Abstract
This dissertation discusses sample size determination to obtain the
desired Bayes factor if the researchers use the Bayes factor to evaluate the null, unconstrained,
and informative hypotheses. The informative hypothesis
can express the specific expectation of the researchers through
(in)equality constraints among parameters of interest in a statistical
model. The evidence in favor of one hypothesis compared to another
can be quantified by the Bayes factor. If the Bayes factor cannot reach
a convincing value in a sample of a particular size, the study would
produce an inconclusive result. Thus, Bayesian statisticians may be
interested in the determination of sample sizes to obtain the desired
Bayes factor. This dissertation develops an R package SSDbain to help applied
researchers to plan the sample size if they use the Bayes factor to evaluate hypotheses. This package can be used to calculate the sample size for null, unconstrained, and informative hypotheses for a two-sample t-test, one-way ANOVA, and multiple
linear regression. The sample size is determined such that
the probability that the Bayes factor exceeds a pre-specified threshold
reaches a pre-specified value. With the tool provided, the researchers
can easily plan their sample size before data collection.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Award date | 7 Apr 2022 |
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DOIs | |
Publication status | Published - 7 Apr 2022 |
Keywords
- Sample size determination
- Bayes factor
- informative hypothesis
- SSDbain