Benford's Law is a probability distribution for the likelihood of
the leading digit in a set of numbers. This book seeks to improve
and systematize the use of Benford's Law in the social sciences to
assess the validity of self-reported data. The authors first
introduce a new measure of conformity to the Benford distribution
that is created using permutation statistical methods and employs
the concept of statistical agreement. In a switch from a typical
Benford application, this book moves away from using Benford's Law
to test whether the data conform to the Benford distribution, to
using it to draw conclusions about the validity of the data. The
concept of 'Benford validity' is developed, which indicates whether
a dataset is valid based on comparisons with the Benford
distribution and, in relation to this, diagnostic procedure that
assesses the impact of not having Benford validity on data analysis
is devised.
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