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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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Nominal Level of Measurement

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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Selectivity and Related Measures for nth-Order Data.

N J Messick¹, J H Kalivas, P M Lang

¹Department of Chemistry, Idaho State University, Pocatello, Idaho 83209.

Analytical Chemistry

|May 31, 2011

Summary

This study introduces new mathematical tools to create figures of merit for nth-order analytical instruments. These tools enable better assessment of selectivity, net analyte signal, and sensitivity for complex analytical data.

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Area of Science:

Analytical Chemistry
Chemometrics
Instrumental Analysis

Background:

Figures of merit are crucial for evaluating analytical instrument suitability.
Existing figures of merit are limited to first-order instruments and data.
A gap exists in evaluating higher-order (second-order and nth-order) instruments and data.

Purpose of the Study:

To develop practical mathematical tools for creating figures of merit for nth-order instrumentation.
To define and develop measures for selectivity, net analyte signal, and sensitivity for nth-order data.
To address the lack of established figures of merit for complex analytical data.

Main Methods:

Development of mathematical tools for nth-order figures of merit.
Focus on a local selectivity measure for second-order instrumentation.
Validation using simulated and real second-order data from GC-FTIR and LC-PDA.

Main Results:

Successful development of practical mathematical tools for nth-order figures of merit.
Demonstration of a local selectivity measure for second-order data.
Validation of the proposed measures using chromatographic and spectroscopic data.

Conclusions:

The developed mathematical tools provide practical means to establish figures of merit for nth-order instruments.
These new measures enhance the evaluation of analytical methods dealing with complex, multi-variable data.
The study lays the groundwork for broader application of higher-order figures of merit in analytical science.