Method: This community-based participatory research study contrasted a confirmatory factor analysis on the electronic health literacy scale (eHEALS) with a sample of rural 7th grade children (n=137) residing in three economically distressed counties in Appalachian using a commonly applied estimator, maximum likelihood (ML), and a BSEM approach using noninformative, weekly informative, and informative priors. Model fit and comparison were evaluated by common SEM-Based fit indices and Bayesian fit indices.
Results: The BSEM results demonstrated empirical support for the 1-factor eHEALS model as a parsimonious and reasonable representation of electronic health literacy. The BSEM produced an excellent fitting model (PPP = .50), whereas the ML-based model showed an acceptable fit for the data, χ2(17) = 32.79 (p = .01), RMSEA (CI) = 0.08 (0.04-0.13), CFI= 0.98, TLI= 0.95. The use of informative priors improved the PPP for the models. Reasons for this discrepancy between ML and BSEM are discussed along with potential advantages and caveats with BSEM.
Conclusion: The eHEALS scale demonstrates strong measurement in a limited sample of 7th grade children residing in Appalachian. The current BSEM study highlights the importance of considering a Bayesian approach when planning and conducting social work research, especially for Grand Challenges research.