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While the ACORN assessment (Nehm and Ha 2011; Opfer et al. 2012) contrasted items with plants and animals, analyses of student responses to those items quantified the frequency of intuitive responses rather than identifying differences in types of intuitive responses for plant versus animal items.
Each participant's ratings for individual meal descriptions were modelled using fMRI and to probe the representation of attractiveness, we contrasted items rated high with ones rated low on that criterion.
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There is something consistent between "After Bathing At Baxter's" and "Crown of Creation" that changes the relationship between the albums from two contrasting items in a chronology to true complements.
Principal component analysis of the residuals identified two sets of contrasting items from which individual person estimates were obtained.
In contrast, items of the ACCEPTANCE and INTENTION measures as well as the measures of the affective domain are polytomously scored.
The items in discussion task appeared more difficult for school A/B than they were for school C. In contrast, items in individual presentation task were easier for school A/B than they were for school C. Fig. 2 Relative item difficulty for the three schools.
In contrast, items assessed on leave prescriptions (4.5%1.9%to7.0 7.0%)) and those that were rewritten by prescribers (3.6% (2.6%to4.6.6%)) had lower PE rates.
In contrast, items that were encoded visually and represented auditorily (Vis:Aud) showed the steepest decline in accuracy (linear coefficient = −0.05131, t = −13.2, P < 0.0001).
Our model did not incorporate these interactions, which may explain why turtles did not appear to select for high contrast items.
In contrast, items with MNSQ values < 1.00 present over-fit (which suggests that there is less variation in the observed data than in the model and, therefore, that the model predicts the data too well, causing inflated summary statistics).
In contrast, items addressing external factors inhibiting change and extrinsic motivation were almost all excluded in this round; examples include 'we are under too much pressure to do our job effectively' and 'we feel pressure to go along with this change' respectively.
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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