Dual scaling of a subjects-by-objects table of dominance data (preferences, paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow different. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means.Correspondence analysis, dominance data, dual scaling, paired comparisons, preferences, principal component analysis, ratings

Anna Torres

Michael Greenacre

English

Research Papers in Economics

Dual Scaling of Dominance Data
and its Relationship to Correspondence Analysis
MICHAEL GREENACRE & ANNA TORRES1
Facultat de Ciµ encies Econµ omiques i Empresarials
UNIVERSITAT POMPEU FABRA
1Working Paper number 430 (December 1999), Department of Economics and Business, Universitat
Pompeu Fabra, Ramon Trias Fargas, 23{27, E-08005 Barcelona, Spain. E-mail: michael@upf.es and
anna.torres@econ.upf.es
1Abstract
Dual scaling of a subjects-by-objects table of dominance data (preferences, paired compar-
isons and successive categories data) has been contrasted with correspondence analysis,
as if the two techniques were somehow di®erent. In this note we show that dual scaling of
dominance data is equivalent to the correspondence analysis of a table which is doubled
with respect to subjects. Both methods are in turn equivalent to a principal components
analysis of the undoubled dominance table which is centred with respect to subject means.
Journal of Economic Literature Classi¯cation: C19, C88
Keywords: Correspondence analysis, dominance data, dual scaling, paired comparisons,
preferences, principal component analysis, ratings.
21 Introduction
Dual scaling (Nishisato 1980, 1994) is a multivariate method for assigning scale values to
the rows and columns of a table of data, with certain optimal properties. Correspondence
analysis (Benz¶ ecri 1973, Greenacre 1984, Lebart et al 1984) is a method for assigning
optimal spatial positions to the rows and columns of a data table. It is well-known
that dual scaling (DS) and correspondence analysis (CA) are mathematically equivalent
techniques in the cases of a two-way frequency table and a multiple indicator matrix
(or response pattern matrix) { see Greenacre (1984) or Nishisato (1994), for example.
Nishisato and Gaul (1988) talk of many methods (such as DS and CA) having a \common
starting point" and \moving into a phase of unique advancement". He says that \the
phenomenon of branching out will continue, and we may see the day when the name dual
scaling will no longer be used synonymously with correspondence analysis". The same
text appears in the introduction of Nishisato's 1994 book.
One particular example often cited as indicative of a di®erence between DS and CA is
the case of so-called \dominance data", which includes pairwise comparisons, rank orders
(or preferences) and successive categories (or ratings) data { see Nishisato 1978. Again in
Nishisato (1994, p.185) it is stated that DS has the advantage over CA in being able to
handle data with negative elements, in contrast to CA which is known to be applicable
to nonnegative input data only. Quoting Nishisato and Gaul (1988, p. 152): \As is well
known, correspondence analysis is not applicable to a data table which contains negative
numbers. One of the distinct aspects of dual scaling, however, lies in the extension to a
data table which contains negative numbers." In this note we show that for all three types
of dominance data the two methods are actually equivalent, the only di®erence being that
CA analyzes the uncentered data (with no negative elements) whereas the dual scaling
formulation analyzes the centered data (with some negative elements). Furthermore, both
of these equivalent analyses are in turn recoverable from an unstandardized principal
components analysis of the preference matrix which has been centred with respect to
subject (usuall row) means.
2 Preference data and dual scaling
We start with the simplest and most structured form of dominance data, that of preference
data. We consider the same data set as Nishis6 1 5 2 4 3 -5 5 -3 3 -1 1
4 3 5 2 4 1 6 1 -3 3 -1 5 -5
5 3 4 2 6 1 5 1 -1 3 -5 5 -3
6 5 3 1 4 6 2 -3 1 5 -1 -5 3
7 1 2 4 5 3 6 5 3 -1 -3 1 -5
8 4 3 2 6 5 1 -1 1 3 -5 -3 5
9 2 1 4 5 3 6 3 5 -1 -3 1 -5
10 6 1 4 3 5 2 -5 5 -1 1 -3 3
Party Plans: A = potluck in the group room during the day; B = pub/restaurant
crawl after work; C = reasonably priced lunch in an area restaurant; D = evening
banquet at a hotel; E = potluck at someone's home after work; F = ritzy lunch at
a good restaurant. Most preferred is 1, least preferred is 6.
We refer readers to Nishisato (1980, 1978, 1994) for an explanation and justi¯cation
of the methodology. Here we merely summarize the result that to perform a dual scaling
of the preference matrix, the ¯rst step is to calculate what is called a \dominance table",
and then e®ectively perform a singular-value decomposition of the table. Speci¯cally, let
the preference data, in Nishisato's notation, be denoted by the matrix K(N £ n), with
general element Kij, where N is the number of subjects (respondents) and n the number
of objects to rank order (thus in our example, N = 10 and n = 6). The dominance table,
with general element eij, is de¯ned as:
eij = n + 1 ¡ 2Kij
The optimal score vectors for subjects and objects solve the following pair of linear rela-
tionships:
x =
c
½
Ey y =
c
½
E
Tx
where
c =
s
1
Nn(n ¡ 1)2
These equations have several solutions, and we can subscript xk, yk and ½k by k to denote
the k-th solution, or k-th dimension. The normalization proposed for these solutions
is given by Nishisato (1978, 1980, p. 130) as xk
Txk = yk
Tyk = n. There is a slight
4error, however, in the case of the subject score vectors, because as we shall see later,
the normalization for xk should be xk
Txk = N, which is indeed the normalization of
the solution reported by Nishisato (1998, Table 1). These are what Greenacre (1984,
pp. 93{95) calls the \standard coordinates" (see also Greenacre 1993, pp. 59{61, for a less
technical, more substantive explanation). For graphical representation of the solution,
Nishisato (1998) proposes the \asymmetric map" with the subject scores in standard
coordinates and the object points in principal coordinates, that is the joint display of the
xk's and ½kyk's.
3 Correspondence analysis and doubling
Whereas CA is primarily a method for categorical data in the form of two-way and multi-
way tables, it has been extended to many other types of data types (Benz¶ ecri 1973,
Greenacre 1984, 1996). In the case of rating scales, variables are usually \doubled" to
create a pair of variables, which can be called the positive and negative poles of the rating
scale (see also Greenacre 1993, chapter 19). The convention is to translate the 1 to 5
rating scale, for example, to a 0 to 4 scale ¯rst, which is the positive pole of the doubled
pair, and then subtract this value from 4 to obtain the negative pole. An alternative
name for the variable and its doubled counterpart is \variable" and \anti-variable". In
the case of a preference table, where the rows add up to a constant, there is not such an
obvious reason for doubling the table and Greenacre (1984, pp. 183{184) has shown the
di®erence between an undoubled and doubled correspondence analysis. There is another
possibility for doubling a preference table, however, and this is to double the rows of the
table, creating two rows for each subject which can be referred to as the \subject" and
the \anti-subject". Again, we adopt the convention that ranks from 1 to 6, in our present
example, are simply rede¯ned to vary from 0 to 5. Then we subtract this ranking from 5
to obtain the doubled counterpart. For example, subject 1 has ranked party plan C with
a 5, that is it is his or her second last preferred alternative. Subject 1 will be denoted
by two rows of the doubled table, in which the third element corresponding to C will be
5 ¡ 1 = 4 in the ¯rst row and 5 ¡ 4 = 1 in the second row. These values can be thought
of as counts, since in the former row the value 4 is the number of alternatives preferred
to C and in the latter row the value 1 is the number of alternatives which C is preferred
to. Thus, from a N £ n table of 1-to-n preferences Kij, let us de¯ne a 2N £ n table of
doubled preferences where the ¯rst N rows are
Lij = Kij ¡ 1
5and the N additional rows are de¯ned by:
Mij = n ¡ 1 ¡ Lij
(Table 2). We have assigned labels to the rows where a negative sign is attached to
the subject numbers in the top half of the matrix since these are the counts against the
respective object, whereas the lower half has a positive sign because these are the counts in
favour of the respective object and in this sense indicating a positive association between
the subject and the object.
Table 2
Preference Data in Doubled Format
A B C D E F
1- 5 0 4 3 2 1
2- 1 5 2 4 3 0
3- 5 0 4 1 3 2
4- 2 4 1 3 0 5
5- 2 3 1 5 0 4
6- 4 2 0 3 5 1
7- 0 1 3 4 2 5
8- 3 2 1 5 4 0
9- 1 0 3 4 2 5
10- 5 0 3 2 4 1
1+ 0 5 1 2 3 4
2+ 4 0 3 1 2 5
3+ 0 5 1 4 2 3
4+ 3 1 4 2 5 0
5+ 3 2 4 0 5 1
6+ 1 3 5 2 0 4
7+ 5 4 2 1 3 0
8+ 2 3 4 0 1 5
9+ 4 5 2 1 3 0
10
1). The correspondence
6matrix P is thus:
P =
"
L
M
#
=Nn(n ¡ 1)
the row and column masses are uniform in each case:
r =
1
2N
1 c =
1
n
1
and the diagonal matrices of the row and column masses are:
Dr =
1
2N
I Dc =
1
n
I
CA is then the singular-value decomposition (SVD) of:
S = D
¡1=2
r (P ¡ rc
T)D
¡1=2
c
= (2N)
1=2
Ã"
L
M
#
=Nn(n ¡ 1) ¡
1
2Nn
11
T
!
n
1=2
=
(2Nn)1=2
2Nn(n ¡ 1)
Ã
2
"
L
M
#
¡ (n ¡ 1)11
T
!
=
1
21=2(Nn)1=2(n ¡ 1)
"
¡E
E
#
= 2
¡1=2c
"
¡E
E
#
(1)
where E is the dominance matrix de¯ned above and
c =
1
(Nn)1=2(n ¡ 1)
=
s
1
Nn(n ¡ 1)2
is the same constant in Nishisato's formulation. Notice that the dominance matrix appears
in the lower, or doubled, part of the augmented matrix, while its negative appears in the
top half.
The SVD of S takes the form:
S =
"
¡U
U
#
D®V
T
where UTU + UTU = VTV = I.
The row standard coordinates are:
© = D
¡1=2
r U = (2N)
1=2U
for the last N rows and ¡© for the ¯rst N rows, and the column principal coordinates
are:
G = D
¡1=2
c VD® = n
1=2VD®
7We now show that this solution is identical to that of the dual scaling problem. From
(1) we can write: "
¡E
E
#
=
"
¡21=2U
21=2U
# µ1
c
D®
¶
V
T
which implies that the SVD of E is:
E = (2
1=2U)
µ1
c
D®
¶
V
T
since 2UTU = I. This also shows that the singular values ® are the same as the ½'s in
Nishisatos dual scaling de¯nition. To satisfy the normalization of Nishisato's de¯nition,
the left and right vectors have to be multiplied by N1=2 and n1=2 respectively to obtain
the standard coordinates and the right vectors have to be scaled by the singular values in
D® = D½ to become principal coordinates. This gives:
X = N
1=2(2
1=2U) Y = n
1=2VD®
which are identical to the CA coordinates.
The results of the CA are given in Table 3, and are identical to those given by Nishisato
(1998, Table 1).
4 Equivalence to principal components analysis
Not only is DS equivalent to CA, but in this case they are both equivalent (up to scaling
factors only) to an unstandardized principal components analysis (PCA) of the original
preference table, centered with respect to row means, that is centering the column vectors.
This is trivially seen by calculating the centered preference table, with elements Kij ¡
1
2(n + 1), that is in our example subtracting the constant row mean of 31
2 from every
element of the table. The result is a table equal to ¡1
2E. Since PCA is just the SVD
of the centered matrix, the results must be identical apart from a change of sign and a
scaling factor, since the singular values of the centred matrix will not yield directly the
values of ½ needed for the computation of principal coordinates.
8Table 3
Results of Correspondence Analysis of Table 2
INERTIAS AND PERCENTAGES OF INERTIA
-----------------------------------
1 0.208971 44.78% ************************************************
2 0.125402 26.87% ******************************
3 0.067526 14.47% ****************
4 0.037876 8.12% *********
5 0.026892 5.76% ******
--------
0.466667
ROW RESULTS (last 10 rows only)
-----------
+---+-----+------------+-------------+-------------+
| I| NAME| QLT MAS INR| k=1 COR CTR| k=2 COR CTR|
+---+-----+------------+-------------+-------------+
| 11| A | 698 50 50| 486 507 57| 299 191 36|
| 12| B | 826 50 50| -75 12 1| -616 814 151|
| 13| C | 977 50 50| 535 614 69| 412 363 68|
| 14| D | 738 50 50| -586 737 82| 16 1 0|
| 15| E | 557 50 50| -509 555 62| -27 2 0|
| 16| F | 586 50 50| 407 356 40| -328 230 43|
| 17| G | 672 50 50| -452 439 49| 330 234 43|
| 18| H | 508 50 50| 289 179 20| -392 329 61|
| 19| I | 650 50 50| -305 199 22| 458 450 84|
| 20| J | 955 50 50| 641 880 98| 187 75 14|
+---+-----+------------+-------------+-------------+
COLUMN RESULTS
--------------
+---+-----+------------+-------------+-------------+
| J| NAME| QLT MAS INR| k=1 COR CTR| k=2 COR CTR|
+---+-----+------------+-------------+-------------+
| 1| 1 | 778 167 186| -627 755 313| -110 23 16|
| 2| 2 | 810 167 209| 384 252 117| 571 558 433|
| 3| 3 | 540 167 106| -149 75 18| -371 465 183|
| 4| 4 | 243 167 129| 242 163 47| 170 80 38|
| 5| 5 | 694 167 140| -483 595 186| 197 99 52|
| 6| 6 | 940 167 231| 632 617 319| -457 323 278|
+---+-----+------------+-------------+-------------+
Results are taken verbatim from program SimCA (Greenacre 1986). The
principal coordinates (multiplied by 1000) are given in the columns headed
k=1 and k=2 respectively. Thus the results in Table 1 of Nishisato (1998)
are identical for the objects (columns) which are in principal coordinates.
To obtain the standard coordinates for the subjects, the principal coordi-
nates should be divided by the square root of the corresponding principal
inertias, i.e.
p
0:208971 and
p
0:125402 respectively. For example, the
¯rst coordinate of 1.06 reported by Nishisato for subject 1, dimension
91, can be obtained by calculating 0:486=
p
0:208971. Notice that the
coordinates of the ¯rst 10 rows are the same as the last 10, but with
a change of sign.
5 Pairwise comparisons and ratings
The equivalence between dual scaling and correspondence analysis for these two other
types of dominance data are easily deduced in a similar fashion.
In the case of paired comparisons, the data are set up directly in the format of the
upper or lower ha
matrix will still sum to a constant 1
2n(n ¡ 1) because
amongst the n(n¡1) ordered pairs of objects there is an equal number of preferences and
dispreferences.
The only di®erence between the doubled matrix in this case and the doubled matrix
in the case of the preference data, is that the data in each row may contain some identical
values, whereas in the case of preferences the data in each row are a strict permutation
of the integers 0;1;2;:::;n ¡ 1.
As far as dual scaling of ratings is concerned, Nishisato (1980) proposed a way of
treating ratings when the rating scale is identical for several items, e.g. each item has the
ordinal scale bad, fair, good, excellent, i.e. an m = 4 point scale. The method is also
discussed by Nishisato and Sheu (1984). The idea boils down to a rank-ordering of the
m ¡ 1 cutpoints between the successive categories and the n items themselves, i.e. it is
identical analytically to a preference table with n + m ¡ 1 objects. The novelty is in the
coding of the data, which are then subject to the usual analysis of rank orders, which we
have already shown to be equivalent to a doubled CA.
106 References
Benz¶ ecri, J.-P. & coll. (1973), L'Analyse des Donn¶ ees. Tome 2: l'Analyse des Correspon-
dances, Dunod, Paris.
Greenacre, M.J. (1984), Theory and Applications of Correspondence Analysis, Academic
Press, London.
Greenacre, M.J. (1986), \SimCA: A Program to Perform Simple Correspondence Analy-
sis", American Statistician, 51, 230{231.
Greenacre, M.J. (1993), Correspondence Analysis in Practice, Academic Press, London.
Nishisato, S. (1978), \Optimal scaling of paired comparison and rank order data: an
alternative to Guttman's formulation", Psychometrika, 43, 263{271.
Nishisato, S. (1980), Analysis of Categorical Data: Dual Scaling and its Applications,
University of Toronto Press, Toronto.
Nishisato, S. (1994), Elements of Dual Scaling: An Introduction to Practical Data Anal-
ysis, Lawrence Erlbaum, New Jersey.
Nishisato, S. (1998), \An interpretable graph for rank order data", in Blasius, J. &
Greenacre, M.J. (eds), Visualization of Categorical Data, Academic Press, San Diego,
pp. 185{196.
Nishisato, S. & Sheu, W.-J. (1984), \A note on dual scaling of successive categories data,
Psychometrika, 49, 493{500.
Nishisato, S. & Gaul, W. (1988), \Marketing data analysis by dual scaling", International
Journal of Research in Marketing, 5, 151{170.
11

A note on dual scaling of successive categories data,

An interpretable graph for rank order data&quot;,

Analysis of Categorical Data: Dual Scaling and its Applications,

Correspondence Analysis in Practice,

Elements of Dual Scaling: An Introduction to Practical Data Analysis, Lawrence Erlbaum,

L'Analyse des Donn¶ ees. Tome 2: l'Analyse des Correspondances,

Marketing data analysis by dual scaling&quot;,

Optimal scaling of paired comparison and rank order data: an alternative to Guttman's formulation&quot;,

SimCA: A Program to Perform Simple Correspondence Analysis&quot;,

Theory and Applications of Correspondence Analysis,

A note on the dual scaling of dominance data and its relationship to correspondence analysis

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A note on the dual scaling of dominance data and its relationship to correspondence analysis

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