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Sinval, J., Schaufeli, W. B., De Witte, H., de Beer, L. T., Procházka, J. & Merkle, E. C. (2022). Update your priors! A Bayesian structural equation modeling analysis to the Burnout Assessment Tool. 3rd BAT research seminar.

J. F. Sinval et al.,  "Update your priors! A Bayesian structural equation modeling analysis to the Burnout Assessment Tool", in 3rd BAT research seminar, Leuven, 2022

@misc{sinval2022_1716062299355,
	author = "Sinval, J. and Schaufeli, W. B. and De Witte, H. and de Beer, L. T. and Procházka, J. and Merkle, E. C.",
	title = "Update your priors! A Bayesian structural equation modeling analysis to the Burnout Assessment Tool",
	year = "2022",
	howpublished = "Digital",
	url = "https://burnoutassessmenttool.be/events_eng/"
}

TY  - CPAPER
TI  - Update your priors! A Bayesian structural equation modeling analysis to the Burnout Assessment Tool
T2  - 3rd BAT research seminar
AU  - Sinval, J.
AU  - Schaufeli, W. B.
AU  - De Witte, H.
AU  - de Beer, L. T.
AU  - Procházka, J.
AU  - Merkle, E. C.
PY  - 2022
CY  - Leuven
UR  - https://burnoutassessmenttool.be/events_eng/
AB  - Objective. Burnout Assessment Tool (BAT) is gaining momentum as a psychometric
instrument, it has a sounding theoretical framework and has already provided good validity
evidence via several samples of different countries. BAT is starting to establish itself as the
first-choice instrument of several researchers to measure burnout. Whenever a psychometric
instrument is used it is desirable that its psychometric properties are assessed. However, when
in the presence of small samples that task can be challenging to attain, particularly when using
the frequentist framework (e.g., categorical models). Bayesian structural equation modeling
(BSEM) represents an alternative to the mainstream (frequentist) structural equation modeling
(SEM). BSEM is also particularly useful if the researcher has concerns about model
convergence or problematic measured variable distributions. All these potential advantages are
due to the specification of priors, priors are the heart of Bayesian statistics. Priors allow the user
to incorporate prior knowledge and beliefs into the analysis and establish the hyperparameters.
Such prior beliefs/knowledge will be updated by new information (the data), creating the
posterior distribution for the model parameters. From the previous research with BAT
researchers can extract information to establish their priors and — desirably — obtain more
accurate estimates. The objective of this study was to analyze the use of different priors through
BSEM in samples of workers from four different countries (Czechia, The Netherlands, South
Africa, and Portugal).
Method. A cross-sectional study was conducted with multiocupacional samples of workers
from different countries (nCzechia = 162; nThe Netherlands = 800; nPortugal = 378; nSouth Africa = 660)
from whom BAT’s scores and sociodemographic data was obtained. Prior were defined using
information from previous research using BAT.
Results and Conclusions. The BAT showed good validity evidence based on the internal (via
Bayesian Confirmatory Factor Analysis). BAT’s dimensionality hold, the Bayesian estimates
of internal consistency also provided satisfactory evidence. Sensitivity analyses of impact of
the defined priors on posterior estimates provided insightful results. BSEM provides a more
flexible representation of substantive theory, since it allows to successfully depart from
previous research, updating the knowledge by the integration of the new data (even with small
samples) and providing a degree of uncertainty for the estimates (i.e., credibility intervals).
ER  -