Last, we tested a series of CFA models, based on the common use of this method in previous GFP studies. We constructed a series of models that varied from estimating a single-factor solution for each measure, a two-factor solution for each measure, and a shared 6-factor solution. However, we encountered several estimation challenges and a number of modeling approaches based on prior GFP research yielded inadmissible solutions. Despite our attempts to address Heywood cases and negative factor variances, the various CFA models yielded a complicated pattern of results that failed to support the GFP hypothesis.
