Re: imputing missing likert-type values

Posted by Rich Ulrich on Jul 11, 2015; 5:29pm
URL: http://spssx-discussion.165.s1.nabble.com/imputing-missing-likert-type-values-tp5730100p5730116.html

I almost always take the average of the items available for the
particular factor to represent the factor, especially when they are
Likert items or close to it. 

If item means are not homogeneous or lie at the extremes, then the
scale is not "Likert" or Likert-type.  On the other hand, even though it might
be wiser not to do so, they are usually (in my experience) treated as if they
were Likert items, and they are treated as if they were equal-interval and
normal.  That is simple and ordinary.  The newer approach that assumes
otherwise uses Item Response Theory, which has a logistic underpinning.
 
On the other hand, it seems uninformed to treat a designed, Likert scale as
if the items are categorical or (merely) ranks.  This "advice" seems to be a
hang-over from the 1950s when a few (non-statistician) psychologists started
regarding the so-called non-parametric statistics as magically, always valid.

NOTE on odd distributions.
For dichotomies, it is sometimes more meaningful to count number of items
endorsed, or number not-endorsed.  For instance there is a short Dementia
scale where the best Total score is 31, but a few items may be Missing
because the subject, for instance, is blind or immobile.  It would have been
wiser if the Scale had been normed as the count of deficiencies; which is
how I used it (that is, the Missing counted as Okay).   How you deal with
unusual cases has to be determined by the purpose of the study and the
substantial meaning of the answers. 

--
Rich Ulrich

> Date: Fri, 10 Jul 2015 08:00:22 -0700

> From: [hidden email]
> Subject: Re: imputing missing likert-type values
> To: [hidden email]
>
> what do people usually do when they handle the missing values of likert type
> scales?
> This is the first time I work on a survey data, and first time with likert
> type scales too.
> Can anyone share some opinions? Thanks.
>
>

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