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Based on these findings it can be concluded that a single factor model best fits the data in the study.
A two-factor model offered an acceptable account of the data; however, when a single correlated error involving two highly similar items was included, a single factor model fit the data very well.
The hypothesized measurement model fit the data better than a single factor model: χ 2 (158, N = 572) = 594.29, p = 0.00; RMSEA = 0.088 900 % CI 0.079 to 0.096); CFI = 0.881; and AIC = 738.29) both in terms of the fit statistics and when directly contrasted with a change in AIC.
Model 1 (see Figure 1) tested the cross-group fit of a single factor model.
These findings provide further support for a single factor model underlying the multiple aspects of autistic traits and symptoms.
We fitted a single factor model to each group to ensure that the measurement model fit appropriately.
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A single-factor model remained a borderline adequate fit (explaining approximately 20% of the variance) for the interitem correlation matrix on all three occasions.
A single-factor model for the E-VS and a 2-factor model for the E-BS were resulted from the EFA with large factor loadings and about 47% and 56% of variance explained respectively.
RESULTS Extent of nonadherence items produced reliable scores across time and fit a single-factor model (CFI = 0.99).
Confirmatory factor analysis showed that a single-factor model comprised of five domain scores was the best fit.
(a) Single-factor model and (b) faceted model with four correlated factors representing the problem-solving steps.
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