Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity

National identity studies diverge on several issues, such as the number of factors and their respective items’ adscription. Multi-Group Confirmatory Factor Analysis (MGCFA) is standard method applied to cross-national datasets. Differences between groups can be result measurement artefacts. We argue that these problems better addressed by an alternative approach builds a synthetic indicator named Relative Identity Synthetic Indicator (RNISI), based Fuzzy Hybrid (FHA). The study aims shed some light latent variable national comparing two methodologies: classic most often used Fuzzy-Hybrid Approach, which, our knowledge, has not been previously applied. This empirical was dataset from across ten countries using waves (2003 2013) International Social Survey Programme (ISSP). FHA results were compared with those obtained MGCFA models in which built second-order construct depends ethnic, ancestry civic first-order variables. comparison lets us conclude considered valid tool measure groups, provide additional information form elasticity figures. These figures employed analyse indicator’s sensitivity group for each items included construct.