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.

‘This impressive book, written by and for social scientists, provides an excellent introduction and comprehensive overview of the application of Benford’s Law in social research. Using real-world datasets and easy-to-follow examples, the authors have done an outstanding job in demonstrating how their methodology/tool can be implemented to evaluate and enhance the validity of self-reported social data.’

Jayajit Chakraborty - University of Texas at El Paso, USA

‘Long and co-authors offer a systematic and accessible approach, ‘Benford agreement analysis’, to dealing with data validity concerns. The examples and discussion of when to use the approach make this book equally valuable for methodologists and empirical social scientists, and will work very well in research methods and statistics courses.’

Andrew Jorgenson - University of British Columbia, Canada