Purposive Selection and the Quality of Qualitative IS Research
2013
- 618Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage618
- Abstract Views361
- Downloads257
Article Description
As qualitative research has found broad acceptance within the IS community, the methodological discourse has turned its attention to questions concerning the quality of qualitative research mostly emphasizing the development of how-to guidelines for good practice. By contrast, criteria for the evaluation of qualitative research, although equally important, have not received equal attention. Drawing on literature from behavioral and social science, this paper discusses methodological concepts of evaluating qualitative research by focusing on techniques to purposefully select data for analysis. In particular, the technique of corpus construction will be introduced, which was specifically designed as an evaluation criterion for qualitative research. Adapted from linguistics, corpus construction offers an alternative that is functionally equivalent to statistical sampling techniques in terms of the quality of empirical research. Hence, the paper contributes the concept of purposive selection as an evaluation criterion to the methodological tool-box and terminology of IS research.
Bibliographic Details
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