Disadvantages of Categorical Data
There are some disadvantages to using categorical data, which are mentioned below:
Limited Statistical Analysis: Categorical data is limited to the kind of statistical analysis that can be performed on it. It does not have the same statistical properties as quantitative data, which means that numerical or statistical analysis cannot be performed on categorical data.
Loss of Detail: When continuous variables are categorized, a level of detail is lost. This can make it challenging to analyze the data and may result in a less accurate representation of the underlying patterns or relationships.
Low Sensitivity: Categorical data research is often low in sensitivity, with responses typically being either good/bad or yes/no. This can limit the ability to detect subtle differences or trends in the data.
Expensive and Time-Consuming: Categorical data requires larger samples, which can be more expensive and time-consuming to gather compared to quantitative data.
Potential for Irrelevant Data: When collecting categorical data, researchers may have to handle irrelevant data, which can add complexity to the data analysis process
Categorical Data
Categorical data classifies information into distinct groups or categories, lacking a specific numerical value. It refers to a form of information that can be stored and identified based on their names or labels. Categorical Data is a type of qualitative data that is easily measured numerically.
In this article, we will learn about, what is categorial data, types of categorical data, and some real-life examples.
Table of Content
- What is Categorial Data?
- Types of Categorial Data
- Difference Between Ordinal Data and Nominal Data
- Features of Categorical Data
- Examples of Categorical Data
- Analysis of Categorical Data
- What is Categorial Variable?
- Advantages of Categorical Data
- Disadvantages of Categorical Data
- Categorical and Numerical Data
- Application Of Categorial Data
- Challenges In Categorial Data
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