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Conceptualizing Generalizability: New Contributions and a Reply

Tsang and Williams offer some good and provocative ideas in their critique o f our earlier article on generalizing and generalizability. In this essay we will advance some new ideas by building on those collected in both Tsang and Williams and our original article (Lee and Baskerville 2003). Because... Full description

Journal Title: MIS quarterly 2012-09-01, Vol.36 (3), p.749-761
Main Author: Lee, Allen S
Other Authors: Baskerville, Richard L
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: Minneapolis: Management Information Systems Research Center, University of Minnesota
ID: ISSN: 0276-7783
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title: Conceptualizing Generalizability: New Contributions and a Reply
format: Article
creator:
  • Lee, Allen S
  • Baskerville, Richard L
subjects:
  • Accountancy
  • Classification
  • Empirical statement
  • Inductive reasoning
  • Information storage and retrieval systems
  • Information systems
  • Information theory
  • Issues and Opinions
  • Judgment
  • Moral judgment
  • Observational research
  • Reasoning
  • Studies
  • Technology
ispartof: MIS quarterly, 2012-09-01, Vol.36 (3), p.749-761
description: Tsang and Williams offer some good and provocative ideas in their critique o f our earlier article on generalizing and generalizability. In this essay we will advance some new ideas by building on those collected in both Tsang and Williams and our original article (Lee and Baskerville 2003). Because IS is a pluralist scientific discipline, one in which both qualitative and quantitative (and both interpretive and positivist) research approaches are valued, “generalize” is unlikely to be a viable term or concept if only one IS research paradigm may lay claim to it and excludes others from using it. Both papers agree on this point, but approach the problem differently. Where we originally generalized generalizability by offering new language, Tsang and Williams conceptualize generalizability by framing it more closely to its older, more statistically oriented form. We agree about the importance o f induction and about the classification or taxonomy of different types o f induction. We build further in this essay, advancing the ethical questions raised by generalization: A formulation ofjudgment calls that need to be made when generalizing a theory to a new setting. We further demonstrate how the process of generalizing may actually proceed, based on the common ground between Tsang and Williams and our original article.
language: eng
source:
identifier: ISSN: 0276-7783
fulltext: no_fulltext
issn:
  • 0276-7783
  • 2162-9730
url: Link


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descriptionTsang and Williams offer some good and provocative ideas in their critique o f our earlier article on generalizing and generalizability. In this essay we will advance some new ideas by building on those collected in both Tsang and Williams and our original article (Lee and Baskerville 2003). Because IS is a pluralist scientific discipline, one in which both qualitative and quantitative (and both interpretive and positivist) research approaches are valued, “generalize” is unlikely to be a viable term or concept if only one IS research paradigm may lay claim to it and excludes others from using it. Both papers agree on this point, but approach the problem differently. Where we originally generalized generalizability by offering new language, Tsang and Williams conceptualize generalizability by framing it more closely to its older, more statistically oriented form. We agree about the importance o f induction and about the classification or taxonomy of different types o f induction. We build further in this essay, advancing the ethical questions raised by generalization: A formulation ofjudgment calls that need to be made when generalizing a theory to a new setting. We further demonstrate how the process of generalizing may actually proceed, based on the common ground between Tsang and Williams and our original article.
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subjectAccountancy ; Classification ; Empirical statement ; Inductive reasoning ; Information storage and retrieval systems ; Information systems ; Information theory ; Issues and Opinions ; Judgment ; Moral judgment ; Observational research ; Reasoning ; Studies ; Technology
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abstractTsang and Williams offer some good and provocative ideas in their critique o f our earlier article on generalizing and generalizability. In this essay we will advance some new ideas by building on those collected in both Tsang and Williams and our original article (Lee and Baskerville 2003). Because IS is a pluralist scientific discipline, one in which both qualitative and quantitative (and both interpretive and positivist) research approaches are valued, “generalize” is unlikely to be a viable term or concept if only one IS research paradigm may lay claim to it and excludes others from using it. Both papers agree on this point, but approach the problem differently. Where we originally generalized generalizability by offering new language, Tsang and Williams conceptualize generalizability by framing it more closely to its older, more statistically oriented form. We agree about the importance o f induction and about the classification or taxonomy of different types o f induction. We build further in this essay, advancing the ethical questions raised by generalization: A formulation ofjudgment calls that need to be made when generalizing a theory to a new setting. We further demonstrate how the process of generalizing may actually proceed, based on the common ground between Tsang and Williams and our original article.
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