This post will give you some advice about using SPSS to summarise data that were generated with a Likert scale. If you want to read up on Likert scales before you go on, you can find some information in this post.
Before we start
Why should you summarise Likert scale data
Elsewhere in this blog, I have written that a Likert scale might consist of several items that measure a similar underlying construct (a latent variable). For instance, if I want to measure people’s attitudes towards sweets, I might ask them to record what they think about the following statements:
|1. I like chocolate||Strongly Agree||Agree||Disagree||Strongly Disagree|
|2. I like cookies||Strongly Agree||Agree||Disagree||Strongly Disagree|
|3. Ι Iike whipped cream||Strongly Agree||Agree||Disagree||Strongly Disagree|
In order to interpret these data, we need to summarise the data in the scale. We can do this in two ways: adding the data or estimating the median. In this post, I will show you how to estimate the median, because this is slightly harder. The same steps can be modified to add up the data.
Using the same example as above, I need to create a new ‘super-variable’, which shows the mean of items (1), (2) and (3) for each respondent.
My assumptions about you
I assume that you will already know how to define variables and values, how to toggle between the numerical expression and verbal descriptor of the values (i.e., you can make SPSS show responses as “strongly agree/agree/disagree/strongly disagree” or as “1/2/3/4”), and how to key in data. I will also assume that you have already established that your scale is internally consistent, so I will focus only on the technical aspects of merging the variables.
Here’s how to merge the Likert items
Your starting point for summarising Likert scale data with SPSS will be a dataset similar to the one shown in Figure 1, below.
When you have created the dataset by typing your data into SPSS, and after you have tested for the internal consistency of the scale (use Cronbach’s α), it’s time to create a new variable.
Merging the variables
From the top menu bar in SPSS, select Transform -> Compute variable. You should now see the following dialogue box.
- Assign a name to the new variable (e.g., Sweets);
- Scroll down the Function Group, and select Statistical;
- From the functions that appear select the Median. [ΝΒ it is possible to select the mean, but I don’t recommend it]. At this point, the following formula should appear in the numerical expression box: Median ( , )
- Place the cursor in the brackets, select the variables you want to merge, and click on the arrow. Repeat with all the variables, separating them with comas.
- Click on OK.
Your new Likert scale
SPSS will automatically generate a new variable, which will appear at the end of your dataset. This will be in numerical form (1, 2, 3, …), but you can change it to a verbal descriptor for consistency (Figure 3). You can use this variable for descriptive statistics (e.g., estimate the central tendency and dispersion), cross-tabulations, correlations and so on…
Now wasn’t that very easy?
Frequently Asked Questions
Over time, a lot of people have asked questions about Likert scales in the comments section of this post. I have collected the most usual things people ask in this section.
There are decimal points in the median I calculated. Is that a problem?
If your median falls between two values, it will have a ‘half’ (e.g., 2.5, 4.5 etc.). This is normal. You can report the median as you see it.
Why you do not recommend grouping the Likert scales as means and you recommend using medians?
The data produced by Likert type items are, strictly speaking, ordinal data. That means that they can tell us how to rank responses (strongly agree is ‘more’ agreement than agree) , but they do not give us information about the distance between them (strongly agree is not twice as much agreement as agree). Think of the medals in the Olympics: they can tell you if an athlete came first, second or third, but you cannot use them to calculate average speed. The median is a cruder statistic than the mean, because it does not take into account the ‘distance’ or ‘weighting’ of responses. In this case though, it is the best statistic we can legitimately use because this ‘distance’ is unknown.
OK, I did what you said, but what should I do next with my study?
It’s hard to answer such a question without knowing more about what you’re trying to find out (your research question) and your data. This is the kind of question that your advisor or mentor will be better qualified to answer.
Where can I find out more information about all this?
There are many statistics manuals you could read, if you want to follow up on the information in this post. My personal favourite is Andy Field’s Discovering Statistics with SPSS. I have also written some more posts about quantitative research below,, which you might find useful:
If you use quantitative methods in your research project, you may want to read this first.
Let’s assume that you have prepared a questionnaire, where respondents had to select among responses ranging from “strongly agree” to “strongly disagree”. For convenience, you have probably followed the established practice of replacing these responses with numbers: “1” for “strongly disagree”, “2” for “agree” and so on. How do you go about analysing these data?
Many questionnaires use Likert items & scales to elicit information about language teaching and learning. In this post, I discuss how to use these instruments effectively, by looking into the difference between items and scales, and explaining how to analyse the data that they produce.
Before you go
If you landed on this page while preparing for one of your student projects, I wish you all the best with your work. There’s a range of social sharing buttons below in case you feel like sharing this information among fellow students who might also find it useful. Also feel free to ask any other questions you may have, using the contact form.