Knowing the different types of statistical data is necessary for conducting exploratory data analysis, as certain statistical measurements can be used only for specific types of data. Statistical data definition
You also need to know what kind of statistical data exists to choose the correct display method. Think of data types as a way to categorize different variables.
Methods for obtaining statistical data
There are different methods by which statistical data can be obtained. Here are the main ones:
- Published information: Statistical data can be obtained through the collection of original data. The person or organization that is in charge of obtaining them is the primary source, while the person that compiles the information is the secondary source.
- Design of an experiment: The experimentation is carried out with a strict control of the treatment that is offered to the study participants.
- Conducting a survey : This is another method of obtaining statistical data. The survey is designed, data is collected, edited, coded, and tabulated for analysis.
- Conduct an observational study : The researcher observes the behavior of the individuals participating in the study, while they are in their natural environment. Statistical data definition
This process can be done in different ways to aid in the decision-making process.
Statistical data types
Next, we have for you the following section with the different types of statistical data and their classification:
Categorical data, also known as qualitative data , represents characteristics such as gender, language, etc. of a person. They can also take numerical values, for example: 1 for women and 0 for men. Keep in mind that these numbers have no mathematical meaning.
The types of categorical statistical data are classified into: Statistical data definition
Nominal data : Other types of statistical data are those that have nominal values that represent discrete units and are used to label variables that do not have a quantitative value.
These data do not have an order, although the order of their values changes, their meaning does not change.
Ordinal data : Ordinal data represent discrete and ordered units. Therefore, it is almost the same as the nominal data, except that their order is important.
Ordinal scales are generally used to measure non-numerical characteristics such as happiness, customer satisfaction, etc.
These types of statistical data are also known as quantitative data , and refer to a measure or count. They are classified as follows: Statistical data definition
Discrete data: Statistical data are discrete when their values are distinct and separate. That is, when the data can only take certain values.
This type of data cannot be measured, but it can be counted. They basically represent information that can be classified.
Continuous data: Continuous data represents measurements and therefore their values. they cannot be counted but they can be measured. In turn, these are classified as follows:
- Interval data: Interval data represents ordered units that have the same difference. Therefore, we speak of interval data when we have a variable that contains numerical values that are ordered and where we know the exact differences between the values.
The problem with interval value data is that we can add and subtract, but we cannot multiply, divide, or calculate ratios. Because there is no true zero, many descriptive and inferential statistics cannot be applied. Statistical data definition
- Relationship data : They are also ordered units that have the same difference. The relationship data is the same as the interval values, except that they have an absolute zero.
Importance of knowing the types of statistical data
Remember that the top-level categories are quantitative or qualitative data. From there they can be grouped into “discrete” or “continuous” data.
Statistical data types are an important concept that allows you to find the correct method to perform an accurate analysis.
In short, nominal variables are used to “name” or label a series of values. Ordinal scales provide good information on the order of choices, as in a customer satisfaction survey .
Interval scales give us the order of the values + the ability to quantify the difference between each one.
Finally, the relationship scales give us the ultimate order, the interval values, plus the ability to calculate relationships, since a “true zero” can be defined.