# Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Population Segment** & **Born** & **Citizenship** & **Living** & **Language** & **Religion** & **Civic** & **Feeling** & **Ancestors** \\ \hline \multicolumn{10}{c}{**Country**} \\ Great Britain (2003) & 0.3298 & 0.3452 & 0.2526 & 0.4605 & 0.4499 & 0.3135 & 0.3320 & 0.4665 \\ Great Britain (2013) & 0.2889 & 0.3100 & 0.1897 & 0.3412 & 0.4318 & 0.2712 & 0.3022 & 0.4212 \\ United States (2003) & 0.2887 & 0.0474 & 0.0502 & 0.1225 & 0.1943 & 0.2064 & 0.1806 & 0.6134 \\ United States (2013) & 0.3487 & 0.2379 & 0.2437 & 0.3497 & 0.4298 & 0.2461 & 0.2733 & 0.4742 \\ \hline \multicolumn{10}{c}{**Political Orientation**} \\ \hline Far left (2003) & 0.3645 & 0.3772 & 0.2546 & 0.5141 & 0.4530 & 0.3192 & 0.3136 & 0.4881 \\ Far left (2013) & 0.3541 & 0.3976 & 0.2851 & 0.7240 & 0.3780 & 0.3466 & 0.3638 & 0.5316 \\ Left, centre left (2003) & 0.3569 & 0.3486 & 0.2466 & 0.4516 & 0.4580 & 0.2865 & 0.3077 & 0.4621 \\ Left, centre left (2013) & 0.3607 & 0.3785 & 0.2683 & 0.5955 & 0.3874 & 0.3188 & 0.3332 & 0.4895 \\ Centre, liberal (2003) & 0.3393 & 0.3313 & 0.2343 & 0.4153 & 0.4735 & 0.2938 & 0.2775 & 0.4356 \\ Centre, liberal (2013) & 0.3318 & 0.3199 & 0.2244 & 0.4929 & 0.4735 & 0.3077 & 0.2911 & 0.4350 \\ Right (2003) & 0.3091 & 0.2958 & 0.2289 & 0.4103 & 0.4883 & 0.2711 & 0.2616 & 0.3911 \\ Right (2013) & 0.3159 & 0.2501 & 0.2032 & 0.2556 & 0.4706 & 0.1812 & 0.2244 & 0.4148 \\ Far right (2003) & 0.1488 & 0.2147 & 0.1535 & 0.6048 & 0.3939 & 0.3049 & 0.1906 & 0.1359 \\ Far right (2013) & 0.0702 & 0.1509 & 0.1004 & 0.1076 & 0.5185 & 0.0795 & 0.0592 & 0.1708 \\ \hline \multicolumn{10}{c}{Born = to have been born in (country); Citizenship = to have the (country) citizenship; Living = to have lived in (country) for most of one’s life; Language = to be able to speak the (country) language; Religion = to be (religion); Civic = to respect the (country)’s political institutions and laws; feeling = to feel (country nationality); Ancestors = to have (country nationality) ancestry.} \\ \end{tabular}
\end{table} 
Table 5: _Cont._The paper analysed only ten countries because the introduction of other countries caused the instability of the model estimated by MGCFA. In contrast, FHA, as a deterministic model, does not need to be adjusted and the results show a good adjustment to the full scalar model. Nevertheless, reaching the full scalar model is always difficult and unlikely, therefore partial scalar models are often adopted. This result leads to the conclusion that the FHA can be an adequate tool to analyse national identity and that it provides robust results without the need of major structural changes to the models. Consequently, a new frontier is open to social scientists when they deal with the study of latent variables, such as national identity. Indeed, FHA for its deterministic nature is more flexible than other structural equation models because they are not so constrained to restricted adjustment properties. In sum, FHA can be a beneficial tool to conduct research on national identity and other antecedents and determinants in social science, such as attitudes towards immigrants and patriotism. It is not easy to assess that FHA is a viable method for analysing other ordinal social science constructs when an application is only based on the nationhood construct. Several other applications across different topics and scales, such as self-reported health or factual knowledge in ordinal items, should be studied to promote the use of FHA in social science. Other interesting lines for future research might be based on the analysis of individual national identity models that show how to go beyond the existing traditional research on nationalism, such as fuzzy clustering methods or latent class models. These methods could benefit the understanding of the complex interplay between nationhood concepts and attitudes towards immigrants. Conceptualization, J.C.M. and A.I. ; methodology, J.C.M. ; software, J.C.M. and A.I. ; validation, J.C.M. and A.I. ; investigation, J.C.M. and A.I.