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With a log-normal scale, the distributions for the 16 items showed a linear pattern for the 'Some' to 'Most' responses (figure 2B), suggesting that these 16 items exhibited an exponential pattern for this response level.
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Consistently recalled items exhibited a higher degree of output-bound accuracy than forgotten items (t [201] = 8.54, p < .001).001
As can be seen in Fig. 2, consistently recalled items exhibited a higher degree of output-bound accuracy than either forgotten (t [64] = 4.50, p < .001) or reminisced (t [67] = 5.69, p < .001) items.
As can be seen in Fig. 2, consistent items exhibited a higher degree of output-bound accuracy than either forgotten (t [136] = 7.29, p < .001) or reminisced (t [139] = 7.41, p < .001) items.
We found the distributions of the 16 negative items exhibited a common mathematical distribution.
Social support, effect of kidney disease, and burden of kidney disease items exhibited a stronger relationship (>0.7).
All items exhibited a good spread of responses across the three categories, with no item demonstrating clear floor or ceiling effects.
Second, although the 16 negative symptom items exhibited a linear pattern with a log-normal scale, analysis based on other mathematical models was not performed.
This is consistent with the findings from the pairwise correlation analysis, specifically that all four items exhibited a relative lower correlation relationship with CHU9D (r < 0.4).
In addition to the previous discussion concerning the poor correlations between reviewers, certain test items exhibited a high level of disagreement for certain validation questions.
*DIF CHI-square probability from TABLE 30.4 of WINSTEPS software §biserial correlations Overall, these 18 items exhibited a good model-data fit.
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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