Lecture 2 – Data Types

As visualisation designers, it is important to know what data types are and what can be done with each of them so that data can be understood appropriately. There are many ways to describe data measurements, but across the fourteen weeks of studying data visualisation, we will be primarily using levels of measurement known as, nominal, ordinal, interval and ratio.

Definition of Data Types

Nominal: Comes from the Latin word nomen and means, pertaining to names. It can be thought as, as being in a category.

Screenshot 2016-07-30 10.13.35
(Waterson, 2016)

It can also be counted and used to calculate per cents, as seen below.

Screenshot 2016-07-30 10.13.46
(Waterson, 2016)

Ordinal: Is based on order and this is primarily done with numbers which for example, can identify the level agreement to a certain issue such as one being ‘strongly disagree’ and 5 being ‘strongly agree’ as seen below. The numbers selected represent ordinal categories which change how the viewer interprets the end analysis.

Screenshot 2016-07-30 10.13.18
(Waterson, 2016)

Interval: Interval data is numeric, and you can do mathematical operations on it but it doesn’t have a meaningful zero point. That is, the value of zero doesn’t indicate the thing you are measuring.

Screenshot 2016-07-30 10.20.12

(Waterson, 2016)

Ratio: Ratio data is numeric, and a lot like interval data, except it does have a meaningful zero point. In ratio data the value of zero indicates an absence of what is being measured. Elements that could be counted as ratio data are height, weight, age and money.

Screenshot 2016-07-30 10.22.53
(Waterson, 2016)

Here is a clear example of how each of the data types can be used.

Screenshot 2016-08-01 13.13.06
(Waterson, 2016)

Here is an example of what data types can be measured (numerical) and what data types can be counted (categorical).

Screenshot 2016-07-30 10.24.05

(Waterson, 2016)

Qualitative and Quantitative Data

Lastly, the lecture briefly focused on qualitative and quantitative data. Qualitative refers to non-numeric data (description/information) while quantitative is typically numeric hence, quantifiable (numerical/information). They can be thought as ‘quality’ and ‘quantity’. The example below will help clarify this.

Screenshot 2016-07-30 10.24.48
(Waterson, 2016)

Reflection
This lecture was good to get an understanding of the levels of measurement, nominal, ordinal, interval and ratio. While this at first sounded quite complicated, I believe I will grasp a better understanding of these data types as the semester goes on. However, I learnt that it is important as visualisation designers, the types of data used need to be understood.

References

Waterson, S. (Speaker). 2016. Lecture Pod 02: Data Types [Vimeo video]. Western Sydney University.

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