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Interpretation for scales of measurement linking with abstract algebra

Overview of attention for article published in Journal of Clinical Bioinformatics, June 2014
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Title
Interpretation for scales of measurement linking with abstract algebra
Published in
Journal of Clinical Bioinformatics, June 2014
DOI 10.1186/2043-9113-4-9
Pubmed ID
Authors

Jitsuki Sawamura, Shigeru Morishita, Jun Ishigooka

Abstract

THE STEVENS CLASSIFICATION OF LEVELS OF MEASUREMENT INVOLVES FOUR TYPES OF SCALE: "Nominal", "Ordinal", "Interval" and "Ratio". This classification has been used widely in medical fields and has accomplished an important role in composition and interpretation of scale. With this classification, levels of measurements appear organized and validated. However, a group theory-like systematization beckons as an alternative because of its logical consistency and unexceptional applicability in the natural sciences but which may offer great advantages in clinical medicine. According to this viewpoint, the Stevens classification is reformulated within an abstract algebra-like scheme; 'Abelian modulo additive group' for "Ordinal scale" accompanied with 'zero', 'Abelian additive group' for "Interval scale", and 'field' for "Ratio scale". Furthermore, a vector-like display arranges a mixture of schemes describing the assessment of patient states. With this vector-like notation, data-mining and data-set combination is possible on a higher abstract structure level based upon a hierarchical-cluster form. Using simple examples, we show that operations acting on the corresponding mixed schemes of this display allow for a sophisticated means of classifying, updating, monitoring, and prognosis, where better data mining/data usage and efficacy is expected.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Professor 1 14%
Student > Doctoral Student 1 14%
Unknown 3 43%
Readers by discipline Count As %
Social Sciences 2 29%
Business, Management and Accounting 1 14%
Mathematics 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2014.
All research outputs
#22,760,732
of 25,374,917 outputs
Outputs from Journal of Clinical Bioinformatics
#60
of 61 outputs
Outputs of similar age
#210,272
of 244,221 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#2
of 2 outputs
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So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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