Nominal Scale

The data gathered after every survey requires to be grouped based on the characteristics. This helps the researchers to assess the analyzed data against the unanalyzed data.

There are two categories of assessing the nominal data. These are Matched Samples and Unmatched Samples.

The Matched Sample:

 In this category, the data with similar characteristics are paired together. The aim is to match each responder of the samples who share the same characteristics except for the one which is to be assessed. 

This helps in receiving better statistics by controlling the other unwanted variables of the data. 

For example, you can gather data on your employees who have contacted covid-19 by controlling the “viral flu” category after matching the list with the number of employees. 

The Matched category includes two types of tests.

• McNemar Test: A distribution-free test
• Cochran’s Q Test: Non-parametric test between a set of 3 or more groups.

The Unmatched Sample:

The category involves random pairs chosen for the purpose of analysis. Unlike, matched category this is an independent sample.

For example, when you want to find out the effects of hair oil and then you select 100 participants irrespective of their health condition.

The unmatched category includes two tests

• Fisher’s Exact Test: The test helps find out if the proportion of one nominal variable is different among the values of another nominal variable.
• Chi-Square Test:  This test helps check if the variables are different in each data.

 Nominal Scale can be utilized via online survey tools to collect data for the official purposes as wells as for surveys on a large scale.