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Ordinal Level of Measurement

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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Related Experiment Video

Updated: Jan 24, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Using ordinal outcomes to construct and select biomarker combinations for single-level prediction.

Allison Meisner¹, Chirag R Parikh^2,3, Kathleen F Kerr⁴

¹1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA.

Diagnostic and Prognostic Research

|May 17, 2019

Summary

Sophisticated methods for biomarker combinations improve prediction of ordinal outcomes, like disease severity. Utilizing the outcome

Keywords:

Biomarker Combinations Ordinal

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Area of Science:

Biostatistics
Biomarker Discovery
Clinical Prediction Models

Background:

Biomarker studies often analyze ordinal outcomes (e.g., no, mild, severe disease).
Predicting a specific outcome level is frequently a key clinical objective.

Purpose of the Study:

To evaluate if advanced methods outperform simple binary logistic regression for ordinal outcomes.
To develop and assess an algorithm for selecting optimal biomarker combinations using ordinal outcome information.

Main Methods:

Compared sophisticated modeling approaches against binary logistic regression after outcome dichotomization.
Proposed and applied an algorithm leveraging ordinal outcome data for biomarker combination selection.
Utilized data from a study on acute kidney injury severity after cardiac surgery.

Main Results:

Advanced modeling techniques demonstrated advantages over the simple binary approach in certain scenarios.
The proposed algorithm reduced selection bias and enhanced the performance of selected biomarker combinations.
Improved predictive performance was observed when utilizing the ordinal nature of the outcome.

Conclusions:

Methods incorporating the ordinal nature of outcomes can lead to superior biomarker combinations.
Leveraging ordinal information in biomarker construction and selection offers significant potential for improved clinical utility.