In this model, the effect of fluid intelligence was strong, as expected and proposed by previous research (e.g., Funke 2010).
A watershed model of individual differences in fluid intelligence.
Fig. 2 Confirmatory factor analysis model for working memory capacity (WMC), fluid intelligence (gF), attention control (AC), mind-wandering during the laboratory tasks (MW), and external distraction during the laboratory tasks (ED).
Finally, to test hypothesis H1.2, we included fluid intelligence in the models M3 and M4 and compared the fit using the LR test.
These explanation patterns also remained stable after the integration of fluid intelligence into the structural model (Fig. 6).
Despite the integration of content knowledge and fluid intelligence, the two-dimensional model of domain-specific problem-solving fitted the data better after all (Table 2, comparison of M5 and M6, Δχ (5) 2 = 39.82, p < .001).001
Finally, we specified a regression model in which CPS ability was the criterion, fluid intelligence, prior knowledge, and general interest (AIST) were the predictors (Figure 4). Figure 4 Regression model with correlated predictors of CPS.
Regression models showed a significant relationship between a fluid intelligence measure and objective hearing aid benefit.
It also had an excellent fit to a one-factor confirmatory factor model and correlated acceptably with other measures of fluid intelligence and participants' grade point average (GPA).
Structure equation modeling revealed a strong relationship between the latent variables fluid intelligence and portal ability (r = .78).78
