Example A

TITLE=Simple Example Using Similarities (Ekman's Data)

SUBTITLE=This uses the color comparison data from Ekman's classic study.  (Press F4 to see data file.)

NOBJECTS=14

SIMILARITYLIST
a 	1													
b 	0.86	1												
c 	0.42	0.5	1											
d 	0.42	0.44	0.81	1										
e 	0.18	0.22	0.47	0.54	1									
f 	0.06	0.09	0.17	0.25	0.61	1								
g 	0.07	0.07	0.1	0.1	0.31	0.62	1							
h 	0.04	0.07	0.08	0.09	0.26	0.45	0.73	1						
i 	0.02	0.02	0.02	0.02	0.07	0.14	0.22	0.33	1					
j 	0.07	0.04	0.01	0.01	0.02	0.08	0.14	0.19	0.58	1				
k 	0.09	0.07	0.02	0	0.02	0.02	0.05	0.04	0.37	0.74	1			
l 	0.12	0.11	0.01	0.01	0.01	0.02	0.02	0.03	0.27	0.5	0.76	1		
m 	0.13	0.13	0.05	0.02	0.02	0.02	0.02	0.02	0.2	0.41	0.62	0.85	1	
n 	0.16	0.14	0.03	0.04	0	0.01	0	0.02	0.23	0.28	0.55	0.68	0.76	1

This is a simple PERMAP data file based on using similarity data.
Comments in the data file are ignored and everything is considered
to be a comment unless it announced by a keyword.  A keyword is
recognized only if it is the first word on a line.  In this file there
are four keywords, two of which are optional.  The keywords are shown in
all caps.  The 'title' keywords are the optional ones.  They serve only  
to transfer information to the MDS map.  Keywords do not need to 
be capitalized but doing so can improve file readability.

Permap reads only text files so if this data set came from an Excel file 
it would have been saved as a text file.  Simple text files have no 
formatting and are usually denoted as being txt, csv, ANSI, or ASCII files.  

The identifiers (also called object names or labels), such as the a, b, c, ..., 
in the above data, are optional.  If you don't want them just omit them, or use
the View menu to tell PERMAP to ignore them.  Object names can have multiple 
parts but each part must start with an alphabetic character unless you give 
PERMAP specific instructions to allow numbers to be names.  See the manual 
for more information on naming objects.

Teaching: Ekman's data (1954) are for human perceptions of the similarities 
of 14 different pure colors.  The colors differ in wavelength but not in brightness 
or saturation.  Ekman asked for judgments on the 91 color pairs from 31 
subjects using a 0 (no similarity) to 4 (identical) scale.  The results from 
all 31 subjects were averaged and divided by 4 to get the similarity matrix.

Teaching: This data set is often analyzed using an ordinal (nonmetric) approach, 
but ratio (metric) or interval (metric) approaches work just as well.  Sometimes 
nonmetric analyses make good theoretical sense and sometimes they are a 
sign of a lazy experimentalist or a timid analyst.  See the PERMAP operations 
manual for more information on this important topic. 

Teaching: This data set is interesting historically and provides a good example 
of a typical MDS application, even though Ekman did not use MDS in its analysis.  
However, there is some question about the validity of the conclusions that were 
drawn based on the data.  Please see the PERMAP operations manual for 
more information on conflicts between large and small dissimilarity values, 
the occurrence of circular patterns, and testing by relaxation into a higher dimension. 
