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A CAT is a digital questionnaire tailored to the individual patient, resulting in each item (question) contributing valuable information, while maintaining comparability across patients.
To answer the first research question, contributing factors for the explanation of disability in fibromyalgia were explored based on a linear regression analysis.
On occasion, respondents provided multiple answers to a question, contributing to a number of responses greater than the total number of respondents.
The main issue regarding epistemic relevance (e.g. [ 56- 58, 79]) is whether the study makes a contribution to academic ethics, for example by adding new knowledge (e.g. developing a sound ethical argumentation for the topic in question), contributing to an ongoing controversy (e.g. providing a new perspective), or criticizing established positions on a theoretical or applied level.
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If an event is not itself a free action, and if no free action by the agent in question contributed to that event, then, we may say, it is not up to the agent whether that event occurs.
Each correct answer to a knowledge question contributed one point to a composite knowledge score.
WS formulated the research question, contributed to the conception of the study, and helped write the study protocol.
Factorial analysis showed a mono-component structure (Eigenvalue 3.628) as well as an item loading between 0.558 (Question 6) and 0.892 (Question 3), indicating that every question contributed significantly to the aggregate result.
Questions with more than one possible correct answer were weighted by the inverse of the total number of correct answers so that no single question contributed more than one point out of fifteen to the score.
The best candidate topics are decided by a voting scheme, where each question contributes to a vote for its original topic ("Gynecology" has 3 votes, and "Infertility" has 1 vote).
Decomposition estimates can be negative or positive; negative estimates indicate that the variable in question contributes to the inequality in the direction which runs counter to the overall inequality.
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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