types-of-data-in-statistics

Introduction

Data are the facts that are collected by direct observation, experiments, and surveys. Data are raw information from which statistics are prepared. Data are collected, stored, analyzed, and interpreted, and some meaningful conclusion is drawn from them.

Difference between primary data and secondary data

The difference between primary data and secondary data are as follows:

Comparison basis

Primary data

Secondary data

Definition

Those data are recorded by the person himself.

Those data that are previously recorded by another person.

Source

Surveys, experiments, observations, interviews, etc.

Internal records, journals, websites, articles, etc.

Time for collection

Large

Less

Cost

Expensive

Economical

Availability

Raw

Refined

Accuracy

High

Relatively low

Types of data

The classification of data is shown in the figure below:

data in statistics

A. Qualitative or Categorical data
Those data that cannot be measured, counted, or expressed in numerical value are called qualitative or categorical data. Qualitative data are collected by questionnaires, interviews, etc. These are generally text that describes the quality or property.
Examples:
a. Gender of a particular community.
b. A series of instructions

Qualitative data are further classified into two categories:
1. Nominal
The data that cannot be ordered are called nominal data. These data can be classified into mutually exclusive categories.
For example
Gender can be classified into male and female.  Another example can be a mode of land transportation. Mode of land transportation can be classified into a bus, train, bicycle, motorbike, car, etc. Here gender and mode of land transportation are nominal data. These data can be classified into different categories but assigning them a rank or ordering them is not meaningful. We cannot say males are superior to females or vice versa which is meaningless.

2. Ordinal
The data that can be ordered are called ordinal data.
For example
A post of a person in an organization. A post in an organization can be classified into the Board of directors, department head, managers, workers, peon,  etc. We can rank these categories where the Board of directors gets the highest rank and the peon gets the lowest rank.

B. Quantitative data
Those data that can be counted, measured, and expressed in numerical value are called quantitative data. These data are associated with a unique numerical value. Quantitative data answer the questions like how much? how many? when? etc.
Examples
a. Census
b. Annual income
c. Amount of rainfall over a year

Quantitative data can be further classified into two categories:
1. Continuous data
Those data that can take any value within any finite or infinite interval are called continuous data. Continuous data can be measured.
Examples:
a. Height of a child
b. Length of a paper
c. Speed of an airplane

2. Discrete data
Those data that can take only certain values are called discrete data. Discrete data cannot be measured but can be counted.
Examples:
a. Number of books on table
b. The number of members in the family
c. The outcome of a rolling dice

If you want to know how categorical data can be converted into a numerical value, follow this link.

Conclusion

Data are the facts that are recorded for analysis, interpretation, etc purposes. Qualitative data cannot take numerical values while quantitative data are associated with a unique numerical value. Nominal values cannot be ordered while ordinal values are given order. Discrete data can be counted while continuous data can be measured.

References

https://www.mygreatlearning.com/blog/types-of-data/

Happy Learning 🙂

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