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As a final modification, we therefore selected to drop item 1 and rerun model 2. We dropped item 1 based on: (1) the error theory (that item 1 had redundancy with item 2), (2) that it (item 1) received the lowest I-CVI score (Table 2), and (3) that it (item 1) displayed a lower factor loading compared to item 2 in the PCA (Table 3) and CFA (Table 4).
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We then reran models using a concussion history that considered only college and professional sport-related concussions to determine how effect estimates changed relative to a total concussion history.
We reran Models 1 8, stratified by shock status.
We reran models adding BMI and center and found no meaningful changes in the results.
We also reran models of body mass of the children at 7 years controlling for both maternal and child PBDE concentrations in the same model.
Analyses were again rerun with model = 2, or the multiple-ratios model, producing different ω for groups labelled in the tree file.
One then would shut down the airlines and rerun the model to (a presumably different) equilibrium.
In this study, the Markov Chain Monte Carlo (MCMC) simulations were rerun using Model 3 for simplicity, to investigate the sensitivity of the results to different choices of hyper-parameters.
We then removed the interaction and reran the model.
We also reran the model after excluding studies that were not CBT based.
When extent of HIT implementation was not statistically significant, we reran the model without these variables.
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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