10 Measures or types of variables

This chapter is adapted from Curtin University UniSkills content and is used under a Creative Commons Attribution ShareAlike 4.0 International Licence.

Learning Objectives

By the end of this chapter, students must be able to:

  • Identify the different types of data/scales used for measurement
  • Understand the application of different scales in surveys

Measures or Types of Variables

Data and Variable Types

This chapter details some important concepts that will be referred to in subsequent pages, including what data and variables are, and how to distinguish between different types.

What is data?

The word data refers to observations and measurements which have been collected in some way, often through research.

Data that is recorded as numbers (and therefore measures quantities) is quantitative data, while data that is recorded as text (and therefore records qualities) is qualitative data. Quantitative data can be analysed using statistics, as can qualitative data that records qualities in terms of different categories (for example what hair colour someone has, what country someone was born in, what their marital status is, etc.), as opposed to data that records qualities in terms of thoughts, feelings and opinions.

It is the former two types of data that you will be working within this module, and shortly you will be introduced to some other terms that are typically used in statistics to describe data of this nature.


What is a variable?

Variables are the characteristics or attributes that you are observing, measuring and recording data for – some examples include height, weight, eye colour, dog breed, climate, electrical conductivity, customer service satisfaction and class attendance, just to name a few.

As the word suggests, the value of a variable varies from one subject (i.e. person, place or thing) to another. For example, the variable height could have a value of 170cm for one person, 163cm for another person and 154cm for another person; the variable climate could have a value of arid for one city, tropical for another city and the Mediterranean for another city; and the variable class attendance could have the value 17 for one class, 25 for another class and 32 for another class, etc.

Categorical and continuous data

Choosing the correct statistic or statistical test to analyse your data depends on the type of data, and hence type of variable(s), so it is very important to be able to distinguish between these. Most of the time you will simply need to classify your data (and hence variables) as either categorical or continuous, but each of these types can also be sub-classified. Definitions and sub-classifications for each are as follows:

Categorical data

is data that is grouped into categories, such as data for a ‘gender’ or ‘smoking status’ variable. Categorical data can be further classified as:

  • nominal when the categories do not have an order, such as for a ‘marital status’ variable (furthermore, if there are only two categories then the terms binary and/or dichotomous are sometimes used); or



  • ordinal when the categories do have an order, such as for a ‘best’ car, the ‘runners up’ car and the ‘third’ car in a list of Australia’s most favourite vehicles.


Continuous data

is data that is measured on a continuous numerical scale and which can take on a large number of possible values, such as data for a ‘weight’ or ‘distance’ variable. Continuous data can be further classified as:

  • the interval when it does not have an absolute zero, and negative numbers also have meaning, such as for a ‘temperature in degrees Celsius’ variable. In marketing, any variable which is measured on a Likert scale is seen as an interval variable. Here’s an example:

Example: On a scale of 1 to 5 (where 1 = extremely dissatisfied) and 5 = extremely satisfied), how would you rate the restaurant on the following criteria:

Cleanliness                                      1          2          3          4          5

Food presentation                         1          2          3          4          5

Food (taste)                                     1          2          3          4          5

Prompt service                              1          2          3          4          5

Courteous employees                1          2          3          4          5


Customers’ ratings on the above criteria can be averaged to arrive at a score for ‘satisfaction with restaurant’.


  • ratio when it does have an absolute zero, and negative numbers don’t have meaning, such as the ‘expenditure’ on groceries or ‘time spent’ in the kitchen



One final thing to note on this topic is that any continuous data can always be turned into categorical data, by simply creating categories out of it. Continuous data for a ‘weight’ variable could be turned into categorical data by creating categories of 50−59kg, 60−69kg, 70−79kg, etc., for example, and this can be useful if you want to analyse your continuous data using statistics and statistical tests designed for categorical data. You can’t go the other way around though and turn categorical data into continuous data, so if you have the choice then for maximum flexibility it is preferable to collect continuous data.




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