Qualitative Data (Categorical Data)

As the name suggest Qualitative Data tells the features of the data in the statistics. Qualitative Data is also called Categorical Data and its categories the data into various categories. Qualitative data includes data such as gender of people, their family name and others in sample of population data.

Qualitative data is further categorized into two categories that includes,

• Nominal Data
• Ordinal Data

Nominal Data

Nominal data is a type of data that consists of categories or names that cannot be ordered or ranked. Nominal data is often used to categorize observations into groups, and the groups are not comparable. In other words, nominal data has no inherent order or ranking. Examples of nominal data include gender (Male or female), race (White, Black, Asian), religion (Hinuduism, Christianity, Islam, Judaism), and blood type (A, B, AB, O).

Nominal data can be represented using frequency tables and bar charts, which display the number or proportion of observations in each category. For example, a frequency table for gender might show the number of males and females in a sample of people.

Nominal data is analyzed using non-parametric tests, which do not make any assumptions about the underlying distribution of the data. Common non-parametric tests for nominal data include Chi-Squared Tests and Fisher’s Exact Tests. These tests are used to compare the frequency or proportion of observations in different categories.

Ordinal Data

Ordinal data is a type of data that consists of categories that can be ordered or ranked. However, the distance between categories is not necessarily equal. Ordinal data is often used to measure subjective attributes or opinions, where there is a natural order to the responses. Examples of ordinal data include education level (Elementary, Middle, High School, College), job position (Manager, Supervisor, Employee), etc.

Ordinal data can be represented using bar charts, line charts. These displays show the order or ranking of the categories, but they do not imply that the distances between categories are equal.

Ordinal data is analyzed using non-parametric tests, which make no assumptions about the underlying distribution of the data. Common non-parametric tests for ordinal data include the Wilcoxon Signed-Rank test and Mann-Whitney U test.

Data is a simple record or collection of different numbers, characters, images, and others that are processed to form Information. In statistics, we have different types of data that are used to represent various information. In statistics, we analyze the data to obtain any meaningful information and thus categorizing data into different types is very important. Data types in statistics help us to make an informed decision about what type of process is used to analyze the data.

Here, in this article, we will learn about types of data in statistics in detail, examples, and others in detail. Before learning about data let’s first learn about Data.