Data represents any raw facts, figures, or information that may be gathered, measured, or analyzed. It could come in different forms: numbers, text, images, audio, or video. Generally, data is used in representing real-world phenomena or describing particular attributes of something, like the temperature of a city, the sales figures for a product, or the score of a sports game.
There are several types of data:
Quantitative Data (Numerical): Numerical data comprise those forms of data that are measurable or countable, such as age, income, or height.
Quantitative data is information expressed in numerical terms. It is data that can be counted, measured, and quantified. Quantitative data is the type most often used in statistical analysis for performing calculations, deriving insights, and making predictions. Quantitative data is objective and can be used for mathematical, statistical, or computational analysis.
Types of Quantitative Data:
1) Discrete Data:
Definition: Discrete data is made up of separated values or counts that can no longer be divided. These are usually whole numbers.
Examples:
Number of Teachers in a school: 15 teachers.
Number of trucks in a parking lot: 30 trucks.
Number of note books on a shelf: 60 note books.
Number of staff in an organization: 100 staff.
2) Continuous Data:
Definition: Continuous data may take any value within the given range and can be measured with precision. Normally, this is obtained through measurement and may have infinite values.
Examples:
Height of a person: 180.5 cm, 180.55 cm, etc.
Temperature in a city: 20.3°C, 20.35°C.
Weight of an object: 40.5 kg, 40.55 kg.
Time taken to complete a task: 10.5 seconds, 10.55 seconds.
Qualitative Data (Categorical): This type of data, describing qualities or characteristics, is usually non-numeric and can be a color, name, or label.
Qualitative data refers to non-numerical information, which describes attributes, qualities, or characteristics. It is the kind of data most often used to capture subjective experiences, opinions, and descriptions that cannot be easily quantified or measured. Rather than quantities or amounts, qualitative data is used to delve into concepts and find patterns or meanings behind the numbers.
Types of Qualitative Data:
1) Dichotomous:
Definition: Dicho-tomous qualitative data refers to a type of categorical data that has only two possible categories or outcomes. These categories are mutually exclusive; hence, an observation can only fall into one of the two groups. Dichotomous data is usually represented as "yes/no" or "true/false" questions with only two distinct options for each observation.
Examples:
Have you voted - yes / no
Did you pass your exam - yes / no
2) Polynomic :
Definition: Polynomic qualitative data refers to categorical data where the number of categories is greater than two. Unlike dichotomous data (that has only two categories, such as "yes/no" or "true/false"), polynomic data may have three or more categories. They are usually non-numerical but may be coded numerically for convenience in analyses.
Examples:
How would you like to rate the quality of food ? - Bad, Ok, Tasty
What is your blood group ? - A, B, AB, O
Exceptions : There are certain data which looks lie Quantitative but its actually Qualitative for example Phone number etc.
There are several types of data:
Quantitative Data (Numerical): Numerical data comprise those forms of data that are measurable or countable, such as age, income, or height.
Quantitative data is information expressed in numerical terms. It is data that can be counted, measured, and quantified. Quantitative data is the type most often used in statistical analysis for performing calculations, deriving insights, and making predictions. Quantitative data is objective and can be used for mathematical, statistical, or computational analysis.
Types of Quantitative Data:
1) Discrete Data:
Definition: Discrete data is made up of separated values or counts that can no longer be divided. These are usually whole numbers.
Examples:
Number of Teachers in a school: 15 teachers.
Number of trucks in a parking lot: 30 trucks.
Number of note books on a shelf: 60 note books.
Number of staff in an organization: 100 staff.
2) Continuous Data:
Definition: Continuous data may take any value within the given range and can be measured with precision. Normally, this is obtained through measurement and may have infinite values.
Examples:
Height of a person: 180.5 cm, 180.55 cm, etc.
Temperature in a city: 20.3°C, 20.35°C.
Weight of an object: 40.5 kg, 40.55 kg.
Time taken to complete a task: 10.5 seconds, 10.55 seconds.
Qualitative Data (Categorical): This type of data, describing qualities or characteristics, is usually non-numeric and can be a color, name, or label.
Qualitative data refers to non-numerical information, which describes attributes, qualities, or characteristics. It is the kind of data most often used to capture subjective experiences, opinions, and descriptions that cannot be easily quantified or measured. Rather than quantities or amounts, qualitative data is used to delve into concepts and find patterns or meanings behind the numbers.
Types of Qualitative Data:
1) Dichotomous:
Definition: Dicho-tomous qualitative data refers to a type of categorical data that has only two possible categories or outcomes. These categories are mutually exclusive; hence, an observation can only fall into one of the two groups. Dichotomous data is usually represented as "yes/no" or "true/false" questions with only two distinct options for each observation.
Examples:
Have you voted - yes / no
Did you pass your exam - yes / no
2) Polynomic :
Definition: Polynomic qualitative data refers to categorical data where the number of categories is greater than two. Unlike dichotomous data (that has only two categories, such as "yes/no" or "true/false"), polynomic data may have three or more categories. They are usually non-numerical but may be coded numerically for convenience in analyses.
Examples:
How would you like to rate the quality of food ? - Bad, Ok, Tasty
What is your blood group ? - A, B, AB, O
Exceptions : There are certain data which looks lie Quantitative but its actually Qualitative for example Phone number etc.
Last edited: