In the research and statistics, we have to use some data measurement scales. Nominal and ordinal data have become the best scales for data measurement. By using these scales, we can classify the categorical data. To classify these two important data types, we have to focus on the non-quantitative data. It means that we have to classify the data without numbers. That’s why these two data types provide us string of data. Anyhow, they provide two different levels of data measurement. While working in the data field, you will have to understand these levels of data measurement.
What is Nominal Data?
The data that we use to name or label the values without using any quantitative value is the nominal data. In some cases, we are also calling it ‘Named Data’. While extracting data from the variables, we have to follow an order. In the case of nominal data, we don’t need to follow this order. It means that we don’t need to arrange the data from lowest to highest values or highest to lowest values.
What is Ordinal Data?
A type of categorical data in which we have to follow an order is the ordinal data. While presenting this kind of data, we have to write the variables in the following order. These variables are the numbers. These numbers indicate the order of the list. Here, you should understand that we can’t measure or determine these numbers mathematically. Anyhow, we have to merely assign these numbers based on the opinions.
Key Differences between Nominal and Ordinal Data:
Definitions:
The first main difference between nominal and ordinal data is in their definitions. Nominal data belong to the group of the non-parametric variables. On the other hand, ordinal data belong to the group of the non-parametric ordered variables. No doubt, these two data types belong to the non-parametric group. Anyhow, we have to place the ordinal data in the following order. In the case of nominal data, we can’t follow any kind of order.
Data Characteristics:
When we compare the characteristics of these two data types, we also find the difference between nominal and ordinal data. In the case of ordinal data, we have to follow a specific set of characteristics. In the case of nominal data, we can’t follow this specific set of characteristics. For the evaluation of these two data types, we can’t use mean and standard variation. It is due to the categorical nature of these two data types. Sometimes, we can use parametric statistics to evaluate the ordinal data. We have to do it by following the methods that are very close to the mean and standard deviation.
Examples:
No doubt, if nominal and ordinal data are two different data types, they have different examples. For example, if you will have to find the characteristics of a group of people, you can see their hair colour, gender and race in the nominal data. To gather the data, you will have to arrange them in ‘First’, ‘Second’ and ‘Third’ categories. Therefore, nouns are examples of the nominal data. On the other hand, the levels of the order are examples of ordinal data.
Tests:
Recommended by a masters dissertation help, to gather and arrange the data by following nominal and ordinal data types, we have to conduct some tests. The McNemar test and Fisher’s Exact Test are the best tests that we carry out for the nominal data. On the other hand, Wilcoxon signed-rank test and Friedman 2-Way ANOVA are the best tests to carry out for the ordinal data. Anyhow, there are differences between these tests. You will have to select the best test types based on the nature of the data.
Data Analysis:
After gathering the data, we have to analyze it to get the results. We have to follow two different analysis methods for nominal and ordinal data. While analyzing nominal data, we have to group the variables into categories. After that, we have to calculate the mode or percentage of the data. On the other hand, we can easily analyze the ordinal data by using mode and median methods. Sometimes, we can also measure this data by following the parametric statistics.
Collection Techniques:
No doubt, we have to follow some collection techniques to gather the data. In the case of nominal and ordinal data, we have to follow different data collection techniques. If we have to gather the nominal data, we have to follow open-ended questions and close-open ended questions to collect the data. On the other hand, if we want to collect ordinal data, we have to use interval scale and art scale type techniques. Anyhow, either you are collecting ordinal data or nominal data; you will have to use a single questionnaire.
Quantitative Value:
Based on the quantitative value, we can also observe the difference between nominal and ordinal data. The nature of the nominal data is categorical only. On the other hand, ordinal data has both categorical and quantitative natures. That’s why we have to assign a quantitative value to the ordinal data. Instead of providing this quantitative value to the ordinal data, we can’t perform any arithmetic operation.
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Uses:
We can use nominal and ordinal data for different purposes. For example, we can use ordinal data to investigate the views and opinions of the people on a specific issue. On the other hand, we can use nominal data for research purposes. Its reason is that it can provide specific data of a person. For example, if you want to gather data on a restaurant, these two data types will provide different data sets. The nominal data will provide the name, address and gender of the customers. The ordinal data can provide feedback to the customers about the services.
Conclusion:
If you will have to deal with the statistical data, you should know nominal and ordinal data types. This information will provide help on how to use different data types. If a statistician has this information, he can make the proper decisions after analyzing the data. The first step to understand the difference between these two data types is to know their respective definitions. After that, you should also know some other differences between these two data types. In these differences, there come data characteristics, data types, variable categories and uses etc.
