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. 2018 Feb;40(2):257-269.
doi: 10.1177/0193945916681564. Epub 2016 Dec 5.

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software

Affiliations

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software

Marjorie Bott et al. West J Nurs Res. 2018 Feb.

Abstract

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

Keywords: Bayesian methods; free software; instrument development methods.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Screen Shot of CBID Software Showing Classical Analysis Type
Figure 2
Figure 2
Comparison of Mplus Output to CBID output using Classical Approach
Figure 3
Figure 3
Screen Shot of CBID Software Showing Bayesian Analysis Type with Flat Uninformative Prior
Figure 4
Figure 4
Bayesian Analysis Output on Ordinal Data using Flat Uninformative Prior
Figure 5
Figure 5
Screen Shot of CBID Software Showing Bayesian Analysis Type with Expert Informative Prior
Figure 6
Figure 6
Bayesian Analysis Output using Expert Informative Prior with Moderate Level of Expertise

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