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Cited by 1,551

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Generalizing Generalizability in Information Systems Research

Lee, Allen S., Baskerville, Richard L.·2003·Information Systems Research
DOI 10.1287/isre.14.3.221.16560
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Abstract

Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.

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Metadata

Authors
Lee, A. S., & Baskerville, R. L.
Venue
Information Systems Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2003-09
Volume
14
Issue
3
Pages
221-243
ISSN
  • 1047-7047 (ISSN-L)
  • 1526-5536
DOI
10.1287/isre.14.3.221.16560
OpenAlex
W2118717649
Cited by
1,551
References
9
FWCI
47.29
Type
journal article
Language
en
Open access
closed

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.