English logo
Boğaziçi University Library
Digital Archive
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
English logo
Boğaziçi University Library
Digital Archive
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Candan, Ali."

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    A continuous auditing implementation with Benford’s law and relative discrepancies to an SME in Turkey
    (Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2021., 2021.) Candan, Ali.; Coşkun, Ali.
    In this study, we analyze the proper use of Benford's Law in the technique of continuous auditing to determine the audit sample of a Small to Medium-Sized Enterprise (SME). Most other studies analyze corporate firms; however, we tested an SME with a relatively small data set. While testing, we determined the best technique, frequency, and data-set type to create an audit sample. We analyzed weekly and monthly time intervals. Also, we sought to answer the question of which dataset to analyze: the cumulative or non-cumulative one. To answer these questions, we benefited from Benford’s analysis and relative discrepancies techniques. The results demonstrate that using the relative discrepancies technique with a weekly cumulative dataset is the best option in our case.

DSpace software copyright © 2002-2025 LYRASIS

  • Cookie settings
  • Send Feedback