The Two-Dimensions-Five-Components Structure of In-group Identification is Invariant across Various Identification Patterns in Different Social Groups

In-group identification has been suggested to consist of two-dimensions (group based self-definition and self-investment) that hierarchically relate to five lower order components (individual self-stereotyping, in-group homogeneity, satisfaction, solidarity, and centrality). The goal of the present research was to test the generalizability of the two-dimensions-five-components structure of in-group identification (Leach et al.’s 2008) across identities with which people show converging and diverging group based self-definition and self-investment. We manipulated the mean level and the linear correlational strength of the two identification dimensions by asking participants to indicate in-groups to which respective identification criteria apply. Confirmatory factor analyses showed that the two-dimensions-five-components model of in-group identification fits both converging and diverging identification patterns better than alternative models, indicating generalizability of the model across various identification patterns. In-group identification has been suggested to consist of two-dimensions (group based self-definition and self-investment) that hierarchically relate to five lower order components (individual self-stereotyping, in-group homogeneity, satisfaction, solidarity, and centrality). The goal of the present research was to test the generalizability of the two-dimensions-five-components structure of in-group identification (Leach et al.’s 2008) across identities with which people show converging and diverging group based self-definition and self-investment. We manipulated the mean level and the linear correlational strength of the two identification dimensions by asking participants to indicate in-groups to which respective identification criteria apply. Confirmatory factor analyses showed that the two-dimensions-five-components model of in-group identification fits both converging and diverging identification patterns better than alternative models, indicating generalizability of the model across various identification patterns.