Abstract
Inequitable gender norms shape adolescents' perceptions and behaviours, increasing the risk for adverse health outcomes as adults. However, there is a lack of reliable scales to measure these norms. The Gender and Adolescence: Global Evidence (GAGE) project proposes a scale for adolescents aged 10-19 years considered vulnerable, (i) distinguishing between individual-level gender attitudes and community-level gender norms (2 factors), and (ii) categorising items into five domains (e.g., education; 5 factors). As part of validating this scale, we analyse the two- and five-factor structure using GAGE datasets from Ethiopia and Bangladesh. We performed Explorative Factor Analyses (EFA) using Principal Axis Factoring and oblique rotation. We tested sampling adequacy using Bartlett's test of sphericity and the Kaiser-Meyer Olkin measure. In the EFA, we tested the two-factor structure and refined the initial five-factor structure by removing variables that failed to load onto a factor or exhibited cross-loadings. Next, we removed variables with low communalities (<0.2) and low factor loadings (<0.3). The EFAs comprised 6,183 observations from the Ethiopia and 2,245 observations from the Bangladesh dataset. The initial five-factor solution seemed more appropriate than the two-factor (individual-community) distinction in both datasets, and only the refined five-factor structure provided a solution in which the items corresponded to the five domains. However, this only applies to 17 and 15 of the original 30 items in Ethiopia and Bangladesh, respectively, and two of the factors only include two variables each. The five-domain structure proved more suitable for the Ethiopia and Bangladesh contexts than the individual-community distinction, but only for a reduced set of items. We thus propose an adaptation for the GAGE gender norms scale in Ethiopia and Bangladesh. Conceptual challenges, such as questionable domain assignments, highlight the need to further refine the scale and to confirm the results by Confirmatory Factor Analysis.