Using Quantitative and Qualitative Data

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. 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 characterize 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:

QUESTION

SCORE

1

2

3

4

5

Question 1

Total

2

3

2

5 (modal)

1

Question 2

Total

4

2

5 (modal)

2

0

Certain software packages (such as a spreadsheet) could be utilized 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 characterize, 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 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 categorizing 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.