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CHI-WEB  February 2002, Week 1

CHI-WEB February 2002, Week 1

Subject:

Summary: Standardized scaled tests, question development

From:

"Hillan, Julie G" <[log in to unmask]>

Reply-To:

Hillan, Julie G

Date:

Wed, 6 Feb 2002 13:59:42 -0600

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (205 lines)

Thanks to those who offered their (thoughtfully written) feedback. I received multiple requests for a summarized response, so I am posting my results to the list (perhaps it will be a good archive resource, too). Below are some good Web resources that I found, and responses from the list.

My original question is at the end of this e-mail.

After doing some research, I found two very informative sites about developing scaled questions.
See:

William M. Trochim (Cornell University)
Research Methods Knowledge Base
Home: http://trochim.human.cornell.edu/kb/
Likert Scaling page:
http://trochim.human.cornell.edu/kb/scallik.htm

Jurek Kirakowski
Human Factors Research Group, Cork, Ireland
Questionnaires in Usability Engineering: A List of Frequently Asked Questions (3rd Ed.)
http://www.ucc.ie/hfrg/resources/qfaq1.html
BTW-Jurek contacted me because someone forwarded him my initial question. "Thanks" to Jurek and the intermediary!
*****
There are good reasons having to do with psychometrics, but your adamant
programmer will more likely understand if it's couched in sampling theory.
Tell the programmer that humans (unlike computers), are Non-Deterministic,
i.e., they produce varying output in response to the same repeated input.
Theoretically there is a single "true" response they should make, but at any
one time their actual response will vary, deviating around the "true"
response to some degree, due to unpredictable biases and error variation.
The more times the question is repeated the closer the central tendency of
the responses will approximate the theoretical "true" response.  Each item
in the test measures the same thing but with some error;  in a well-selected
set of items the errors will cancel each other out.  This is a version of
the statistical phenomenon known as "regression toward the mean."
*****
The first question is really whether or not they are the same
question.  'Easy to recover from mistakes' and 'I make few mistakes'
are not the same question, for example.  Someone might look at the
surface similarity and think they were.

Second thing, are you combining some questions in to factors?  I.e.,
all the ones related to ease of learning, are you combining them in
some way?  Some high-end tests (more like personality tests than
usability ones) do this.  A simple minded way to do this is to take a
mean of all the questions that are 'the same' and substitute that as
a factor score.
*****
When researchers develop a test questionnaire, for example a personality test
go a usability questionnaire it is common to use many items in the
pilot-testing, and then do a Factor Analysis to identify factors that
"loads" on the different items. After doing this you can choose the items
that correlate highest with the factors (there are a set of other criterias
to). It is common practice to use a set of items on each factor. If you use
some similar questions on each factor you can measure the  reliability
(cronbachs Alfa) of your scale, and find out if there are a real pattern in
the users answers. It is true that you get better results when you use more
items on the same scale. You reduce the chances of user errors and the mean
of two items measuring the same thing is always better than only one item.
*****
It is a common way to measure validity, specifically construct validity
(does the question really measure what you think it is measuring?).  If you
ask a Likert scale question at the beginning of the survey, say "I like to
be able to arrange the icons on my desktop" then later on ask another,
similar one, something like: "Being able to arrange the items on my desktop
is useful to me" and the answers on the first question correlate with the
answers on the second, you have construct validity.  Obviously it is very
tricky to write these types of questions, I am sure some could find
argument with the pair I wrote above (liking to be able to do something is
not the same as finding it useful).
*****
There are two things are essential to any type of rating scale.  Reliability
and Validity.

Simply stated, reliability is a measure of consistency.  How well the same
scale gives the same rating under the same conditions.  Validity is a
measure of accuracy, how precisely it measures what it is that it's supposed
to be measuring.  You can be reliable but not valid, however you can not be
valid without being reliable.

Think of it this way.  You have a piece of wood that is 10 inches long, and
you want to make a ruler.  However, when you mark it off, you accidentally
mark 9 inches instead of 10, so each inch is really 1.1 (close enough)
inches.  You will now consistently measure everything inaccurately.

With respect to reliability and validity, the following point is extremely
important.  If you are using a canned, standardized scale, you will want to
be sure that _what you want to measure_ is the exact same thing that _the
scale was designed to measure_.  Else it all falls apart.  Sometimes it's
not as simple as 'the scale was designed for satisfaction and I want a
rating of satisfaction'.  You'll find that a lot of satisfaction scales come
out of the HR world and tend to look at things like job satisfaction.  The
providers of any canned test should be able to provide you with measures of
their reliability and validity.   If they don't or won't, find another
scale.

If you're developing your own standardized scale, I would advise using
caution.  Developing Likert scales is not as simple as putting five or seven
numbers with anchors on each end. True standardized scale development
requires multiple rounds of testing, analysis, revision, etc. using
correlations, coefficient alphas, SME reviews, and more really fun toys.

Along those lines, rephrased questions are a help with internal consistency,
the reliability of the scale.  However, if you're using a short scale, you
may want to ensure that your reliability is baked into the scale beforehand
instead of doing it on the fly.  With a short test, your users are going to
quickly tire of answering the same questions and the overall value added is
debatable.

I highly recommend a great little book (and it really is little) called
"Scale Development: Theory and Applications" by Robert F. DeVellis (Applied
Social Research Methods Series, Volume 26, Sage Publications, London).

It goes over scale development on a high level, question development and
formatting, question types (there's lots of types of likerts) and phrasing
(like double barreling).  Cost me $20 back in grad school.

This book will really help with those one off type scales (not standardized)
that we all like to use at the end of a test to measure something.

*****
You've probably heard this a few times by now, but the reasons I've
most often seen cited are that particular wordings/orders of presentation might
prejudice participants towards particular answers.

*****
I often have to explain this to developers myself.

In general, I use factor analysis in my projects when possible.  The reason
that I give is that some questions have very specific interpretation by an
individual.  There is no way to know ahead of time which questions are going
to have that association, so it is important to ask questions that:

 a. Have been tested with rigor
 b. Relate to a general concept.

If we were to create a test that ensured that we knew every person was using
the same measure, it would take a very long time.   Instead, we ask them
about the same Factor a couple different ways.  This helps to manage bias
and to normalize the data across all participants.

In addition, this helps to ensure that the benchmarking has some rigor
behind it so that if future versions of the same tool are evaluated we have
reasonable confidence that the measures can be compared.

You also might want to review some of the USE and QUIS documentation for
support.

*****
It is done to assure reliability. If the person answers these questions the
same way, then you can assure that that is how the person truly feels and
it was not just random. It is done on a lot of personality tests and the
Myers Brigg test etc. and that is the basic reason. Good luck with it.

*****
One of the best sources I can point you to is:

Oppenheim, A. N. (1992). Questionnaire design, interviewing and attitude
measurement (2 ed.). London: Pinter.

The author talks at length about the development of Likert questionnaires,
their advantages, and their pitfalls. The issue you are describing is that
of reliability.

Essentially, any questionnaire is only as good as it is reliable...or in
other words...as it is able to produce consistent results if the same person
were to complete it multiple times. The more related items you have in your
questionnaire, the greater the odds are that you will get a consistent (i.e.
reliable) measure of the individuals' 'true score' for that dimension. If
you only have a few questions in your questionnaire, you run the risk that
misinterpretations or extraneous factors associated with each item may cause
individuals to respond differently from one time to the next.

Imagine the extreme example of this.... What if our questionnaire asked only
one question..."How satisfied are you with the system?"  It is quite easy to
imagine that someone might answer this a certain way one day and a different
way the next due to any number of influencing factors, many of which may not
even be related to their experience with the system (e.g. general mood, a
recent conversation or news article regarding satisfaction, etc.). If on the
other hand, our questionnaire, included multiple questions around this
construct, the odds are improved that we will obtain the individual's 'true
score' for their satisfaction.

Having said that, some short questionnaires have shown to be highly reliable
(e.g The After-Scenario Questionnaire developed by Lewis at IBM has
demonstrated a high reliability of over .90 in multiple studies).
 *****
Original question:

Hi CHI-Webbers.

I am using a standardized Likert-scale satisfaction test to get some beta test metrics. One of the programmers involved in the project is adamant that some of the questions in the test are just different ways of asking the same thing (over and over). I explained to him that that is often done on tests of this sort, to gain a more accurate picture of (a) whether the initial answer given was true, and (b) the degree of un/satisfaction.

He was not satisfied with that answer.

My appeal to this knowledgeable, professional group of people is, why, exactly is this done? As a test-taker, I know this is pretty standard. I have never been a Likert scale
test-developer.

Please respond to me directly, and I will happily summarize on a request basis, (or to the list if I get many requests for the feedback).

Thanks!

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November 1998, Week 1
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October 1998, Week 1
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September 1998, Week 1
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July 1998, Week 1
June 1998, Week 4
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April 1998, Week 5
April 1998, Week 4
April 1998, Week 3
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April 1998, Week 1
March 1998, Week 5
March 1998, Week 4
March 1998, Week 3
March 1998, Week 2
March 1998, Week 1
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February 1998, Week 1
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January 1998, Week 1
December 1997, Week 4
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December 1997, Week 1
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September 1997, Week 5
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September 1997, Week 3
September 1997, Week 1
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August 1997, Week 1
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June 1997, Week 5
June 1997, Week 4
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June 1997, Week 1
May 1997, Week 5
May 1997, Week 4
May 1997, Week 3
April 1997, Week 5
April 1997, Week 4
April 1997, Week 3
April 1997, Week 2
April 1997, Week 1
March 1997, Week 4
March 1997, Week 3
March 1997, Week 2
March 1997, Week 1
February 1997, Week 4
February 1997, Week 3
February 1997, Week 2
February 1997, Week 1
January 1997, Week 5

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