Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/244580
Authors: 
Bodnar, Olha
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 6/2021
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
In the Cochrane Database of Systematic Reviews (CDSR) 75% of reported meta-analyses contain five or fewer studies. For a small dataset a reasonable goodness-of-fit test on a statistical model cannot be performed since either it requires a large sample size for the validity of asymptotic approximation or it might be not powerful enough to detect a deviation from the target model. Random effects model under the assumption of normality is commonly used in many fields of science. It also appears to be a classical approach for data reduction in interlaboratory studies in metrology and in meta-analysis in medicine. However, the assumption of normality might not be fulfilled in many practical applications. If a data set is small, then no statistical test on distribution will perform well. The intrinsic Bayes factor is used for selecting an appropriate probability model among several competitors, which not necessarily have to be nested. We apply the proposed methodology to the measurement results used to determine the Newtonian constant of gravitation and the Planck constant.
Subjects: 
random effects model
t-distribution
Bayesian model selection
intrinsic Bayes factor
Newtonian constant of gravitation
Planck constant
JEL: 
C02
C11
C18
Document Type: 
Working Paper

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