Transcript Statistics for Marketing and Consumer Research

Multidimensional scaling
Chapter 13
Statistics for Marketing & Consumer Research
Copyright © 2008 - Mario Mazzocchi
1
Multidimensional scaling
• a set of statistical techniques which allow one to
1. translate consumer preferences or perceptions towards
products or brands into a reduced number of
dimensions (usually two or three)
2. Represent them graphically into a preference map or
perceptual map
• It is also possible to show both objects and
subjects (the consumers) in the same graph
through multidimensional unfolding (MDU)
• MDU is a technique which unfolds the coordinates
for consumers (or groups of consumers) on the
basis of their preferences or perceptions through
an ideal point model
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Common Space
0.75
London
Paris
0.50
Berlin
0.25
Dimension 2
Interpretation: How trendy is the city
Example of MDS output – holiday
destinations in two dimensions
Amsterdam
Rome
0.00
Madrid
-0.25
Athens
Stockholm
-0.50
Bruxelles
-0.75
-0.5
0.0
0.5
Dimension 1
May be interpreted as “climate”
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• Each of the respondents is asked to
rank the cities, without necessarily
specifying why one city was preferred to
another
• Similarities in ranking across an
adequate number of respondents reflect
perceived similarities between cities (e.g.
London is more similar to Berlin than to
Athens)
• Graph distances reflect dissimilarities
• If the two dimensions can be labelled
according to some criterion, as for
principal component or factor analysis,
then it becomes possible to understand
the main perceived differences.
3
Marketing applications
• Sensory evaluation and new product development
• Example, a company developing a low-salt soup
• An evaluation panel is asked to assess a set of existing soup brands
according to several criteria concerning taste, smell, thickness, storage
duration, perceived healthiness and price
• Consumers are asked to identify their ideal product in terms of the
same characteristics which may not coincide with one of the existing
soups
• The final output is a perceptual map displaying both consumer
preferences (in terms of their ideal products) and the current
positioning of the existing brand
• A concentration of consumers’ ideal points identify a segment (cluster
analysis might also be used as a tool to segment respondents)
• if no brands appear in the neighbourhood of a segment then there is
room for the development of a new product in that area
• If the perceptual dimensions have been clearly identified this also
allows one to choose the characteristics of the new products.
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Example of brand positioning
The two dimensions are the output of some
reduction technique
- PCA or FA for interval (metric) data
- correspondence analysis for non-metric
data
coordinates for brands are obtained by
running PCA (or FA) on sensory
assessments (usually through a panel of
experts unless objective measures exists)
Consumer positions (as individuals or as
segments) can be defined in two ways
1) using their “ideal brand” characteristics
2) by translating preference ranking for
brands into coordinates through unfolding
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Brand positioning
The product should be
healthy as both A & C
like that dimension.
The thicker it is, the
closer is to C
compared to A
Segment A
chooses
three but it
is not that
close
There is room for a new
product for segment C
also close to sgm. A
Brand five survives
because of segment
C, but it is far from C’s
preferences
Consumer segment B
Brand 5
is close to Brand three
Brand repositioning. If brand five had this marketing research information, one could
improve one’s performance by enhancing the perceived healthiness of the product
Brand
and
4 are and through a targeted advertising campaign). This
(e.g. reducing
the 1salt
content
Consumer segment D
perceivedwould
as similar
move brand fivcloser to segment
C with Brand 2
is happy
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Other applications of MDS
• If consumer perceptions are compared
through MDS before and after an advertising
campaign aimed at changing perceptions it
becomes possible to measure the success of
the advertising effort
• Finally, MDS could be exploited to simplify
data interpretation and provide some prior
insight before running psycho-attitudinal
surveys.
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Running MDS
• MDS is a container for statistical techniques to produce
perceptual or preference maps.
• There is a range of options and choices depending on the
type of MDS data.
• object of the analysis: it can be a product, a brand or any
other target of consumer behaviour, like tourism
destinations in the initial example. The object can be
depicted as a set of characteristics, represented through
• objective dimensions (e.g. salt content in grams)
• subjective dimensions as declared by respondents
(subjects) in a survey
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Preferences and perceptions
• With subjective dimensions, consumer evaluations can be
based on preferences or perceptions
• Measurement through preferences (preference map)
• the subjects rank several objects according their overall evaluations
(e.g. ordering of soup brands).
• Measurement through perceptions (perceptual or
subjective dimensions, perceptual map)
• the respondent must attach a subjective value to an object’s
feature (e.g. a rating of the thickness of each soup brand)
• When individual attribute perceptions are measured,
respondents may be asked to state the combination of an
object’s features that correspond to their ideal object (to
be translated into an ideal point in the spatial map).
• The ideal point can alternatively (and preferably) be
derived through an unfolding statistical model.
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Measurement
• Preferences
• rank order scales
• Q-sorting
• other comparative scales.
• Perceptions
• non-comparative scales
• Likert
• Stapel
• Semantic Differential Scales.
• Two types of variables for MDS
• Non-metric variables just reflect a ranking, so that it is not possible to
assess whether the distance between the first and second object is larger or
smaller than the distance between the second and the third.
• Metric variables reflect respondent perception of the distances
• Generally, preference rankings are classified as non-metric and
perceptions and objective dimensions are metric.
• This distinction can be very important, as it leads to two different MDS
approaches.
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Non-metric vs. metric MDS
• The output of non-metric MDS aims to preserve the
preference ranking supplied by the respondents
• Metric MDS also takes into account the distances as
measured by perceptions or objective quantities.
• This distinction is often overcome by the use of
techniques which allow one to transform nonmetric variables and treat them as if they were
metric, like the PRINQUAL procedure in SAS or
correspondence analysis (see lecture 14)
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Multidimensional scaling steps
1.
2.
3.
4.
5.
6.
7.
Decide whether mapping is based on an aggregate
evaluation of the objects or on the evaluation of a set of
attributes (decompositional versus compositional
methods)
Define the characteristics of the data collection step
(number of objects, metric versus non-metric variables)
Translate the survey or objective measurements into a
similarity or preference data matrix
Estimate the perceptual map
Decide on the number of dimensions to be considered
Label the dimensions and the ideal points
Validate the analysis
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Decompositional vs. compositional MDS
• Decompositional (attribute-free) approach
• The spatial maps reflect the subject evaluations
• Comparisons of the objects in their integrity
• Advantages: respondent assessment is easier, it is possible to obtain a
separate perceptual map for each subject or for homogeneous groups of
subjects
• Limits: no specific information on the determinants of the relative position
of the objects. It is not possible to plot both the objects and the subjects in
the same map. It is difficult to label the dimensions (labels are based on
the researcher’s knowledge about the objects)
• Compositional (attributed-based) approach
• Subject assess es a set of attributes (compositional or attribute-based
approach).
• Preferred when it is relevant to describe the dimensions and explain the
positioning of objects and subjects in the perceptual map
• Requirements: all the relevant attributes must be considered while avoiding
including irrelevant ones; the combination of attributes must be adequate
to reflect the overall object evaluation.
• The method to be used depends on the chosen approach
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Objects and variables
• The higher the number of objects the more accurate the output of MDS in
statistical terms
• However, data quality suffers because it might be difficult for subjects to
provide large number of comparisons.
• The number of objects required for the analysis increases with the number
of dimensions being considered
• For two-dimensional MDS it is advisable to have at least ten objects
• For three-dimensional MDS it is advisable to have about fifteen objects
• As the number of objects increases goodness-of-fit measures become less
reliable).
• Measurement through metric or non-metric variables
• The starting matrix for MDS is different
• With non-metric data (ordinal variables or paired comparison data) the initial
data matrix only considers ranking and not the distance between the objects
• With metric variables the matrix preserves the distances observed in the
subject evaluations.
• Most of MDS methods can also deal with mixed data-set with both metric and
non-metric data
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Data matrix
• Data for MDS are similarities between objects or preference
(ranking) of objects
• Decompositional approach: a matrix for each subject exists, which
translates into a matrix comparing all objects
• Compositional approach is chosen, a matrix for each subject and
attribute exists and this translates into a matrix comparing all
objects for each attribute
• Similarity data: the subject compares all pairs of objects and ranks
the pairs in terms of their similarity (usually this leads to non-metric
MDS)
• The similarity (or dissimilarity) matrix can be also computed from
metric evaluation (rating) of the objects
• Compositional approach: summarize (e.g. through averaging) the distances
between the objects across the subjects, assuming all subjects have the same
weight
• Decompositional approach: a synthetic measure of similarity between objects
is computed for each subject (weights can be used if available and appropriate)
then the similarity matrix across the subjects is derived
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Estimation
• Estimation starts from a proximity or similarity matrix and
produces a set of n-dimensional coordinates
• Distances in this n-dimensional space reflect as closely as
possible the distances recorded by the proximity matrix
• Metric scaling is based on a proximity matrix derived from
metric data
• Non-metric scaling projects dissimilarities based on ranking
(ordinal variables) preserving the order emerging from the
subjects’ preferences
• Non-metric scaling should also be applied to metric
distances when the researcher suspects that collected data
might be affected by relevant measurement errors (e.g.
when respondents may encounter difficulties in stating
their perceptions with precision while ordering can be
regarded as more reliable)
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Metric scaling
• With metric variables, one might apply FA (or PCA) to reduce the
dimensions and obtain the scores which represent the coordinates.
However, there is a difference
• Coordinates obtained from PCA and FA are the best representation of the
original data matrix in terms of variability
• Metric scaling coordinates ensure that the distance between two points is as
close as possible to the distance as measured in the proximity matrix
• Classical MultiDimensional Scaling technique (CMDS) also known as
principal coordinate analysis
• Decompositional approach (unique similarity matrix comparing all objects)
• The proximity or similarity matrix is obtained by applying the Euclidean
distance on the data matrix (or other distance measures as those for cluster
analysis).
• The objective of CMDS is to extract a a n-dimensional configuration of points
whose distances dij* are as close as possible to the original distances dij
according to the following quadratic equation
p i 1
 (d
i  2 j 1
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ij
*2
ij
d )
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Non-metric scaling
• Ordinal variables (preference data)
• coordinates are obtained through computational algorithms
• Many procedures. The original method (Shepard-Kruskal) is as follows
•
•
•
given a number of dimensions n, the p objects are represented through an
arbitrary initial set of coordinates
a function S is defined to measure how distant the current set of
coordinates is from the original ordering (monotonicity requirement)
using an iterative computer numerical algorithm the values that minimize S
are found
• The procedure can be extended to include a search for the optimal
number of dimensions n.
• Other algorithms:
•
•
•
ALSCAL (SPSS & SAS)
Algorithms in the MDS procedure in SAS
INDSCAL
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Goodness-of-fit and STRESS
• The STRESS measure (STandardized REsiduals Sum
of Squares) is a function of the original and derived
distances to evaluate the goodness-of-fit of a MDS
p 1 p
solution:
2
STRESS 
  (d
i 1 j  i 1
p 1
ij
 dˆij )
p

i 1 j  i 1
d ij2
• The smaller the stress function, the closer are the
derived distances to the original ones
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STRESS and number of dimensions
• The STRESS value decreases as the number of dimensions
increases
• The number of dimensions can be evaluated through a scree
diagram of STRESS against the number of dimensions (as for
FA, PCA or cluster analysis) where the optimal number
corresponds to an elbow
• The preferred number of dimensions is usually two or three
which allows for graphical examination
• The search usually goes from one to five dimensions
• Identification of the optimal number within the metric and
non-metric iterative algorithm
• An additional step evaluates the STRESS function
• The algorithm stops when the addition of a further dimension does not
reduce the STRESS value to a perceptible extent
• With two dimensions a STRESS value below 0.05 is generally
considered to be satisfactory.
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Labelling dimensions
• Interpretable dimensions (attaching a meaning to
coordinates) enhance the use of MDS maps (e.g.
new product development)
• Interpretation may be difficult
• Compositional approaches (or attribute ratings are
otherwise available) allow for more objective
methods based on the relative weight of each
attribute (something similar to factor loadings)
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Ideal points
• Objective: position ideal points (for each subject)
and the actual brand evaluations (the objects)
within the same map
• Ideal point: set of coordinates which represents
the stated optimal combination of attributes
(under the compositional approach)
• If no precise statement is made by the subject it is
still possible to locate the ideal point
• Indirect positioning of ideal points is based on a
procedure which ensures that distances of the
objects from a subject’s ideal point reflect the
preference ordering as much as possible
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Internal vs. external preference
mapping
• Internal Preference Mapping (IPM)
• the proximity matrix for the objects is based on evaluations from
consumers. The final map shows:
• products as they are perceived by the consumers
• consumers according to their preferences.
• External data (i.e. objective measures or expert evaluations) can be
used to interpret the dimensions but not to draw the map
• External Preference Mapping (EPM)
• the proximity matrix contains objective (analytic) measures of
product characteristics (or evaluations from expert panels)
• The maps contain information external to the set of consumers
which provide their evaluation of the products
• The final map shows
• products as they are evaluated by the external source
• consumers according to their preferences
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Internal preference mapping
• The ideal point (or vector) for each subject is estimated
from the preference orderings through unfolding
• Example
• four brands (A, B, C and D) are evaluated.
• Consumer one states a preference ordering as D, B, C and A, where
D is the most preferred brand
• Consumer two states the ordering C, B, D, A
• The ideal point for Consumer one will be closer to D and far away
from A, while for Consumer two the ideal point will probably be still
far away from A but closer to C than to D.
• The distance of the ideal point from the objects in the product
space should reflect as much as possible the ordering of the
consumer preferences
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IPM and unfolding
• The ideal product is not necessarily a precise point in the
preference map but could be represented as a line (or an
arrow) going from the least preferred objects towards the
most preferred ones
• Unfolding approach
• Decompose all preference orderings for a given set of objects
(products) so that the same products can be represented in a lower
dimensional space
• Once the products are positioned on the preference map it is
possible to see where the subjects (consumers) are positioned
• While the dimensions reflect some product characteristics that are
the same for all consumers each consumer attaches a different
weight to those dimensions
• Consumers have different ideal points because they place a
different weight on the dimensions
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External preference mapping
• EPM follows a different philosophy from IPM
– It strictly requires the use of perceptual (metric) data
– Evaluations of the product characteristics are on a measurement scale
rather than their simple ordering
– Measurements are usually based on analytic or objective evaluations or
expert evaluations (external to the set of consumers which provide their
product evaluation).
• The input matrix contains the (quantitative) measurements
of all attributes for each product.
• A data reduction techniques (usually PCA) allows one to
attach a set of coordinates (the principal component
scores) to each of the products
• The principal components define the dimensions of the map
and can be interpreted (labelled) through the component
loadings.
• An algorithm (e.g. PREFMAP) allows one to elicit the
position of subjects (consumers) in the map.
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IPM or EPM?
• Both approaches can be applied to the same data set but they
reflect different philosophies
– a consumer very much likes red full-bodied wine and white sweet and
sparkling wine
– IPM: these two products share similar preferences and will be
positioned next to each other
– EPM: the product characteristics are very different they will look
distant on the perceptual map.
• The choice between IPM and EPM is mainly related to the
choice of prioritizing either the preferences of the subjects
(IPM) or the product characteristics (EPM).
• When many dimensions are chosen the two approaches
produce similar results but with reduced dimensions
discrepancies are likely to emerge
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IPM vs. EPM
• IPM is better when
• Perceptual data are inadequate to reflect preferences as it is not
necessarily true that the combination of the product attributes is an
adequate representation for the product
• Physical attributes as they are perceived by the consumer
are processed into a number of perceived benefits and
these benefits are then translated into preferences
• Thus the relative weight of the physical attributes as
compared to the abstraction process could drive the choice
between IPM and EPM.
• For those goods where the cognitive evaluation is mainly
based on the objective attributes EPM seems to be
preferable
• Goods where the connection between perceptions and
preferences is not so natural (and affective processes play
a major role) are better analyzed with IPM
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MDS in SPSS – the data
• MDS data set
• Fifty individuals (the subjects or consumers)
were asked to rank ten sports (the objects or
products) according to their preference
• a panel of expert sport journalists provided an
evaluation of the attributes of each sport (the
product characteristics) in terms of strategy,
suspense, physicality and dynamicity
• The final data set (MDS.sav) has one row for
each sport and one column for each consumer
plus four columns for the sports’ attributes
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The MDS data set
Ratings by consumers
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Evaluations of product
characteristics by experts
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IPM & unfolding
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Unfolding
Proximities are defined
from the subjects’
preference rankings
This nominal variable
provides the labels for
the objects (sports)
When measures for the
same set of objects are
provided by different
sources (e.g. different
groups/scenarios) –
data should be stacked
Defines model
options
Allows to
place
restrictions
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Defines
options for the
algorithm
Choose
plots
Displays and saves
additional output
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Unfolding options
Select identity
as data come
from a single
source
Rankings are
dissimilarities
and ordinal
data
Number of
dimensions to
be explored
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Options
Convergence criterion for
the STRESS function
Choose the
starting
configuration
The penalty term helps avoid
degenerative solutions (where
points can hardly be distinguished
from each other).
The weight of the penalty term
increases as the strength
becomes smaller.
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When the penalty range is zero, no
correction is made to the Stress-I
criterion, while larger range values
lead to solutions where the
variability of the transformed
proximities is higher
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Plots
The final
common
space shows
subjects and
objects on the
same plot
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Applies different
colors or markets to
different objects
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Outputs
Output tables can be
selected here
Output coordinates
(distances) can be saved
into a new file
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Unfolding output
Measures
Iterations
Final Function Value
Function Value
Parts
Badness of Fit
Goodness of Fit
Variation
Coefficients
Degeneracy Indices
Stress Part
Penalty Part
Normalized Stress
Kruskal' s Stress-I
Kruskal' s Stress-II
Young's S-Stress-I
Young's S-Stress-II
Dispersion Accounted For
Variance Accounted For
Recovered Preference
Orders
Spearman's Rho
Kendall' s Tau-b
Variation Proximities
Variation Transformed
Proximities
Variation Distances
Sum-of-Squares of
DeSarbo's
Intermixedness Indices
Shepard's Rough
Nondegeneracy Index
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992
.3835645
.0410912
3.5803705
.0016885
.0410908
.1905153
.0720164
.1781156
.9983115
.9666225
.8471837
.8617494
.7273984
.5043544
.3322572
.5071630
.4694185
.5609796
The final STRESS-I value of 0.04 is acceptable.
Other measures of “badness-of-fit” and “goodness-of-fit” are
provided and confirm that the results are acceptable.
The variation coefficient of the transformed proximities
can be used to check for the risk of degenerated solutions
(points are too close to each other). In this case, the
variation coefficient of the transformed proximities is 0.33 as
compared to the 0.50 of the original ones, which means that
most of the variability is retained after transformation.
Furthermore, the distances show a variability which is more
or less equal to the original one, indicating that the points in
space should be scattered enough to reflect the initial
distances.
The DeSarbo’s Intermixedness index and the Shepard’s
RNI also provide warning signals for degenerated solutions:
the former should be as close to zero as possible and the
latter as close to one as possible. There are no strong
signals for a degenerated solution
One may wish to try different parameters for the penalty
term to see whether these indicators improve.
37
Plots
Plot of objects
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Plot of subjects
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Joint plot
According to the sample, basketball,
baseball and cricket share
similarities in subjects’ perceptions
and so do American football, motor
sports and ice hockey.
A third “cluster” is provided by
handball, waterpolo and volleyball,
while football seems to be
equidistant from all other sports.
Consumers are also grouped in
clusters according to their
preferences and the joint
representation allows one to show
not only which sports (products) are
closer to the preferences of different
segments, but also which sports
need to be repositioned to attract
more public, like the cluster with
volleyball, waterpolo and handball.
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Repositioning
• If one can attach a meaning to dimensions one and
two it becomes possible to understand what
characteristics of the products should be changed
• A method to obtain an interpretation of the
coordinates consists in looking at the correlations
betweens the coordinates of the sports and the
object characteristics that can be measured
objectively or through the evaluation of expert
panellists.
• The algorithm has created an output file coord.sav
which contains the two coordinates for each sport
and consumer and can be used to obtain the
bivariate correlations
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Labelling dimensions
DIM_1 DIM_2 Strategy Suspense Physicity Dinamicity
DIM_1
1.000
0.000
-0.839
-0.167
0.130
0.362
DIM_2
0.000
1.000
0.338
-0.180
0.330
0.168
Sports on the left side of the graph are likely to be more strategic, while those
on the right are more dynamic.
Considering the second dimension, as values move towards the top, the sports
are expected to become more physical and strategic, while negative values
seem to indicate a lack of suspense.
Ideally, those who want to bring people closer to volleyball, water-polo or
handball should try and move the points toward the top left area, thus trying to
persuade “consumers” that these sports are more strategic (especially),
dynamic and physical than currently thought.
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SAS procedures
• The SAS procedure for multidimensional scaling
(proc MDS) is a generalization of the ALSCAL
algorithm
• MDS applies multidimensional scaling as a mapping
technique for objects, but does not perform
unfolding
• The TRANSREG procedure allows unfolding
• There is an option (COO) which returns the
coordinates of the ideal point or vector for internal
preference mapping
• proc PRINQUAL performs PCA on qualitative data
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