You are taking a survey that asks you questions about how much you like your
job, but in the midst of those questions is an item that asks you about how much
you dislike your job. It is the opposite of those other questions. That is called
a reverse-worded question, and in IBM SPSS Statistics software, that kind of
question needs to be reverse-scored before you can use it. We are going to
dive deeper into how to reverse score items in SPSS.
Why do we use reverse-worded questions? Test developers know that if we ask
questions that are all positive, we can get what is called a "halo effect."
The halo effect occurs when survey takers consistently rate items at the extreme
high end of the scale, like this evaluation of Dr. Jeffrey Tobin's
presentation about Universal Learning. The consistently high scores - which
really reflect the overall satisfaction with the presentation - inflate the
estimation of how you actually feel about any particular item, making each
one less useful for determining how much people actually think this presentation
will apply to their job. Now, the halo effect feels great if you're the
presenter, but it's problematic for the researcher. You might as well just use
one item: "This presentation was GREAT, right?" because every item is rated
exactly the same as every other item. There is no variability between items.
So to keep people on their toes, several items will be reverse-worded. We might
ask how much you agree with the statement "I feel a sense of pride in
doing my job" and then later ask you how much you agree with the statement,
"I sometimes feel my job is meaningless." Now we expect the people who strongly agree
that they feel a sense of pride in their job would strongly disagree that their
job is meaningless. All of the items are coded initially as 6 = strongly
agree and 1 = strongly disagree, but for the reverse worded items we want
to reverse the score so that 1 = strongly agree, and 6 = strongly disagree
Here's how we do that in SPSS.
For this example, we are going to use a subset of data from the Job Satisfaction
Survey created by Dr. Paul Spector, from the University of South Florida. This is
a widely-used measure of job satisfaction that comprises nine facet
subscales. Now, each facet is based on four items - so 36 items total - and the
theory is that these nine facets are the construct of "job satisfaction", and
therefore your total score on all of these facets represents your overall job
satisfaction. Each of the items is scored on a Likert scale from one to six in
which high scores on the scale represent job satisfaction and low
scores on the scale represent job dissatisfaction; however, each of these
facets includes negatively-worded items. So for example, the Satisfaction with Pay
subscale includes an item that says "I feel unappreciated by the organization
when I think about how much they pay me." Now, this item is the opposite of
"satisfaction with pay," but it can still be used in the scale if we reverse score it.
Here is what Dr. Spector says about items like this: "a score of 6
representing strongest agreement with a negatively worded item is considered
equivalent to a score of 1 representing strongest disagreement on a positively
worded item - so, if you strongly agree with a negative item it's the same as if
you strongly disagree with a positive item - allowing them to be combined
meaningfully. We are going to focus on just two of the subscales: satisfaction
with pay and satisfaction with the nature of work. In other words, do you
have a sense of personal satisfaction from the job that you do. Is your work
meaningful? And of course, you could feel that your
work is meaningful, but still also feel that you are underpaid.
So these facets are independent of one another. You can score high on one and
low on the other. When we look at the scoring rubric for these scales, we see
that there are two negatively-worded items in the "satisfaction with pay"
subscale - indicated in orange - and one negatively worded item in the
satisfaction with the "nature of the job" subscale. I am using a simplified data
set called "JobSatReverse.sav" with 429 cases, based on actual responses.
The first way to reverse score a variable is to use the Recode into Different
Variables function. Now this method works best for small or moderate numbers of
variables. It allows you to easily add labels and it preserves the original
variables. So from the drop-down menu choose Transform -> Recode into Different
Variables. Right-click and display variable names. Now move the three
reversed items into the box: items 8, 10, and 19. Each variable must be
renamed because we are recoding into different - or new - variables. The name of
the new variable should be the same as the original name, followed by a small
letter R, for "reversed". This will show you at a glance that you are looking at a
reversed version of the original variable. Do this for each item.
Although optional, I recommend that each variable also have a variable label.
The most effective variable labels are the text of the actual item from the survey,
so for example, item 8 says "I sometimes
feel my job is meaningless." That item text is going to become my variable
label, and I follow each label with another small letter R. Adding that
letter R will save you immense frustration later, because you will never
have to wonder if the data set that you are looking at has already been reversed
scored or not. Trust me on this; you will thank me later.
Now that the variable names and labels have been created, we will reverse score
the item. Click on Old and New Values. Our items were scored on a scale of 1 to 6.
Each old value of 1 will become 6, number 2 becomes 5. After each change, be sure to
click on the Add button, or you will receive an error message telling you to
do so. Keep going until you have 6 =1 Well, this raises a question: when your
scale has an odd number of response options, then the middle score - in this
case a 3 - will stay the same. What should you do when you recode?
I recommend always including the middle score in your recoding scheme. It ensures
that you do not accidentally miss a score and I've had problems with scores
being dropped when I did not include that 3 = 3. Now that all of our
items are reversed, click Continue and then OK. The new and different variables
are added to the bottom of the variable list. I prefer to replace the original
items with our new reversed scored variables, and then I'll move the
originals to the bottom. To further clean up this data set, I would recommend not
deleting the original variables, but hiding them using Variable Sets. See my
video on using Variable Sets for how do that.
If you do not need to keep the original variables, you can do essentially the
same thing using the Recode into Same Variables function. For reverse scoring,
you would start by renaming each variable, adding a lowercase R to the
ones that will be reversed, then you should add the "- R" to each label.
If you do this, however, you lose the original variable, so make sure that this
is what you REALLY want to do. After that, the process is the same as Recoding into
Different Variables. For each value, recode it into the corresponding new value, and
when you are done, click Continue and OK.
The second way to reverse score a variable in SPSS is to use a Compute
Variable function. With this technique, you have to recode one variable at a
time, but you can still add variable labels as you go. We are going to create
a formula, and to do this, we need to know the number of response items. Remember
that all of the items are coded on a Likert scale as 6 = strongly agree
and 1 = strongly disagree. There are 6 response items, so now add 1. 6 + 1 = 7
We will use the formula 7 - X to reverse each variable. Let me show
you how. From the drop-down menu choose Transform -> Compute Variable. Name the new
variable "JSS_8r". For the formula, type "7 - JSS_8"
or you can double-click to move the variable into this box. Click on type and
label and add the label "I sometimes feel my job is meaningless - R"
Click Continue and OK. The new variables are created in the data set.
The third way to reverse score a variable is to use syntax. Now this can
actually be faster when you are reverse scoring a large number of variables
because you can use a lot of cut-and- paste. So for each variable, start with
the command COMPUTE followed by the name of the new variable which will be the
name of the original variable plus a lowercase R, indicating that it has been
reversed. The formula to compute this variable is 7 - JSS_8, which is the
original score on the variable. Now then, add the variable labels. This is the name
of the new variable JSS_8r, followed by the actual item text from the survey.
It's in single quotes (') although double quotes (") will work, and
then EXECUTE, always followed by a period.
Using the syntax for all variables, I Select All and then Run. The new
variables are created in the data set. Using syntax is handy when you will be
working with various data sets that use the same survey and naming convention.