Elsewhere in this blog, I have written that a Likert scale might consist of several overlapping items. For instance, if I want to measure subjects’ attitudes towards sweets, I might ask them to record how they feel about the following statements:
|1. I like chocolate.|
|2. I like cookies|
|3. Ι Iike whipped cream|
In order to interpret these data, we need to summarise the data in the scale. The safest way to do this is by estimating the median value of all the items. 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.
In the paragraphs that follow, I will show how to do this, using SPSS. 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 the scale is internally consistent, so I will focus only on the technical aspects of merging the variables.
Your starting point will be a dataset similar to Figure 1 below.
When you have typed in your data, and 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, 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.
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?
Featured Image by Michael Kwan [CC BY-NC-ND]