This study introduces an approach to estimate the uncertainty in bibliometric
indicator values that is caused by data errors. This approach utilizes Bayesian
regression models, estimated from empirical data samples, which are used to
predict error-free data. Through direct Monte Carlo simulation -- drawing
predicted data from the estimated regression models a large number of times for
the same input data -- probability distributions for indicator values can be
obtained, which provide the information on their uncertainty due to data
errors. It is demonstrated how uncertainty in base quantities, such as the
number of publications of a unit of certain document types and the number of
citations of a publication, can be propagated along a measurement model into
final indicator values. This method can be used to estimate the uncertainty of
indicator values due to sources of errors with known error distributions. The
approach is demonstrated with simple synthetic examples for instructive
purposes and real bibliometric research evaluation data to show its possible
application in practice.Comment: 31 pages, 5 figure