other · The Encyclopedia of Applied Linguistics · 2025
Bayesian Statistics
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Abstract
Abstract Over the past two decades, advances in computational power have facilitated the use of complex statistical modeling in academic research. As a result, sophisticated analytic strategies, such as multilevel modeling and structural equation modeling, are becoming commonplace, and computationally costly procedures, like Bayesian data analysis, have moved to the forefront. In particular, the use of Bayesian methods is gaining ground in the field of applied linguistics. The present overview provides a brief introduction to Bayesian data analysis, covering both why and how one might decide to use Bayesian estimation for statistical modeling, along with some of the commonly referenced advantages and disadvantages of this approach. This primer serves as a starting point for any linguist interested in beginning the journey of understanding and implementing Bayesian methods.
Cite this paper
Casillas, J. V. (2025). Bayesian Statistics. The Encyclopedia of Applied Linguistics, 1–8. https://doi.org/10.1002/9781405198431.wbeal20776
Casillas, Joseph V. “Bayesian Statistics.” The Encyclopedia of Applied Linguistics, 2025, pp. 1–8, https://doi.org/10.1002/9781405198431.wbeal20776.
Casillas, Joseph V. 2025. “Bayesian Statistics.” The Encyclopedia of Applied Linguistics: 1–8. https://doi.org/10.1002/9781405198431.wbeal20776.
Casillas, J.V. (2025) ‘Bayesian Statistics’, The Encyclopedia of Applied Linguistics, pp. 1–8. Available at: https://doi.org/10.1002/9781405198431.wbeal20776.
@misc{casillas2025bayesian,
title = {{Bayesian Statistics}},
author = {Casillas, Joseph V.},
journal = {The Encyclopedia of Applied Linguistics},
pages = {1--8},
year = {2025},
doi = {10.1002/9781405198431.wbeal20776}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.