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Bootstrap analysis of the difference in log-likelihood (logL) between the test model and the controls normalized by the log-likelihood of the test model (ΔlogL/logLtest).
To evaluate the reliability of differences between the test model and controls, we applied a standard bootstrap approach (Martinez and Martinez, 2001).
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The discrepancies between the controls and the test model are particularly pronounced during the beginning of the rising phase of the ramp.
There was no statistically significant difference between the tested model and the data, χ(1) = 2.89, p > 0.05.
There was no statistically significant difference between the tested model and data, χ (2) = 2.03, p > 0.05.
There was no statistically significant difference between the tested model and the data, χ (1) = 3.48, p > 0.05, while the fit indexes had values of CFI = 0.98, RMSEA = 0.13.
There was no statistically significant difference between the tested model and the data, χ (1) = 2.65, p > 0.05, and the fit indexes had appropriate values, CFI = 0.98, RMSEA = 0.01.
One degree of freedom was assumed, representing the difference in the number of free parameters between the tested models.
ΔAIC = difference in AIC between the tested models and the selected model given in bold.
The data used in this research are cross-sectional in nature, which raises concerns about the causal relationships between constructs in the tested model.
The results show a good correlation between the test and model simulation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com