The results of the EFA of the IPSS-SR showed a Kaiser-Meyer-Olkin (KMO) score of 0.86, indicating that the data matrix was valid for the factor analysis (
31). Seven eigenvalues >1 were identified, and the parallel analysis pointed to six factors. The scree plot pointed to the presence of three to seven factors. These initial solutions can be found in Appendixes E–G of the online
supplement. On the basis of the values in the parallel analysis (four eigenvalues >1, with the remaining eigenvalues <1 and closer to the randomly created values) and the scree test, we decided to retain four factors in the final model and to test it by using a CFA. We conducted a second EFA with all 31 items forced into four factors (see Appendix H of the online
supplement for the factor loadings). We deleted items 11 and 21 because their factor loadings were <0.3 for the factors in the pattern matrix. No items had cross-loadings less than a 0.15 difference from an item’s highest factor loading in the pattern matrix. We conducted a third EFA without items 11 and 21 and by forcing the model to retain four factors (see Appendix I of the online
supplement for the factor loadings). Subsequently, we tested this four-factor structure by using a CFA and compared it with the original five-factor solution that was expected based on the development of the IPSS-SR. The four-factor model resulted in mediocre model fit, but the theoretically based five-factor solution showed even worse fit (
Table 2, models 3 and 4). Therefore, we decided to additionally explore and test three-, six-, and seven-factor models and a higher-order solution model (i.e., in this type of model, one higher order factor is modeled to explain the correlations between the theoretically based five factors). Before testing the solutions by using a CFA for each potential factor solution, the number of factors was forced into the EFA to identify items that should be deleted because of low factor loadings (<0.3) or cross-loadings with less than a 0.15 difference from an item’s highest factor loading in the pattern matrix. The higher-order structure and seven-factor solutions (both models including all items) could not be estimated, the three-factor model (in this model, items 11 and 19 had been deleted due to a factor loading <0.3) showed no better fit, but the six-factor structure (this model included all items) showed better fit compared with the initial four-factor model (
Table 2, models 5–8). However, output of the six-factor model indicated a correlation >1 between the first and sixth factor, and we therefore decided to merge these factors into the first factor. Face validity of the five remaining factors was inspected and considered low for one of the factors (factor 5, items 10, 16, 17, 22, 24 and 25) (for the test of this model, see
Table 2, model 9). Scaling these items under factors they seemed to belong to (factor 3, items 10, 16, 24; factor 2, items 17, 22, and 25) increased face validity but did not improve fit (
Table 2, model 10), and we therefore decided to delete these items. Deleting the items led to better fit (
Table 3, model 11), although mediocre results were shown on the CFI, TLI, and RMSEA. The remaining four factors were “communication skills and social support” (factor 1), “understanding my own feelings” (factor 2), “coping with grief and major life change” (factor 3), and “understanding feelings of others” (factor 4).