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Re: st: RE: statalist-digest V4 #4588 - was Graphing


From   David Hoaglin <dchoaglin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: statalist-digest V4 #4588 - was Graphing
Date   Tue, 24 Jul 2012 08:22:37 -0400

Allan,

I resisted the temptation to mention a trellis arrangement, in part
because the bars to be compared would not have a common baseline.

Stacked bars usually fail because the bars are stacked.  I was
referring to empirical evidence, rather than authority.  Bill
Cleveland and colleagues conducted studies of people's ability to make
accurate comparisons in various graphical-perception tasks.  They
arrived at the following ordering, from most accurate to least
accurate (Cleveland 1985, Table 4.3):
1. Position along a common scale
2. Position along identical, nonaligned scales
3. Length
4. Angle --- Slope
5. Area
6. Volume
7. Color hue --- Color saturation --- Density .
In a stacked bar chart, the comparisons are among lengths (unless some
variable is encoded in them, the widths carry no information).  Why
not move up in the hierarchy when one can?

I agree that it is important to distinguish data that are ranks from
data that are grades.

Also, as often, some analysis is likely to be helpful in understanding
the patterns in the data and deciding what to present.

David Hoaglin

William S. Cleveland. The Elements of Graphing Data. Wadsworth
Advanced Books and Software, 1985.

W. S. Cleveland and R. McGill. Graphical perception: theory,
experimentation, and application to the development of graphical
methods.  Journal of the American Statistical Association 1984;
79:531-554.

W. S. Cleveland and R. McGill. Graphical perception and graphical
methods for analyzing scientific data.  Science 1985; 229:828-833.

On Tue, Jul 24, 2012 at 6:22 AM, Allan Reese (Cefas)
<allan.reese@cefas.co.uk> wrote:
> The answers offered to Aminu may be helpful but they appear to ignore
> the contradiction in the question as posed:
> [Aminu] I have a qualitative data where 8 diseases ranked (1
> (most-important) to 5 (least important)) based on perception - 37
> subjects were interviewed so 37 records in the dataset.
>
> It appears the diseases were not *ranked* but *graded*, an important
> distinction.  If ranked, only one disease can be first for each person,
> and the ranks would run 1-8.  If graded, could one person think all
> eight had the same importance?  Before offering code to draw specific
> graphs, it is necessary to know the intended use of the graph (analysis
> or presentation); if the latter, what message is it intended to convey?
> For example, a slide to make the point "everyone thinks this disease is
> important, but this one is considered trivial" might well use eight
> stacked bars and vivid colours.
>
>
> [David Hoaglin ] "A key message is that stacked bars are generally a bad
> idea.  You would do better with a little histogram for each disease (5
> bars, each sitting on the horizontal axis) and no numbers on top.
>
> "This approach has the advantage that the 8 bars for each rank, though
> not adjacent, have a common baseline."
>
> [AR] 8 histograms side by side may be less easy to compare than 8
> stacked vertically in a trellis. This is like comparing "profiles" in
> correspondence analysis.  Rather than take authoritarian advice, Aminu
> might try both and see which conveys the intended message.  This may be
> culturally dependent (relating to direction of reading) and I think
> Aminu in is Nigeria.
>
> [Nick Cox] My bottom line is that stacking is often chosen, but rarely
> optimal. One disadvantage of stacking is that it imposes dependence on a
> key or legend.
>
> [AR] I'll agree with Nick that random choices from a "graphics gallery"
> are often confused and confusing. Stacked bars often fail because the
> author didn't think about the order of stacking - Excel stacks values in
> alphabetic order of labels.  Stata makes it easy to choose the order and
> here the values are inherently ordered, so reference to a legend is less
> problematic. Since the values for each reply are discrete (no of 1s, no
> of 2s etc), another option is a line chart for each disease, so the
> slope between values helps the visual comparison of "profiles".
>
> I'll guess the data were input as 37 cases with a variable for the
> importance of each disease.  If -reshaped- as 37*8 cases with
> subject/disease/importance as variables, -tab disease importance- gives
> the table of counts that the graph is intended to illustrate.  Hence one
> can do some analysis before drawing a graph to present the findings.
>
> Allan
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