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Generalization and induction: Misconceptions, clarifications and a classification of induction
MIS Quarterly
  • Eric W. K. TSANG, University of Texas at Dallas
  • John N. WILLIAMS, Singapore Management University
Publication Type
Journal Article
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Publication Date
In “Generalizing Generalizability in Information Systems Research,” Lee and Baskerville (2003) try to clarify generalization and classify it into four types. Unfortunately, their account is problematic. We propose repairs. Central among these is our balance-of-evidence argument that we should adopt the view that Hume’s problem of induction has a solution, even if we do not know what it is. We build upon this by proposing an alternative classification of induction. There are five types of generalization: (1) theoretical, (2) within-population, (3) cross-population, (4) contextual, and (5) temporal, with theoretical generalization being across the empirical and theoretical levels and the rest within the empirical level. Our classification also includes two kinds of inductive reasoning that do not belong to the domain of generalization. We then discuss the implications of our classification for information systems research.
  • Research methodology,
  • generalization,
  • generalizability,
  • induction,
  • deduction,
  • statistical generalization,
  • statistical syllogism,
  • inductive analogy,
  • Hume’s problem of induction
University of Minnesota
Creative Commons License
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
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Citation Information
Eric W. K. TSANG and John N. WILLIAMS. "Generalization and induction: Misconceptions, clarifications and a classification of induction" MIS Quarterly Vol. 36 Iss. 3 (2012) p. 729 - 748 ISSN: 0276-7783
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