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Introduction to types of attributes of data

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Source: World Bank data portal

Data is a collection of distinct pieces of information, which are recorded and structured in a manner that makes it easy for analysis. Data is usually generated when something is measured or recorded. When many such pieces of information are recorded consistently – they can be easily analyzed individually or in groups. Data can be seen as a raw material that can be refined to produce meaningful information.

Data is a collection of distinct pieces of information, which are recorded and structured in a manner that makes it easy for analysis. Data is usually generated when something is measured or recorded. When many such pieces of information are recorded consistently – they can be easily analyzed individually or in groups. Data can be seen as a raw material that can be refined to produce meaningful information.

For instance, data is generated by surveys (such as census data), when something is voted on (such as elections results data), when something is registered (such as birth records data), when something is purchased (such as sales records for an online store). Data is also generated by mobile devices, sensors, the internet, and satellites (such as GPS data) – and many other technologies.

In our daily lives, data is commonly found organised in tables. Contents of a single distinct table can be referred as a dataset. The analysis of a dataset can generate new knowledge and visual representations – which are valuable to make arguments and better decisions. Such as the change in the ratio between urban and rural dwellers over time (see image above).

Common Data Types 

Source: https://www.nytimes.com/2019/05/17/us/us-birthrate-decrease.html

In this section, talk about the different types of data. Take the child in the picture as an example. His birth is being recorded by the medical staff. The data that is being recorded could be about his/her first name, middle name or last name. It could be about gender, date of birth, place of birth, name of parents. Data about his physiology such as eye colour, hair colour, weight, height and so on could be recorded. All these things are data of different types.

The difference between data types

The two major categories of data are qualitative and quantitative data. 

Qualitative or categorical data is everything that refers to the quality of something: A description of hair and eye colours, names, and additional notes made by the medical staff are all qualitative data of the child above. 

Quantitative or numerical data is data that refers to a number. In the case of a baby, the age, height, weight etc are all examples of this.

 

Categorical data

Categorical data can be further divided in to nominal or ordinal data

Nominal data is defined as data that is used for naming or labeling variables, without any quantitative value. It is sometimes called “named” data – a meaning coined from the word nominal. Nominal data basically refers to descriptions or qualities of something or an event. For example, The image below shows a question from a list of questions. The answer to the question will produce a nominal data that describes the respondent’s experience with the said training  

Ordinal data is a type of categorical data with an order. The variables in ordinal data are listed in an ordered manner. It is, therefore, the same as nominal data, except that it’s ordered.

For example, very hot, hot, cold, very cold, warm are all nominal data when considered individually. But when placed on a scale and arranged in a given order (very hot, hot, warm, cold, very cold), they are regarded as ordinal data. 

Numerical data

However there are also other categories that you will most likely encounter:

Discrete data is numerical data that has gaps in it, usually measured in whole numbers. So if we record the age of a child in years, but not months, we have a dataset with discrete values 0, 1, 2, 3, 4 and so on.

Continuous data is numerical data with a continuous range: such as the weight of the child which can be any value (4.5 kg, 4.57 kg or 4.579 kg ). In continuous data, all values are possible with no gaps in between. 

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