Model 1 allowed each scale from a given measure to load on to one of five factors (i.e., measure specific general factors) which were allowed to correlate freely. That is, we estimated a TCI GFP, a NEO-PI-R GFP, a CPI GFP, a HPI GFP, and a MPQ GFP. These five GFPs were then freed to correlate with each other. This model had a poor fit across multiple indices (Table 4). In addition, the matrix for the factor correlations had a large number of Heywood cases (i.e., correlations greater than 1.00). In spite of these limitations, we estimated Model 2 with a second-order structure that allowed all of the measure specific GFPs to load freely on one higher-order factor (i.e., the across measure GFP). Overall fit for this model was also poor and demonstrated a decrease in fit (χ2 df = 5; χ2 = 87.57, p < .001). Moreover, there were indications of serious misspecification with three (NEO-PI-R, MPQ, HPI) of the five first-order factors having negative variances. Thus, this approach failed to provide clear support for a GFP.
