I want to show you three techniques that can be particularly useful when
analyzing multiple response data in IBM SPSS Statistics. I call these "advanced"
techniques, not because they are especially difficult; rather, because they
are less-obvious solutions that you might not know about without some
advanced knowledge of SPSS.
The first technique is filling in missing values when you use survey
software to collect your data. This is a raw Excel version of a response set from
Qualtrics. This format is very similar to what you would get as output from a
SurveyMonkey survey, as well. This is what many students confront when they use
online survey software for a project. In this study, the ethnicity variable gave
respondents the option to "check all that apply". If they checked the item, it was
coded as 1, 2, 3, 4, etc. If they did not check the item, the values are missing.
This is "system missing" data. It is missing because the system did not
collect that data; however, we know that this missing data indicates that someone
really selected "No" instead of "Yes". We actually know what these missing data
SHOULD HAVE been. In other words, these data are not missing because certain
respondents were not asked the question; they are missing because the respondent
did not think this option applied to them. We can clean up this missing data
by inserting a 0 for all missing responses. When doing multiple responses,
replacing missing data is less important if all that you need are the frequencies
from your "check all that apply" responses, but replacing missing data can be very
important if you want to analyze the data using Crosstabs. Here I have
imported this raw Excel data into SPSS and done some minor data cleaning, mostly
for anonymity. Now I will replace all of those system missing data. Go to...
Transform -> Recode into Same Variables... Move all of the ethnicity variables that
have system missing values into the numeric
variables box and then click on Old and New Values... Under Old Value, select the
range of values: 1 through 6. These will all be recoded as a 1. Now click on
System-missing, to select all of the system missing values. Under New Value,
we will assign a value of 0. Click Continue and OK. This technique will
replace all of the missing values with 0's. 1 will be the counted value. So we
have 1's and 0's ready to use in our multiple response set. The second
technique is useful when you have more than one counted value that you want
included in your multiple response set. This is data from the 2016 General
Social Survey. For these items, people were asked about the reasons that they
attended an art exhibit and they could check all that applied to them. Look at
the item asking about the importance of wanting to experience high-quality art
in the decision to attend an art exhibit. The answer options are major reason,
minor reason, not a reason, and then all of the missing data codes. We want to
consider only whether a given reason was a major reason, a minor reason, or not a
reason, excluding all of the other responses. So we want answer options 1, 2.
& 3, ignoring all others. Go to Analyze -> Multiple Response -> Define Variable Set
Find all of the items about importance and then move them into the Variables in Set:
box. Instead of using dichotomies: yes or no, 1 or 0, we select Categories... and
our range was responses 1 through 3. We will name our multiple response set
"Attend", for reasons that they attended the art exhibit, and then click Close to
define it. Now go to Analyze -> Multiple Response -> Frequencies. Move the Attend
response set into the box Table(s) for: Click OK. In the output, we are given the
number of people who provide a response to each item, the percent of responses,
and the percent of cases, for major reason, minor reason, and no reason. We are
focusing on the responses of interest, keeping them separate, and we have
ignored all other responses. You might use this technique, for example, if you
wanted to consider whether people indicated strongly agree, agree, or
neutral, on a set of items, rather than considering each items separately. What
can we do, however, if we want to consider both major and minor reasons together.
The third technique I want to show you is what to do when you have two answers
that you want to consider together, instead of separately. For example, we
want to know whether a given reason was any kind of reason at all: major or minor.
We want to combine both major reason and minor reason into a single category and
compare it to all others. For this, we need to combine the data first and then
create the variable response set. We will keep using the same GSS 2016 data
set. Go to Transform -> Recode into Same Variables. (Honestly, a better technique
for this would be to recode into different variables, so that you can keep
both your original variable and the new one. I am recoding into the same
variable simply because of the huge number of variables in this data set and
I don't want to scroll all the way to the bottom to find the new variables
that I recode.) The importance here is to simply show you the technique. Move all
of the variables to be recoded into the Numeric Variables: box then
click on Old and New Values... Under Old Value, select "1" (major reason) and set
the new value to "1". (I always do this so that I am sure that I have accounted for all of
the answer options.) Select "2", which is minor reason, and set the new value to "1".
This combines both the major and minor reasons into a single category, coded as "1".
that represents that this was a reason of some kind. Depending on your
data set, you may choose to leave the other data as they are, or if you want to
set all of the other data to zero, (considering that the missing data mean
"not a reason") click on All other values: and set the new values to "0". As I said
earlier ,if you get the frequency table, the output will be exactly the same
regardless of what you do with all other values. The only values that SPSS
considers are the counted values. On the other hand, if you are going to use
crosstabs, setting the other values to 0 will give you a much better cross-
tabulation table. These three advanced techniques will give you even more
options when using multiple response data in SPSS.