Qualitative Data in Statistics
Qualitative Data uses variables to represent labels or characteristics of entities or objects, such as movie genres or travel methods. The labels cannot be represented in numerical form, and their numerical values may not hold any significance. Qualitative data is also known as categorical data it is expressed through indicators and deals with perceptions.
Qualitative data cannot be averaged, and aggregate methods like mean or average do not hold for non-numerical data. Qualitative data can be grouped based on categories, and it is useful in determining the frequency of traits or characteristics. For instance, the color of hair can be categorized into three main colors, being, black-brown or blonde. It deals with perceptions. Qualitative data is useful in determining the particular frequency of traits or characteristics.
Qualitative Data
Qualitative data is defined as data that approximates and characterizes. Qualitative data can be observed and recorded. In the field of analysis, the terms “qualitative data” and “quantitative data” are used frequently. Quantitative and Qualitative are the two sides of the coin named “Data in Statistics” but as many people are familiar with quantitative data (i.e., numerical data of various sorts), qualitative data is often less understood. Understanding the qualitative data is essential for researchers, analysts, decision-makers, or anyone who wants to gain deep insights into people’s behaviors, attitudes, and experiences.
Qualitative data represents information and concepts that are not quantified numerically. They are typically acquired through sources like interviews, focus groups, personal diaries, lab notebooks, maps, photographs, and other observational or printed materials.
In this article, we have tried to explain Qualitative data with different approaches to its analysis, and also learn about the advantages and disadvantages of Qualitative Data.
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