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The fourth and final step of item reduction resulted in 26 items (Table 4).
The second step of item reduction resulted in 33 items (Table 3).
The initial step of item generation garnered a large pool of questions (18 items and 37 sub-items).
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In the final step of the item's representation creation, the system analyzes the feature set and checks whether they are terms or aspects.
The first step, selection of items and scale scoring, can be done by using a combination of various strategies, including an in-depth literature review, consultation of end users (for example, ICU clinicians and patients), and direct clinical observation and other sources.
In fact, the one out-of-step item in this cavalcade of whimsy and wish-fulfilment is Star Trek itself.
In the third step, the invariance of item difficulty hierarchy was detected across diagnostic groups through DIF tests.
In step 3, a set of items was identified that passed the first round of individual rating as well as the second-round discussion.
In sum, for paring down items, we followed these steps: 1) examination of item response variance and deletion of items that did not meet the criterion; 2) Rasch analysis on clusters of items within each domain and deletion of poor-fitting or redundant items; and 3) Rasch analysis of all retained items to ensure fit to the Rasch model and to generate statistics for the instrument as a whole.
The next step is the identification of items (step C).
The related 10-item raw score showed adequate scalability (Loevinger's H coefficient = 0.4208), monotonicity e partial double monotonicity (nonintersections of Item Step Response Functions).
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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