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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Using cognitive interviewing to improve questionnaires: An exemplar study focusing on individual and
Kendra Kamp1, Gwen Wyatt1, Sharon Dudley-Brown2
1Michigan State University, 1355 Bogue St, East Lansing, MI 48824, United States.
Abstract:
Questionnaires are a standard component of quantitative research, but seldom do researchers consider the importance of item clarity and participant comprehension. This is particularly true among the often overlooked individual and condition-specific items which characterize the patient and disease process. Cognitive interviewing is one approach to assess item clarity and identify how participants understand and respond to questions. The purpose of this paper is to describe the process of cognitive interviewing used to identify questions that are unclear or challenging to answer for a unique population, emerging adults (age 18-29) with inflammatory bowel disease (IBD). Through cognitive interviewing four areas were identified as needing improvement among individual and condition-specific items: 1) clarity - describing terms and adding details to item directions; 2) cognitive recall burden - rewording questions to avoid the need for mental math, 3) timeframe - adding phrases like 'in the past 2 weeks,' and 4) question relevance - including items on disease remission. Analysis of these four areas may guide other researchers working with IBD patients to obtain high quality data, as well as stimulate questionnaire adaption using cognitive interviewing with other populations. Cognitive interviewing can be useful when drafting a new questionnaire or when adapting an established questionnaire; in either case, it can enhance item clarity and participant comprehension.
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