Using Quantitative and Qualitative Data
At the end of a quantitative or qualitative research project, you need to make sure that you
detail your findings in a way that adds value to your decision-making or problem-solving
process. Here, we outline some methods for gathering both forms of data.
Options for using quantitative data
This is data comprising raw figures that you’ll need to interpret. The following are some of the
options available to you:
1. Numerical count
The most simple, and often most effective, of all options. If you need only to establish the number
of people who replied to a survey, don’t complicate matters. Just remember to qualify your figures
so that they make sense to whoever will be using them. For example: ‘297 out of 350 employees
replied to the staff survey.’
2. Frequencies
This is the logical next step after a numerical count, where you break down some of the figures. A
good example of a frequency would be: ‘Of all survey respondents confirming that they were happy
with the company benefits package, 29 were over 40 years old.’
3. Range
This is a simple measure of variability, used to demonstrate how wide ranging the answers given
were. An example would be: ‘The age of survey respondents ranged from 18 to 52.’
4. Rankings
Rankings are slightly unusual in quantitative data in the sense that they are not an actual
measurement. They simply indicate where a value stands in relation to other values or where the
value stands in relation to the total. For example: ‘Based on the scores received, the company
pension scheme was ranked the most popular part of the benefits package.’
5. Percentages
A common and well-used statistic, which expresses information as a proportion of the whole: ‘87%
of company employees make use of the staff restaurant.’ Percentages are useful because they are
easy to interpret – stating that 87% of employees use the restaurant has more weight and impact
than saying 348 out of 400. They are also a good way of showing relationships and comparisons:
‘The figure 87% shows an 11% increase on last year.’
Turning your quantitative data into a percentage is fairly straightforward, but might require the use
of a calculator:
You receive 297 responses to a staff questionnaire, 162 of which are from part-time employees.
Ref:
http://member.goodpractice.net/Leadership-Foundation-KB/resources/personal-skills/handling-information-and-data/using-quantitative-and-qualitative-data.gp
You want to show this as a percentage. To work this out, first perform the following calculation:
162 / 297 = 0.54545455
If you want your final percentage to be a round figure, you need to round down this calculation to
two decimal points. In this case it would 0.55, as the third decimal point figure is 5. If the third figure
is between 5 and 9, you round up. If it’s between 0 and 4, you round down.
The next stage is to take this 0.55 and turn it into an actual percentage. This is done using the
following calculation:
0.55 x 100 = 55
So, 55% of the survey respondents are part-time.
6. Measures of central tendency
These are used to characterise what is typical for a group, and to identify central characteristics.
There are three common measures that are used:
Mean
The mean is the average, and is often used in reporting data.
If all 297 participants of the above survey stated how old they were and you wanted to establish
their average age, you would, first of all, need to add all the ages together – we’ll say this totals
11,622. It’s now a straightforward calculation to work out the average age:
11,622 / 297 = 39.1313131
Using the rounding up or down methodology highlighted above in our percentage example, the
average age of the survey respondents is 39.
Mode
This is the most commonly occurring answer or value. For example, if personal assistants were
asked as part of a survey to rate, on a scale of 1 to 5, the quality of their printing facilities, and most
rated them a 4, then 4 would be the modal answer.
If you wanted to manually check the modal answer for every question on the survey, you could
create a table adopting the following structure to help you keep a running total of answers as you
work through each questionnaire received:
QUESTIO
N
Question
1
Total
Question
2
Total
1
2
SCORE
3
2
3
4
2
4
5
2
5
(modal)
1
5
(modal)
2
0
Ref:
http://member.goodpractice.net/Leadership-Foundation-KB/resources/personal-skills/handling-information-and-data/using-quantitative-and-qualitative-data.gp
Certain software packages (such as a spreadsheet) could be utilised to perform this function for
you, but there are times when using a program would be more cumbersome and slower than
working it out manually.
Median
The median is the middle answer, representing the midpoint where half of the cases fall below and
half come above the value. It is useful if you want to establish the midpoint value, or want to
arrange lower or upper groupings.
In the following situation, you have a group of 10 students who have sat an exam, and want
establish the median result in order to group the students. To calculate the median, first arrange
your data end to end – arrange their percentage marks from worst to best, and establish the
midpoint mark:
32% 46% 49% 51% 53% 59% 66% 70% 73% 77%
In this case two marks make up the midpoint, so we need to take an average of the two:
53 + 59 = 112 / 2 = 56
56%, then, is the median figure.
Is it more important to ascertain what is the average, the most common or the midpoint? That
depends upon your data and its purpose. To ensure you have well-rounded information, it is often
better to have all three figures.
Options for using qualitative data
As qualitative data is often used to describe, explain or characterise, it’s not quite as
straightforward deciding how to use it, or how to present your findings.
To give one example, you may have conducted a questionnaire with 50 people, and now have a
collection of comments, opinions and suggestions. Unlike a quantitative survey where the figures
will tell their own story, this scenario relies on a degree of subjectivity from the researcher – it’s up
to you to make sense of the findings and structure them in a way that is useful.
There are numerous ways to do this, the easiest being to detail each question, then list every
comment afterwards. However, this is only practical if you are working with a small number of
questionnaires.
Instead, try to structure your data so that it adds value to your decision-making or problem-solving
process. In this instance, your data could be quotes, observations, samples of text, etc. There are
various formats this can take[1]:
1. Natural
This is where the information is presented in the order it was formulated. So, if your data was taken
from an observational session, you would structure it sequentially to represent the natural flow of
Ref:
http://member.goodpractice.net/Leadership-Foundation-KB/resources/personal-skills/handling-information-and-data/using-quantitative-and-qualitative-data.gp
the subject being observed.
2. Most simple to most complex
Start with the simplest answer or example you have, and then gradually work your way through to
the most complex. Building your data up in this way should help aid the understanding of any party
using your findings.
3. Quantitative-informed
Using this method, your data should be presented according to strategies most commonly found in
quantitative analysis, such as frequencies or ranges. An example of this would be categorising the
responses of those who are over 25 years old separately from those who are under this age.
4. Theory-guided
With the theory-guided method, the data arrangement is governed by the researcher’s own
theories. An example would be if your research is forming the basis of a report, and you are
working up to a conclusion, your findings would be presented in a manner that supports this. This
should be done in logical steps that build up to your recommendations or specific points.
5. Major to minor
Quite simply, where you present the most important findings first, with the least important last.
6. ‘No particular order’ order
Data is arranged with no particular pattern in mind. Take care when using this method – it can often
look like you’ve given no thought to the data at hand.
Use your data wisely
Whichever research approach you’ve chosen, be it quantitative, qualitative or both, remember its
purpose is to help you to:
 establish facts
 draw conclusions
 make recommendations
By using the methods we’ve outlined in this article astutely, you should find that they add real value
to any decision-making or problem-solving process.
Related Items
Related Resources
 Qualitative or Quantitative Data?
 Handling Information and Data
 Understanding Information Sources
Ref:
http://member.goodpractice.net/Leadership-Foundation-KB/resources/personal-skills/handling-information-and-data/using-quantitative-and-qualitative-data.gp