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Three different models were created to help determine which model fit the data best.docx

# Three different models were created to help determine which model fit the data best.docx

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Three different models were created to help determine which model fit the data best. Their model fit indices (see Table 4.6) were examined using criteria recommended by Hu and Bentler (1999) and Kline (1998) and results indicated the three-factor model best represents the data. Table 4.6 Overall model fit indices for CFA Model Df P GFI CFI RMSEA CMIN/df SRMR Criteria N/A > .05 > .095 > 0 .95 <0 .06 < 3 < 0.08 3-factor 24 .141 .953 .988 .048 1.311 .0497 2-factor 26 .000 .866 .900 .132 3.345 .086 1-factor 27 .000 .701 .701 .224 7.730 1.250 Note. CFI = Comparative Fit Index; RMSEA = root mean square error of approximation; CMIN; SRMR = standardized root mean square residual The fit statistics of the one and two-factor models provided a poor fit for both models with all indices failing to meet the required criteria. In contrast, the three-factor model obtained values demonstrating a good model fit (CFI= .988; RMSEA = .048; SRMR = .0497) and these results aligned with the findings of Aljughaiman and Ayoub (2012) on the Aurora Battery. The three-factor CFA model is presented in Figure 4.1. Its standardized path coefficients for the analytical factor produced the lowest mean of the hypothesized three factors (mean = .71), however, all its coefficients were considered at least acceptable indicators of its latent factor (> .06). Path coefficients of the latent creative and practical factors were all above .08 suggesting all items were strong indicators of the latent factors. In addition, the CFA model showed latent factors were positively covaried ( r = .54 to .61) at a moderate level (Hair, Black, Babin, Anderson, 2010). WITH HAIR THEY ARE CONSIDRED STRONG AT .6…

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A convergent validity test was run
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• Winter '16
• Dr. Muturi
• Type I and type II errors, AURORA, three-factor model

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