Multiple response questions are very common in research. When working with multiple responses in SPSS, there are specialized procedures that will help you better summarize a combination of variables resulting from multiple response questions. In this tutorial, you will learn multiple response analysis using frequencies, crosstabulations and charts in SPSS.
Multiple response questions like these ones are very common in research.
So instead of allowing a respondent to pick just one option, with a multiple response, the respondent can choose multiple options from a list provided.
When entering the data in software like SPSS or Excel, this is the way you set it all up
Each of the items in your list options becomes a variable on its own.
In many cases, it is recommended to use 0 to mean the option was not selected and 1 to mean that the option was selected.
0 – no it wasn’t selected, 1 it was selected
If you used data collection software like KoBo Toolbox, or Open data Kit, your multiple response questions will already be exported with each option being represented by a variable and zeros and ones being used for not selected and selected.
Now let’s look at how you would summarize such variables in SPSS
The first option for summarizing multiple response questions is to run frequency tables for all the variables. Let’s try that
So you go to Analyze…
I will grab all the variables except the ID into the Variables box on the right
The output is OK. At least from here we can tell that for example 70.5% have farming as one of their household sources of income
For non-farm business, that’s 51.5% and so on
The problem with this however is that there are way too many tables
In most cases, we are only interested in the frequency of those who chose each of the options we have. That means we are only interested in counting the ones and not the zeros
This is where you need to use the multiple responses option in SPSS
First, you need to define each multiple response set
Let’s minimize the output
You will find that there are several places for defining multiple response sets in SPSS – for reasons best known to the developers at IBM
And unfortunately, you need all of them
Let’s start with going to Analyze
Define variable set
We will drag all the variables in our multiple response set to the right hand side
Then, we will specify which value we are going to be counting here where it says counted value.
Remember that we are only interested in counting the number of people who actually selected the response. And when they did so, we recorded 1
So we will type 1 as the counted value
Then, we need to give a name to the variable that will hold our multiple responses.
I will say IncomeSources, without a space – remember this is a variable name
Ten we can specify the label for our variable
I will say Household sources of income…
Then click Add
If you have more multiple response sets, you can continue in the same manner, adding the variables to the variables in set box and the specifying the value you are counting, then finally the variable name and label
Let’s close this
So now we have our variable, we can use it to create one table of frequencies. But we can only do that using the Multiple response option on the menu
So click Analyze again
Drag the multiple response variable to the right hand side…
This looks much neater
The percent value is expressed out of the total N at the bottom.
The total N here is not the number of cases in the data set, but rather the total number of times all the options were selected.
Since these are multiple responses, the Total N is bigger than the actual number of cases.
So the Percent should be construed as a frequency relative to the total of the number of times all the options were selected.
While the percent of cases here is for each of the option out of the total number of cases we have in the data set.
It’s exactly the frequencies we had earlier on when we used the Frequency procedure to summarize each of the variables individually.
So the Percent should tell you which option was selected the most out of all the options, but Percent of cases should now tell you the real representation of how many times the option was selected by the total number of cases.
You may have noticed that we can also produce a crosstabulation using out multiple response variable.
Let’s try that
Go to Analyze…
Let’s drag the household sources of income variables here, to the rows…
Then let’s use the gender variable in the columns
Notice we have question marks inside the gender brackets.
That’s because we need to specify the values of gender
So click define ranges
We are using 0 for female and 1 for male so we will type those values here
It’s best to turn on column percentages so we can see the relative frequencies by gender
To do that, click options
Under cell percentages, click column
Finally, click OK
Nice, so now each of the options has been cross tabulated by gender, without having to worry about those who did not select the option
Now what happens when we want to produce some charts with the multiple response variable
Let’s go to Graphs
Well, as you can see from the variables list here, the multiple response variable we just defined from the Analyze menu is nowhere to be seen
That variable does not appear here.
The one that will appear here is the one you will have to define using the Data menu
So lets close this for now
Go to Data…
Define multiple response sets…
The way we define the multiple response set here is exactly the same
So drag our multiple response variables from farming to remittances into the variables in set box
In the counted value box, we type 1
Then we go ahead and define the set name here.
Once again, I will call it IncomeSources, again without a space
This time, no label. Click Add
Minimize the output
Now this time when you go to graphs
Perfect, we have our multiple response variable right here.
Let’s try a bar chart with it
Drag and drop the first bar chart type[e from the gallery
Drag income sources and drop it onto the x axis
Let’s change the statistic using the element properties
So here, instead of count, let’s change to percentage
Now we have a bar chart using our multiple response variable
That’s it about summarizing multiple response variable
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