Relatively little is known about the extent to which culture moderates findings in applied psychology research. To address this gap, we leverage the metaBUS database of over 1,000,000 published findings to examine the extent to which six popular cross-cultural models explain variance in findings across 136 bivariate relationships and 56 individual cultural dimensions. We compare moderating effects attributable to Hofstede’s dimensions, GLOBE’s practices, GLOBE’s values, Schwartz’s Value Survey, Ronen and Shenkar’s cultural clusters, and the United Nations’ M49 standard. Results from 25,296 multilevel meta-analyses indicate that, after accounting for statistical artifacts, cross-cultural models explain approximately 5–7% of the variance in findings. The variance explained did not vary substantially across models. A similar set of analyses on observed effect sizes reveal differences of |r| = .05–.07 attributable to culture. Variance among the 136 bivariate relationships was explained primarily by sampling error, indicating that cross-cultural moderation assessments require atypically large sample sizes. Our results provide important information for understanding the overall level of explanatory power attributable to cross-cultural models, their relative performance, and their sensitivity to variance in the topic of study. In addition, our findings may be used to inform power analyses for future research. We discuss implications for research and practice.

关于文化对应用心理学研究结果的影响程度知之甚少。为了解决这一差距，我们利用metaBUS数据库中超过1,000,000篇已发表的发现，研究了六种流行的跨文化模型在136个双变量关系和56个个体文化维度上解释发现差异的程度。我们比较了霍夫斯泰德的规模，格劳伯的做法，格劳伯的价值观，施瓦茨的价值观调查，罗南和申卡尔的文化群以及联合国的M49标准所产生的调节作用。25,296次多级荟萃分析的结果表明，在考虑了统计假象后，跨文化模型解释了结果差异的约5–7％。解释的差异在各个模型中没有实质性变化。r | = .05–.07归因于文化。136个二元关系之间的差异主要是由抽样误差解释的，这表明跨文化适度评估需要非典型的大样本量。我们的结果为理解跨文化模型的整体解释能力，相对表现以及研究主题对变异的敏感性提供了重要信息。此外，我们的发现可能会被用于为将来的研究提供功率分析方面的信息。我们讨论对研究和实践的意义。