What can you do with ordinal data? Or more to the point, what shouldn’t you do with ordinal data?
First of all, let’s look at what ordinal data is.
It is usual in statistics and other sciences to classify types of data in a number of ways. In 1946, Stanley Smith Stevens suggested a theory of levels of measurement, in which all measurements are classified into four categories, Nominal, Ordinal, Interval and Ratio. This categorisation is used extensively, and I have a popular video explaining them. (Though I group Interval and Ratio together as there is not much difference in their behaviour for most statistical analysis.)
Nominal is pretty straight-forward. This category includes any data that is put into groups, in which there is no inherent order. Examples of nominal data are country of origin, sex, type of cake, or sport. Similarly it is pretty easy to explain interval/ratio data. It is something that is measured, by length, weight, time (duration), cost and similar. These two categorisations can also be given as qualitative and quantitative, or non-parametric and parametric.
But then we come to ordinal level of measurement. This is used to describe data that has a sense of order, but for which we cannot be sure that the distances between the consecutive values are equal. For example, level of qualification has a sense of order
- A postgraduate degree is higher than
- a Bachelor’s degree,which is higher than
- a high-school qualification, which is higher
- than no qualification.
There are four steps on the scale, and it is clear that there is a logical sense of order. However, we cannot sensibly say that the difference between no qualification and a high-school qualification is equivalent to the difference between the high-school qualification and a bachelor’s degree, even though both of those are represented by one step up the scale.
Another example of ordinal level of measurement is used extensively in psychological, educational and marketing research, known as a Likert scale. (Though I believe the correct term is actually Likert item – and according to Wikipedia, the pronunciation should be Lick it, not Like it, as I have used for some decades!). A statement is given, and the response is given as a value, often from 1 to 5, showing agreement to the statement. Often the words “Strongly agree, agree, neutral, disagree, strongly disagree” are used. There is clearly an order in the five possible responses. Sometimes a seven point scale is used, and sometimes the “neutral” response is eliminated in an attempt to force the respondent to commit one way or the other.
The question at the start of this post has an ordinal response, which could be perceived as indicating how quantitative the respondent believes ordinal data to be.
What prompted this post was a question from Nancy under the YouTube video above, asking:
“Dr Nic could you please clarify which kinds of statistical techniques can be applied to ordinal data (e.g. Likert-scale). Is it true that only non-parametric statistics are possible to apply?”
As shown in the video, there are the purists, who are adamant that ordinal data is qualitative. There is no way that a mean should ever be calculated for ordinal, data, and the most mathematical thing you can do with it is find the median. At the other pole are the practical types, who happily calculate means for any ordinal data, without any concern for the meaning (no pun intended.)
So the answer to Nancy would depend on what school of thought you belong to.
Here’s what I think:
All ordinal data is not the same. There is a continuum of “ordinality” if you like.
There are some instances of ordinal data which are pretty much nominal, with a little bit of order thrown in. These should be distinguished from nominal data, only in that they should always be graphed as a bar chart (rather than a pie-chart)* because there is inherent order. The mode is probably the only sensible summary value other than frequencies. In the examples above, I would say that “level of qualification” is only barely ordinal. I would not support calculating a mean for the level of qualification. It is clear that the gaps are not equal, and additionally any non-integer result would have doubtful interpretation.
Then there are other instances of ordinal data for which it is reasonable to treat it as interval data and calculate the mean and median. It might even be supportable to use it in a correlation or regression. This should always be done with caution, and an awareness that the intervals are not equal.
Here is an example for which I believe it is acceptable to use the mean of an ordinal scale. At the beginning and the end of a university statistics course, the class of 200 students is asked the following question: How useful do you think a knowledge of statistics is will be to you in your future career? Very useful, useful, not useful.
Now this is not even a very good Likert question, as the positive and negative elements are not balanced. There are only three choices. There is no evidence that the gaps between the elements are equal. However if we score the elements as 3,2 and 1, respectively and find that the mean for the 200 students is 1.5 before the course, and 2.5 after the course, I would say that there is meaning in what we are reporting. There are specific tests to use for this – and we could also look at how many students changed their minds positively or negatively. But even without the specific test, we are treating this ordinal data as something more than qualitative. What also strengthens the evidence for doing this is that the test is performed on the same students, who will probably perceive the scale in the same way each time, making the comparison more valid.
So what I’m saying is that it is wrong to make a blanket statement that ordinal data can or can’t be treated like interval data. It depends on meaning and number of elements in the scale.
What do we teach?
And again the answer is that it depends! For my classes in business statistics I told them that it depends. If you are teaching a mathematical statistics class, then a more hard line approach is justified. However, at the same time as saying, “you should never calculate the mean of ordinal data”, it would be worthwhile to point out that it is done all the time! Similarly if you teach that it is okay to find the mean of some ordinal data, I would also point out that there are issues with regard to interpretation and mathematical correctness.
Foot note on Pie charts
*Yes, I too eschew pie-charts, but for two or three categories of nominal data, where there are marked differences in frequency, if you really insist, I guess you could possibly use them, so long as they are not 3D and definitely not exploding. But even then, a barchart is better. – perhaps a post for another day, but so many have done this.