Scales of Measurement: A Demystification of the Ordinal Scale

The ordinal scale of measurement is not understood by many researchers, especially in the social and business fraternities. The thinking that coding values of ordinal scale variables convert data from being qualitative into being quantitative is held by these researchers. A sample of randomly selected articles on factors affecting students’ academic performance is studied to establish how ordinal level variables are analyzed. Results show that the greater part of researchers do not know that, although it is correct that where there is quantity there is number, the converse is incorrect. Parametric techniques dominate in the analysis of ordinal data. Scenarios are forwarded for the purpose of sending home the message of differentiating when number is quantity and when it is not. Techniques that are designed for the analysis of ordinal data are then shared.

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Publication Details

Journal
African Journal of Law Political Research and Administration
Published
2025-03-25
DOI
https://doi.org/10.52589/ajmss-v0suhl6w
Primary Topic
School Choice and Performance
Type
article
Field-Weighted Citation Impact
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article

Scales of Measurement: A Demystification of the Ordinal Scale

Peter Chimwanda, Edwin Rupi
African Journal of Law Political Research and Administration
School Choice and Performance
article

Scales of Measurement: A Demystification of the Ordinal Scale

Peter Chimwanda, Edwin Rupi
article en

Abstract

The ordinal scale of measurement is not understood by many researchers, especially in the social and business fraternities. The thinking that coding values of ordinal scale variables convert data from being qualitative into being quantitative is held by these researchers. A sample of randomly selected articles on factors affecting students’ academic performance is studied to establish how ordinal level variables are analyzed. Results show that the greater part of researchers do not know that, although it is correct that where there is quantity there is number, the converse is incorrect. Parametric techniques dominate in the analysis of ordinal data. Scenarios are forwarded for the purpose of sending home the message of differentiating when number is quantity and when it is not. Techniques that are designed for the analysis of ordinal data are then shared.

African Journal of Law Political Research and AdministrationVol. 8(1)
Openalex Percentile: Top 95%
School Choice and Performance
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