<p>When applying analyses across countries in a large-scale dataset, conventional wisdom suggests that between-group variability should be smaller than within-individual variability to ensure the dataset’s applicability. This study investigates how variance components are estimated differently across multivariate G-theory designs in a large-scale nested dataset, using both simulated data and the PISA 2015 dataset. It also explores the applicability of the scale in such datasets. The findings indicate that the estimated variance components between countries are smaller than the person-level variance in PISA 2015, and that they are much smaller in applicable datasets than in inapplicable ones. The between-country variance in the PISA attitudes toward science scale is substantially smaller than person-level variance. Furthermore, multi-group measurement invariance testing supported scalar invariance across all 58 countries, providing direct evidence that the scale functions equivalently across national contexts. In addition, reliability is generally high across designs; however, it increases with the number of participants in the country-randomized design but remains constant in the country-fixed design. These findings suggest that different designs require different revisions to facets to enhance overall reliability.</p>