Development of an accessible and user-friendly web-application (BayesiCALLY) for conducting Bayesian confirmatory factor analysis.

Background: Bayesian questionnaire validation is generally more efficient compared to conventional validation methods. However, possibly due to linguistic and conceptual barriers, the Bayesian approach is underutilized in questionnaire validation studies in primary health care research. The objective is to develop a better acceptable and usable Bayesian framework for questionnaire validation that will employ online surveys for obtaining required expert input, so called prior elicitation.

BayesiCALLY Application

(Version 16.0, last updated Jan 27, 2021):

This web app is designed for researchers and clinicians with any level of statistical training who are interested in validating a questionnaire. The tool (BayesiCALLY) uses confirmatory factor analysis (CFA) to estimate relative importance of each question with respect to its domain, i.e. item-domain correlation. The web app (BayesiCALLY) output presents results from both classic and Bayesian CFA.

Guides & resources:

Part 1: Introduction to a Questionnaire Validation Tool (BayesiCALLY) with Bayesian Confirmatory Factor Analysis;

Part 2: Questionnaire validation, small sample size & Bayesian Confirmatory Factor Analysis with (BayesiCALLY);

Part 3: Bayesian Confirmatory Factor Analysis & questionnaire validation on patient satisfaction with (BayesiCALLY);

Part 4: Bayesian Confirmatory Factor Analysis web application: BayesiCALLY;

Part 5: Summary Bayesian Questionnaire Validation.

Contribution: This will enable and promote knowledge integration from diverse stakeholder perspectives at an early stage of questionnaire development. This will also lead to an effective use of preliminary study data or expert input in the validation process.

Acknowledgement: The work is done in cooperation with the method development platform of the Quebec SPOR-SUPPORT Unit (Canada).

Impact: Ultimately, this research will translate into higher quality of evidence-based care employing questionnaires, improved patient outcomes and increased cost-efficiencies in health-care system.

BayesiCALLY Application Evaluation

Contact: hao(dot)zhang5(at)mail(dot)mcgill(dot)ca

References:

Merkle, E. C., & Rosseel, Y. (2015). blavaan: Bayesian structural equation models via parameter expansion. arXiv preprint arXiv:1511.05604.

Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of statistical software, 48(2), 1-36.

Recent publications:

Zhang, H. & Schuster, T. (2021). A methodological review protocol of the use of Bayesian factor analysis in primary care research. Systematic Reviews, 10(1), pp.1-5.

Gagnon, J., Hersson-Edery, F., Reoch, J., Zhang, H., Schuster, T., & Pluye, P. (2020). Development and Validation of the McGill Empowerment Assessment–Diabetes (MEA-D). Diabetes Spectrum.

Zhang, H., Schuster, T. (2018). Questionnaire instrument development in primary health care research - A plea for using Bayesian inference. Canadian Family Physician. 64 (9) 699-700.

Conference publications:

Zhang, H., & Schuster, T. BayesiCALLY—a new Bayesian questionnaire validation tool for instrument development in primary care research. 47th North American Primary Care Research Group (NAPCRG) Annual Meeting, Toronto, Canada, Nov 16-20, 2019. DOI: https://www.napcrg.org/conferences/2001/sessions/1140

Zhang, H., Schuster, T. Inform Prior Elicitation for Bayesian Questionnaire Validation with Confirmatory Factor Analysis. Joint Conference of the Sub-Saharan Africa Network (SUSAN) of the International Biometrics Society (IBS) and DELTAS Africa Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB), Cape Town, South Africa, Sep 8-11, 2019. DOI: http://ibssusan2019.samrc.ac.za/AbstractBook.pdf