Qualitative Data vs. Quantitative Data
What is Qualitative Data?
The qualitative data is the type of data that can be observed but can’t be measured. The object classification in this type of data is done with regards to the attributes and properties. It is the approximate data analysis, which can’t be computed or that accurate. The one analyzing this type of data needs to have the prior knowledge about the types of objects and their characteristics. If any of the laypeople tries to analyze it, the things can get worse as the qualitative data is descriptive in nature, and when it comes to analyzing it requires some expert approach.During the data analyzation process, the objects are placed into different categories after they are distinguished by the physical attributes and the properties of the object. The data interpretation is purely based on the observation and the properties, which can be observed but can’t express using the numbers. In a more compact way, we can say that is the type of data interpretation in which language and words are used for the arrangement and analyzing the data. Texture, taste, feel, smell is few of the observable properties that are used in this type of data interpretation. Other than observations, the qualitative data is based on the interviews or evaluations. The qualitative data is also called the categorical data as the information is classified by category and not by the numbers.
What is Quantitative Data?
The quantitative data is the one which is represented using the numbers, numerical values, and the measurement units. The data is classified into different groups by the quantity, amount, or range. In other words, we can say that it is the number game on which the different arithmetic operations can also apply, and the validity can also be checked for it. The quantitative data is the method in which data is numerically counted or expressed. Table charts, graphs, histograms are even used here for the purpose of expressions. With using the above mentioned, the data evaluation for one gets quite easy as it covers the all in the very concise way. The measuring of length, volume, area, and temperature are few of the prominent examples of this type of data analyzation. The numbers are required in this case along with the measuring units. Experiments, surveys, and observations are conducted in this type of data evaluation.