From: GLB@UNB.CA (GLB0000)
Newsgroups: bit.listserv.edstat-l
Subject: Aggregating a 7-Point Scale
Date: 9 Nov 92 17:35:53 GMT
Reply-To: GLB0000 <GLB@UNB.CA>
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Comments:     <09NOV92.14702068.0055.MUSIC@UNB.CA>

> We have a survey which employs a 7-point scale: Very Satisfied, Satisfied,
> Somewhat Satisfied, Neutral, Somewhat Dissatisfied, Dissatisfied, Very
> Dissatisfied.
>
> When discussing the responses to individual questions we want to aggregate the
> scale a bit, for example,
>
> Very Satisfied and Satisfied                       - "Satisfied"
> Dissatisfied and Very Dissatisfied                 - "Dissatisfied"
> Somewhat Satisfied, Neutral, Somewhat Dissatisfied - "Lukewarm"
>
> Can anybody offer guidance on which is the better aggregation scheme to follow
> that given above, or that given below:
>
> Very Satisfied, Satisfied, and Somewhat Satisfied           - "Satisfied"
> Dissatisfied, Very Dissatisfied, and Somewhat Dissatisfied" - "Dissatisfied"
> Neutral                                                     - "Neutral"
>
While others on this list may disagree, I'd argue that the
answer to your question requires a knowledge of both certain statistical
issues and of the substantive topic involved in the question.
In other words, there is no simple solution that is "correct"
in all cases.

First and foremost, you want to collapse the data in a manner
that retains any important patterns in the distribution.

Second, from a statistical point of view, particularly if you intend to
do more than simply describe the univariate distribution of responses,
there are several (potentially contradictory) concerns.
If you intend to use the variable in bi-variate or multi-variate
tables, then having a roughly equal number of cases in each
category helps assure that there will be sufficient observations
within each cell of the table.  Alternatively, if you wish
to use certain other procedures (e.g. regression)
it is nice if the distribution looks "normal".  This lets you
satisfy the assumptions of the procedure.  Use of regression,
however, means you treat the data as interval rather than ordinal.
While the legitimacy of doing this
has long been questioned, there exists 1) a considerable literature
suggesting it is okay in many circumstances and 2) a sizable
number of researchers who engage in the practice routinely.
The relevant conceptual point is that, if you intend to do this,
then this intention should inform how you collapse your data.
In other words, based upon your substantive knowledge of the
subject matter is the unit distance of response between
"satisfied and lukewarm" or between "satisfied and uncertain"?

Even if you don't intend to use such a technique, the substantive
issue remains.  Essentially, in order to remain true to your
data, you need to keep your categories as homogeneous as possible.
Whether this suggests the "lukewarm" or the "uncertain" version
depends upon the substance of the issue involved, though
in most cases I'd lead toward lumping all the types of satisfied
responses together.

Third, there are methodological issues involved.  Your question
labels responses in the middle as "neutral".  While there is frequently
an inclination to equate this with Undecided (and research shows
that many respondants treat them as equivalent) you should be aware
that they are conceptually distinct.  Finally, there may exist
cultural patterns in response which become lost when you collapse
the data.  It is well known in Canada, for example, that French
Canadians are much more likely to respond in the extremes (i.e.,
either strongly agree or strongly disagree) than are English
speakers.

In short, there is no magic bullet.  You have to think both
about the technical issues involved and about the substance
of what you are attempting to represent.

Gary Bowden
glb@unb.ca
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU