On 17 Mar 2001 19:54:27 -0800, [EMAIL PROTECTED] (Will Hopkins)
wrote:
> I just thought of a new justification doing the usual parametric analyses
> on the numbered levels of a Likert-scale variable. Numbering the levels
> is formally the same as ranking them, and a parametric analysis of a
> rank-transformed variable is a non-parametric analysis. If non-parametric
> analyses are OK, then so are parametric analyses of Likert-scale variables.
Good comment.
One thing that happened, in recent years, was that Conover,
et al., showed that you can to the t-test on Ranked data and
get a really good approximation of the "exact" p-level,
even when the Ns are quite small.
Further: Ranked data has theoretical problems with *ties* --
which is the chronic condition Likert-scale items. In fact, using the
t-test on Ranks sometimes gives a better p-value that what your
textbook recommends for "adjusting for ties."
Further again: In the cases where there are "odd" distributions,
in the several categories, you want to check to see what the
rank-tranformation assigns to categories as their effective "scores"
and then select between analyses. For my data, the 1...5
assigned scoring almost always looks better than the intervals
achieved by ranks.
Agresti has a detailed example of arbitrary scoring of categories
in his textbook, "Introduction to categorical data analysis."
>
> But... an important condition is that the sampling distribution of your
> outcome statistic must be normal. This topic came up on this list a few
> weeks ago. In summary, if the majority of your responses are stacked up on
> one or other extreme value of the Likert scale for one or more groups in
> the analysis, and if you have less than 10 observations in one or more of
> those groups, your confidence intervals or p values are untrustworthy. See
> http://newstats.org/modelsdetail.html#normal for more.
Good comment, too.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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