Application of Bayesian Estimation to Educational Research - Nnadi, Fidelis
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The use of continuously varying opinionated data is synonyms with Educational study. Survey studies have shown that sample size of respondents limits the generalization of the findings to the target population. To overcome the named challenges, Bayesian estimation's MCMC algorithm, specifically random walk was applied to optimize the sample size space with the intention to obtain the least measurement error. With random walk algorithm, the findings of a study can very confidently be applied unto the target population.

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Produktbeschreibung
The use of continuously varying opinionated data is synonyms with Educational study. Survey studies have shown that sample size of respondents limits the generalization of the findings to the target population. To overcome the named challenges, Bayesian estimation's MCMC algorithm, specifically random walk was applied to optimize the sample size space with the intention to obtain the least measurement error. With random walk algorithm, the findings of a study can very confidently be applied unto the target population.
Autorenporträt
Dr. Fidelis O. Nnadi holds a Ph.D in Physics Education from The University of Nigeria under the guidance of a renowned guru in Physics Education, Measurement and Evaluation, Prof B G Nworgu. He obtained his M.Sc and B.Sc degrees in Physics Education from ESUT, where he trains younger scholars. Dr. Nnadi is an academic with interest in modeling.