Validation of the Blended Learning Usability Evaluation–Questionnaire (BLUE-Q) through an innovative Bayesian questionnaire validation approach
- Author:
Anish Kumar ARORA
1
;
Charo RODRIGUEZ
;
Tamara CARVER
;
Hao ZHANG
;
Tibor SCHUSTER
Author Information
- Publication Type:Research article
- From:Journal of Educational Evaluation for Health Professions 2024;21(1):31-
- CountryRepublic of Korea
- Language:0
-
Abstract:
Purpose:The primary aim of this study is to validate the Blended Learning Usability Evaluation–Questionnaire (BLUE-Q) for use in the field of health professions education through a Bayesian approach. As Bayesian questionnaire validation remains elusive, a secondary aim of this article is to serve as a simplified tutorial for engaging in such validation practices in health professions education.
Methods:A total of 10 health education-based experts in blended learning were recruited to participate in a 30-minute interviewer-administered survey. On a 5-point Likert scale, experts rated how well they perceived each item of the BLUE-Q to reflect its underlying usability domain (i.e., effectiveness, efficiency, satisfaction, accessibility, organization, and learner experience). Ratings were descriptively analyzed and converted into beta prior distributions. Participants were also given the option to provide qualitative comments for each item.
Results:After reviewing the computed expert prior distributions, 31 quantitative items were identified as having a probability of “low endorsement” and were thus removed from the questionnaire. Additionally, qualitative comments were used to revise the phrasing and order of items to ensure clarity and logical flow. The BLUE-Q’s final version comprises 23 Likert-scale items and 6 open-ended items.
Conclusion:Questionnaire validation can generally be a complex, time-consuming, and costly process, inhibiting many from engaging in proper validation practices. In this study, we demonstrate that a Bayesian questionnaire validation approach can be a simple, resource-efficient, yet rigorous solution to validating a tool for content and item-domain correlation through the elicitation of domain expert endorsement ratings.
