Uses
Quantitative and qualitative data complement each other in helping us to better understand the labour market.
We can use
quantitative data to compare the exact size and extent of difference in two quantities. With quantitative data, we are able to compute summary measures of the variable, such as their
mean,
median, or
mode . This gives us a concise way to understand the variable being measured, such as the median age of workers in an industry.
As for ordinal qualitative data, we are able to determine the most common category (or the mode) as well as the category that contains the median observation (or the one that divides the bottom half of observations from the upper half).
For example, let’s look at the highest qualification attained of persons in Coconut Island’s resident labour force. In the chart below, qualifications to the right are ordered higher than those on the left. However, we are not able to quantify the extent of their difference. As such, the mean of this variable cannot be determined. Nevertheless, we are still able to derive the median and mode of this variable.
Coconut Island’s Resident Labour Force by Highest Qualification Attained
As the categories in nominal qualitative variables have no natural ordering and their extent of difference cannot be quantified, we will not be able to derive the median (which requires ordering) or mean of such variables. However, we can derive its mode, which refers to the category that contains the largest number of observations in the dataset.
An example of a nominal variable is the industry for job vacancies on Coconut Island. For this case, it is not possible or logical for us to derive the mean or the median industry where the job vacancies come from, but we can derive the mode which refers to the industry with the largest number of job vacancies.
Job Vacancies by Industry on Coconut Island
The averages that can be computed for each type of data are summarised in the table below.
Averages That Can Be Computed with Each Type of Data
