Categorical and quantitative are two types of data used in research and analysis. There is a marked difference between these types of data and the knowledge of that is very important for a basic understanding of research and even for general knowledge.
Each type of data has its own strengths and use. Some studies use categorical data because it does justice to the objectives of the study while others prefer quantitative data because it is able to meet the requirements well. Understanding the major differences can help select the best type of data for the study ad get accurate results.
Categorical data, also known as qualitative data is data which is grouped or classified together. For example, categories of gender are male and female. The values of the variables are labelled as categories. So in essence, categorical data is composed of groups of similar nature. On the other hand, quantitative data uses numbers to reflect measurements. Measuring the numbers of male and female participants is an example of quantitative data.
Categorical data is plotted using bar and pie charts; this data is best represented through these tools. Quantitative data is commonly represented with scatter plots and line graphs.
Categorical data can be analyzed using qualitative means and descriptions. Quantitative data can be analyzed by performing mathematical operations like average, mean, regression on the data. Since categorical data is qualitative, having no measurements, no such operations can be performed on it.
It is hard to compare the results of two categorical data sets because of the lack of measurements. For example you cannot compare the two car categories of Toyota and Suzuki. However, you can compare them on their features and performance. For the quantitative data, it is very easy to compare two data sets because you have numbers to work with. The heights of two boys can easily be compared because the numbers are very simple to work with.