increasing sample sizes without considering grouping, or ignoring individual differences—still rely on group-level conclusions or overlook how people within cultures may diverge, so they don’t adequately guard against ecological fallacy.