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    1. Psykologi och pedagogik
    2. Psykologi

    Applying Benford's Law for Assessing the Validity of Social Science Data

    AvMichael A. Long,Paul B. Stretesky

    Inbunden, Engelska, 2023

    971 kr

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    Häftad

    306 kr

    Beskrivning

    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.

    Produktinformation

    • Utgivningsdatum:2023-11-23
    • Mått:155 x 235 x 17 mm
    • Vikt:494 g
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:224
    • Förlag:Cambridge University Press
    • ISBN:9781009123075

    Utforska kategorier

    • Psykologi inom Psykologi och pedagogik
    • Sociologi inom Samhälle och politik

    Mer om författaren

    Michael A. Long is a Professor of Sociology at Oklahoma State University, USA. He is the author or co-author of six books and over 90 journal articles and book chapters primarily in the areas of environmental sociology, green criminology, sustainability, food insecurity, public health, and quantitative methodology. He has received funding from the National Science Foundation, US Department of Agriculture, the British Academy, among others for his research. Paul B. Stretesky is a Professor of Criminology at Northumbria University, UK. He is the author of eight books and over 100 journal articles and book chapters, primarily in the areas of environmental justice, environmental sociology, green criminology and food insecurity. He has funding from the Natural Environment Research Council (UK) for his research. Kenneth J. Berry is an Emeritus Professor of Sociology at Colorado State University, USA. He is the author of seven books and 185 referred journal articles. He is a leading scholar in the statistics subfield known as 'permutation methods.' In 2002 he was named John N. Stern Distinguished Professor in the College of Liberal Arts at Colorado State University. Janis E. Johnston works at the US Department of Agriculture (USDA) as a policy analyst. She is co-editor of a book on social equality and a co-author of five statistics books that focus on the subfield of statistics known as permutation methods. Michael J. Lynch is a Professor of Criminology at the University of South Florida, USA. He is the founder of the subfield of criminology known as 'green criminology.' He is the author/editor of 20 books, some of which have multiple editions, more than 120 journal articles and 55 book chapters.

    Recensioner i media

    '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

    Innehållsförteckning

    • 1. Introduction; 2. Validity and self-reported data; 3. Benford's law and assessing conformity; 4. Data characteristics and the workflow of Benford agreement analysis; 5. Benford agreement analysis of the Sea Around Us project fish landings data; 6. Benford agreement analysis of US and global COVID-19 new-cases data; 7. Assessing impacts of problematic Benford validity; 8. Conclusion.