Selecting Representative Affective Dimensions using Procrustes Analysis: An
Application to Mobile Phone Design
Meng-Dar Shieh, Tsung-Hsing Wang
Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
70101
Chih-Chieh Yang
Department of Multimedia and Entertainment Science, Southern Taiwan University,
Tainan County, Taiwan 71005
Abstract
Collecting affective responses (ARs) from consumers is of crucial importance to
designers wishing to produce an appealing product. Adjectives are often used by
researchers as an affective means by which consumers can describe their subjective
feelings about a given product design. This study proposes a Kansei engineering (KE)
approach for selecting representative affective dimensions using factor analysis (FA)
and Procrustes analysis (PA). A semantic differential (SD) experiment asks consumers
their ARs toward a set of representative product samples. FA is used to extract
underlying latent factors using an initial set of affective dimensions. A reverse
elimination process based on PA is capable of determining the relative importance of
adjectives in each step according to the calculated residual sum of squared differences
(RSSDs), thus, finally, obtaining the ranking of the initial set of adjectives. The results
of the proposed approach are also compared to the method which combines FA and a
two-stage cluster analysis (CA). A case study of mobile phone design is given to
demonstrate the analysis results.
Keywords: Factor analysis; Cluster analysis; Procrustes analysis; Kansei engineering;
Product design.
1. Introduction
A product’s appearance is one of the most important factors affecting a
consumer’s purchasing decision. Many systematic product design studies have been
carried out to get a better insight into consumers’ subjective perceptions. The most
notable research is Kansei engineering (KE) (Jindo et al., 1995). The basic
assumption of KE is that there exists a cause-and-effect relationship between the
1
product form features and the affective responses (ARs) of consumers (Han and Hong,
2003). As a typical KE process, Nagamachi (1993) suggested the following
procedures: (1) select the object; (2) collect adjectives; (3) understand the structural
meanings of the adjectives; (4) prepare slides or samples of the materials; (5)
evaluate emotions; (6) do statistical analysis; and (7) build an expert system. Schutte
et al. (2004) proposes a general framework based on a similar rationale to that of
Nagamachi (1993). Their framework consists of the following steps: (1) choose the
product domain; (2) span the semantic space; (3) span the product properties space;
and (4) synthesize. Despite the detailed procedures in the KE studies, the main
purpose is to process information on large amounts of product samples and analyze
the corresponding ARs systematically. In recent years, the usefulness of KE has been
proven and successfully adapted in various design fields other than product design:
interior design (Kawasumi et al., 2002), urban planning (Llinaresa and Page, 2008),
material design (Karana et al., 2009), web design (Cooper and Kamei, 2005), user
interface design (Wellings et al., 2009), etc.
When initializing a KE study, the appropriate adjectives with which to describe
the ARs of the product samples are hard to choose. The selection of representative
adjectives is sometimes conducted as a pilot study. Researchers usually collect a very
large and exhaustive number of adjectives as initial affective dimensions. Redundant
or similar adjectives must be screened out before applying these adjectives to future
experiments. However, the current adjective selection process in the literature is often
unsystematic. Manual selection methods based on pre-defined rules conducted by
experts, such as those proposed in the study of Delin et al. (2007), do not provide
quantitative information about the extracted representative adjectives and their
original adjective set. In order to obtain consumers’ ARs, the semantic differential
(SD) experiment (Osgood et al., 1957) is often conducted by asking consumers to
evaluate product samples using chosen adjectives. Factor analysis (FA) is the most
frequently adapted technique used for analyzing the evaluated scores obtained from
SD experiments (Coxhead and Bynner, 1981). In fact, FA is part of the feature
extraction method, which reduces input dimensions into fewer latent dimensions. By
applying such feature extraction techniques, similar adjectives can be merged into
factors according to the ARs of consumers. Researchers can then examine and
interpret the effects of the adjectives by analyzing the factor loadings of the original
adjectives on the latent factors. For example, FA has been used to study the
differences in ARs between consumers and designers (Hsu et al., 2000). In the study
of Hsiao and Chen (2006), FA was used to examine the affective dimensions applied
to different kinds of products of differing sizes. In the KE literature, many efforts have
been made to study the consumers’ ARs using FA, such as those by Chuang et al.
2
(2001); Chuang and Ma (2001); Alcantara et al. (2005); You et al. (2006).
To the best of the authors’ knowledge, there is no KE research on extracting
representative affective dimensions in an effective manner. One possible recipe for
this task is to combine cluster analysis (CA) with FA. Based on such a technique,
(Choi and Jun, 2007) proposed a method to determine the adjective clusters needed
for studying the tactile sensation experienced when touching the surface roughness of
commercial polymer-based products. For retrieving critical adjectives as the
measurement scale, the evaluation data obtained from the pilot test without FA
processing can also deal with CA directly (Hsu et al., 2000; Chuang et al., 2001). Due
to the high correlations among adjective evaluation data, it seems better to use feature
extraction methods like FA to remove any redundancy in the data before grouping
adjective into clusters.
Similar to the research topic discussed in this study, the field of sensory analysis
also addresses the need to select representative evaluation scales for measuring the
subjective perceptions of consumers. Sahmer and Qannari (2008) mention several
strategies for selecting a subset of sensory attributes. They mention that the simplest
solution is to choose the attributes with large factor loading (in absolute value) of the
latent factors. This strategy can lead to unsatisfactory results when the attributes with
similar factor loadings appear in the same factors. The second strategy provides an
alternative by applying a clustering technique to the factor loadings, such as the
clustering of variables (COV) method proposed by Vigneau and Qannari (2003). The
attributes can be arranged into homogenous clusters according to their factor loadings.
When applying clustering techniques to latent factors, choosing a suitable number of
clusters is of crucial importance (Sahmer et al., 2006). This strategy is in fact similar
to the technique which combines FA and CA adapted in the study of Choi and Jun
(2007). The third strategy makes use of Procrustes analysis (PA) to select critical
attributes while preserving as much as possible the multivariate structure of the data.
The PA approach for selecting a subset of variables was first introduced by
Krzanowski (1987), which was combined with principal component analysis (PCA) to
process sensory profiling data. The superiority of this approach lies in it providing
direct measurements of the discrepancy between the selected subset and the full set.
Moreover, the standard subset searching approaches, such as forward selection,
backward elimination or stepwise selection, can be easily combined with the PA
approach. In chemometrics, PA has also been used to select informative variables and
to find the correlation among data sets of spectroscopic measurements (Andrade et al.,
2004). The idea of using PA to analyze SD evaluation data was proposed by
Yamamoto et al. (2005). However, their study emphasizes analyzing the differences
among consumers rather than selecting representative affective dimensions.
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In this study, the authors report the potential for PA to select representative
affective dimensions. A KE approach which integrates FA and PA is proposed. The
results of the proposed approach are also compared to the method which combines FA
and a two-stage CA. The reminder of this paper is organized as follows: Section 2
reviews the background of FA, CA, and PA. Section 3 presents the proposed method
for selecting representative affective dimensions. Section 4 demonstrates the results
and discusses the proposed method. Finally, Section 5 gives some brief conclusions.
2. Background review
2.1. Factor analysis for analyzing the semantic differential data
Typically, the SD experiment results in a three-way data matrix in the order of
k  n  m , where k is the number of consumers, n the number of product samples
evaluated and m the number of adjectives on which the samples are evaluated. Note
that each consumer evaluates exactly the same product samples. In this study only a
( n  m ) two-way data matrix, obtained by averaging over k consumers’ ( n  m )
matrices, is considered. The utility of FA as applied to the SD data is to obtain latent
factors and factor loadings of the input affective dimensions. The factor loadings can
then be used to interpret the influence of the affective dimensions on the latent
factors.
2.2. The problem in selecting representative affective dimensions
The factor loadings obtained from FA do not provide direct criteria for selecting
representative affective dimensions. To choose the adjectives with the largest factor
loading in the absolute value of each latent factor seems to be reasonable. It may fail
to select the most representative ones when the adjectives have similar loadings in the
same factors. The most popular way according to the product design literature is to
apply CA to separate the adjectives into homogenous groups based on the similarity
of the factor loadings. The distribution of the adjectives and their local position to
each other are very useful for researchers when studying their relationships. However,
it is doubted that the most representative ones are those which lie in the center of each
cluster. Moreover, when the results of clustering are overlapping and do not have
apparent grouping, it is difficult to determine the most representative affective
dimension.
As another potential tool for selecting representative affective dimensions, PA
has been applied in sensory analysis (Krzanowski, 1987), chemometrics (Andrade et
4
al., 2004), and product design (Yamamoto et al., 2005). Although PA is not so familiar
to the product design community, its interesting properties motivated the authors to
compare and contrast the results from PA and those from CA. In contrast to the
capacity of CA to analyze local relationships of affective dimensions, PA can be used
to analyze the overall structure of the SD data. It provides direct measurements of the
loss of information when deleting certain adjectives from the original SD data. With a
proper process for deleting the adjectives for which their influences to the overall
structure is minimized, it provides the researchers with a “global” analyzing tool in
contrast to the “local” analyzing tool of CA.
2.3. Cluster analysis
This study adopted a two-stage CA, which combines the hierarchical and
non-hierarchical methods used in the study of Chung et al. (2007). Although there are
various algorithms such as K-means clustering, self-organizing maps, and fuzzy
c-mean clustering methods that can be used, these methods do not provide direct
measurement to determine the number of clusters. To determine the suitable number
of clusters we applied hierarchical CA with Ward’s minimum variance method.
Subsequently, the standard K-means clustering method is used to construct
homogenous groups with respect to the factor loadings of the affective dimensions.
For selecting representative affective dimensions, the adjectives whose positions lie
nearest to the center of each cluster are picked up. Therefore, the number of the
selected representative adjectives is equal to the number of clusters, which is
determined by hierarchical CA.
2.4. Procrustes analysis
To select representative affective dimensions, PA is performed on the factor
loadings of the latent factors obtained from the FA. The idea behind PA is to match
the two data matrices as closely as possible by translating, rotating and stretching
them. The loss of information caused by the deletion of some variables can be
measured by the residual sum of squared differences (RSSDs), d , between the two
data matrices W and Z . The RSSD can be calculated as follows (Krzanowski,
1987):
d (W , Z )  trace{WW   ZZ   2} ,
(1)
In Eq. (1), the prime refers to matrix transpose, trace() , the trace operator is the
sum of the diagonal elements of the matrix, and  is the diagonal matrix, obtained
by singular value decomposition (SVD) of the m m squared matrix W Z , as in the
5
following:
(2)
Z W  U V  ,
where U U  I m ,V V  VV   I m . The smaller the value of d , the more similar are
the two data matrices. Therefore, a perfect match of two data matrices gives d  0 .
For detailed deviation of PA, the authors refer to Krzanowski (1987).
In fact, for a simple problem with only 2 or 3-dimensional factors, such as a
formation where each adjective is a coordinate of its factor loadings, the results of PA
can be visually observed. Since the practical usage of FA when applied to analyzing
SD often extracts few factors, for ease of interpretation, the strategy for combining PA
with FA is very useful when studying the global structure of SD data. For an
n-adjectives problem, if the q most important ones are reserved, there are
n!
kinds of adjective combinations to delete. The combination of the
Cqn 
q !(n  q)!
adjectives with the minimized RSSD value yields the most similar structures when
compared to the original. However, in this study, a backward elimination process is
used instead of evaluating large amount of different combinations.
3. The Kansei engineering approach to selecting representative affective
dimensions
This study proposes a KE approach for selecting representative affective
dimensions to aid product design. This approach begins with preparing a set of
representative products. An experiment of SD evaluation is conducted for consumers
to evaluate these product samples using initial affective dimensions. FA is then
applied to extract underlying factors using the full set of initial adjectives. Although
forward selection, backward elimination or stepwise selection can be combined with
PA to select subsets of adjectives, forward selection and stepwise selection lead to
time consuming procedures, as mentioned in the study of Krzanowski (1987). In this
study, a backward elimination process is adopted due to its computationally efficiency
and combined with the criteria of RSSDs calculated by PA for selecting representative
adjectives. A case study of mobile phone design demonstrates the results of the
analysis.
3.1. Selection of representative products
A total of 69 mobile phones of different designs were first collected from the
Taiwan marketplace. In order to reduce the mental effort of subjects and simplify the
experiment process, KJ method (Kawakita, 1986) is adopted to select representative
6
products. Three professional designers, each with at least five years experience, were
then asked to conduct the session of KJ method. Each product sample is printed out
into a card and the characteristics of form and color are described in the back.
Designers discuss the similarities of product samples with each other and group them
into several clusters. Finally, 12 representative products are selected for the following
SD experiment.
3.2. Preparation of initial affective dimensions
In product design research, either single or pairwise adjectives can be used to
describe consumers’ affective dimensions. Since relationships such as relevancy,
dependency, redundancy, cause/effect, and similarity often exist among adjectives,
pairwise adjectives are more suitable for describing consumers’ affective dimensions
(Han and Hong, 2003). Adjectives in each pair have similar concepts but are opposite
to each other. For example, the adjectives “masculine-feminine” is based on the
concept of gender and the two adjectives are polar opposites. This situation is very
common when adjectives are used to describe consumers’ affective dimensions. In
this study, 22 pairwise adjectives (Table 1), adopted from the research of Hsu et al.
(2000), were used as the initial set of affective dimensions.
< Insert Table 1 about here >
3.3. Experiment design for semantic differential evaluation
To collect consumers’ SD data for mobile phone design, 18 subjects, 10 males
and 8 females, were asked to evaluate 12 representative product samples using 22
adjectives, on a scale between -1 to +1. The order of presentation of the products was
randomized to avoid any systematic effects. All of the subjects' evaluation scores for
each product sample were averaged to get a final utility rating. A user-friendly
questionnaire interface is designed, as shown in Fig. 1, to collect the evaluation data
in a more effective way. The evaluation data of each subject could be recorded
directly, thus simplifying the post-processing procedure of the data.
< Insert Fig. 1 about here >
3.4. Analyzing initial affective dimensions using factor analysis
One important issue that affects the usage of FA is the number of factors to be
7
determined. The different numbers of loading factors will vary and influence the
selected affective dimensions. PCA, with varimax orthogonal rotation, was first used
to examine different numbers of factors from 3 to 7. Two criteria, including the
eigenvalues and the percentage of variances explained for all extracted factors, can
then be used to determine the number of factors. First, the extracted factor with an
eigenvalue greater than 1.0 can be retained (Nunnally, 1967). Next, the cumulative
percentage of variance explained for all of the factors can be used to choose a suitable
number of factors. Typically, it should account for at least 60% of the total variance
(Kim and Mueller, 1978).
3.5. Selecting representative affective dimensions based on Procrustes analysis
After calculating the latent factors and the corresponding factor loadings of each
initial affective dimension, PA was used to extract the ( q ) most important adjectives.
The RSSDs d i can be calculated by PA to compare the selected subset and the full
set of the variables, thus preserving the group structure of the data. This study uses a
backward elimination process for searching the subset of adjectives. This process
begins with the complete data matrix L obtained from FA, and subsets of L can be
compared according to the discrepancy value d i , obtained by PA. In each step, the
adjective which causes the least disturbance to the data matrix when it is omitted, is
the one that has the smallest d i . This adjective is removed from the set to leave
m 1 adjectives. This procedure is repeated on the reduced set of variables to find
the one out of m 1 variables that can be eliminated with least disturbance. The
process goes on until only q adjectives remain. These variables will be the best q
adjectives, in the sense that they best capture the structure of all original m
adjectives. In this study, q is set to 1, thus all adjective will be processed. The
complete procedure for selecting representative adjectives is described as follows:
(1) Apply FA to the initial affective dimensions to obtain the matrix of factor loadings
L;
(2) Start with an empty list of adjectives R  [] and the list of selected adjectives
Y  [1,..., m] ;
(3) Repeat until q adjectives are obtained:
(a) Find the adjective a with smallest discrepancy value d a for the adjective
list Y using Eq. (1).
(b) Insert the adjective a into the adjective list R : R  [a, R] ;
(c) Remove the adjective a from the adjective list Y : Y  Y  [a] ;
(4) Output: Ranked adjective list R .
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4. Results and discussions
4.1. Results of factor analysis
FA as a feature extraction method was used to merge similar adjectives from the
original 22 groups and form new groups by choosing a suitable number of factors.
The results of factor loading using three factors are shown in Table 2. The extracted
three factors account for 50.1%, 23.4% and 14.3% of explained-variance, respectively.
Notice that factor 1 accounts for more than half of the percentage of variance. The
total cumulative percentage of variances is 87.8%, indicating that the result of FA
using the three factors is quite acceptable. As the number increases in the factor from
3 to 7, the total cumulative percentage increases are very slightly (less than 5%).
However, the variance of factor 1 still remains around 50% and the variance of the
other factors would become too small (lower than 10%). Consequently, the factor
loadings of three factors were used to extract representative adjectives. For the task to
select representative adjectives, a simple and intuitive way is to pick up the adjectives
with larger absolute factor loadings in each factor. For example, to select adj1, adj6,
and adj3 from Factor 1, adj9, adj8, and adj2 from Factor 2, adj4 and adj5 for Factor 3.
However, either global structure or local relationship of the adjectives is completely
neglected in this manner. The extracted adjectives will locate in the extreme side of
each factor axis.
< Insert Table 2 about here >
4.2. Results of adjective selection process using a two-stage cluster analysis
In order to select representative adjectives using two-stage CA, hierarchical CA
with Ward’s linkage method was first adopted to determine the number of clusters.
Then, K-means clustering is used to group the adjectives into four clusters. In order to
extract one adjective from each adjective group, the squared Euclidean distance was
used to calculate the centroid of each cluster. The adjective with the shortest distance
to the centroid is selected as the representative for the cluster. As shown in Table 3,
the adjectives with the shortest distance to the centroid of each cluster are
unoriginal-creative (Cluster 1), childish-mature (Cluster 2), hard-soft (Cluster 3), and
heavy-handy (Cluster 4). Although CA is a popular method mainly because it is very
intuitive and easy to interpret, it is difficult to decide which adjectives are the most
representative ones when there are several adjectives with similar distances to the
cluster centroid. For example, in cluster one, the distances to the centroid of adj12,
9
adj7, adj16 and adj1 only differ slightly (ranging from 0.088 to 0.099).
< Insert Table 3 about here >
4.3. Results of adjective selection process using Procrustes analysis
After obtaining the factor loadings from FA, the backward elimination process
using PA is conducted to analyze the importance of the adjectives; and determine their
ranking. The relative importance of each adjective can be examined according to the
calculated RSSD values during the elimination process. For example, the RSSD
values calculated in Steps 1, 2, 7, 9, 16, and 21 are shown in Fig. 2. The RSSD value
of each adjective gives intuitive interpretation of their importance. For Step 1, shown
in Fig. 2(a), the RSSD of adj4 (heavy-handy) is the largest which implies that if it
were to be eliminated from the adjective subset, the loss of information would be the
greatest compared to the other adjectives. Adj8 (masculine-feminine) and adj20
(personal-professional) gave medium RSSD values, which means that they are of
moderate importance. In this step, adj17 (plain-gaudy), with the smallest RSSD value,
was eliminated. The result after eliminating adj17 is shown in Fig. 2(b). It can be
observed that adj20 became the most important adjective in this step and adj4 became
the second most important. In Step 7, shown in Fig. 2(c), adj20 remains the most
important; however, in Step 9, shown in Fig. 2(d), the importance of adj4 exceeds that
of adj20. This elimination process of adjectives still remains very stable even when
the number of variables is small (see Figs. 2[e] and 2[f] for Steps 16 and 21). The
RSSD value of each adjective for all the elimination steps is shown in Fig. 3. Notice
that the Y axis is a logarithmic scale (to base 10) for the RSSD value. Since one
adjective is eliminated at each step, the RSSD value of the remaining adjectives in
each step increase gradually. The results of the final adjective ranking obtained by the
backward elimination process of PA are shown in Table 4. The top five of the selected
adjectives are adj21, adj4, adj20, adj6 and adj8.
< Insert Fig. 2 about here >
< Insert Fig. 3 about here >
< Insert Table 4 about here >
4.4. Comparison of the results using Procrustes analysis and cluster analysis
10
Fig. 4 shows the distribution of all adjectives in the space of three extracted
factors for PA. The last nine adjectives in the ranking are marked by an empty circle.
It can be seen that the distribution of adjectives without the last nine adjectives of the
ranking still exhibit similar global structures compared to all the other adjectives. For
example, a dense adjective group located to the right of the factor 1 axis, shown in Fig.
4(a), can preserve its structure after eliminating adj12, adj14, adj15, adj16 and adj17.
The adjectives, say adj5, adj13, adj18 and adj19, located apart from the main structure
were also eliminated in the early steps of the selection process.
Fig. 5 shows the distribution of all adjectives in the factor space for CA. The
centroids of four extracted clusters are marked by a cross (denoted as C1, C2, C3, C4).
In Cluster 1 (colored in pink), the adjectives adj22, adj17, and adj21, which are
departing from the centroid (see Table 3), are less representative for this cluster. The
rest of the adjectives are very crowded, especially on the Factor1-Factor2 plane. The
adjectives in Cluster 2 (colored in orange) and Cluster 3 (colored in blue) scattered
further apart, compared to Cluster 1. However, it is still difficult to determine which
adjectives are more representative when the distances to the centroid are not apparent.
In Cluster 4 (colored in light green), the two adjectives (adj4 and adj18) are both
distantly removed from the centroid. Therefore, K-means clustering, as a CA method,
is more suitable for analyzing the local similarity in cluster groups by examining the
distribution of adjectives in the factor space. Moreover, when adopting CA to analyze
the local relationship for adjectives, a soft clustering method such as fuzzy C-means
clustering seems to be more flexible than a hard clustering method such as K-means
clustering. The circumstances for overlapping clusters of adjectives can be identified
using the membership degree to different clusters obtained from fuzzy C-means
clustering.
The selected results in Figs. 4 and 5 show that the differences between these two
methods. PA can be used to analyze the proper process for deleting the adjectives for
which their influences to the overall structure is minimized. It provides researchers
with a “global” analyzing tool in conjunction with the “local” analyzing tool of CA.
< Insert Fig. 4 about here >
< Insert Fig. 5 about here >
5. Conclusions
In the product design field, questionnaires based on SD experiments are often
used to collect the ARs of consumers to provide industrial designers with better
11
product alternatives. FA as a feature extraction technique is the most frequently used
method for analyzing the SD data obtained from these experiments. However, the
results of FA do not provide direct criteria for selecting representative affective
dimensions. In this study, we propose a KE approach to overcome this FA
shortcoming by introducing a backward elimination process and using the criteria
calculated from PA as a measure of relative importance for the affective dimensions.
In the case study of mobile phone design, the results using the proposed approach
extracted from the initial set of 22 adjectives are compared to that of a two-stage
clustering. This popular recipe combined with FA and CA to analyze the SD data,
which is widely used in the product design field, suffers from the shortcoming that it
is difficult to extract representative adjectives from the clustering results. Although
CA is a useful tool for analyzing the local relationship of adjectives, PA, a technique
which has been widely used in the fields of sensory analysis, has been proven to be a
potential method for extracting representative adjectives. The relative importance of
the adjectives can be examined by calculating RSSD values during the elimination
process. It is very useful in aiding designers to identify a minimum set of adjectives
which reserves the maximum information of the global structure.
Although the proposed methodology which combined FA and PA uses only the
averaged SD data across all subjects, further extensions to deal with the original
three-way SD data is possible by inducing generalized Procrustes analysis (GPA).
Moreover, the effects of product design, such as the form features, color, textures, and
their relations to the selected representative affective dimensions need to be verified
in our future studies. Discussions for using the proposed methodology applied to
different kinds of products, such as consumer electronics, furniture etc., is also of
primary interest to the authors.
12
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15
Fig. 1. A questionnaire interface for SD evaluation.
16
Fig. 2. The RSSD value of adjectives calculated in (a) Step 1, (b) Step 2, (c) Step 7, (d) Step 9, (e) Step 16 and (f) Step 21 during the elimination
process.
(a)
(b)
17
(c)
(d)
18
(e)
(f)
19
Fig. 3. The RSSD value of each adjective for all the elimination steps.
20
Fig. 4. The adjectives exhibited against (a) Factor 1-Factor 2 and (b) Factor 3-Factor 2; the adjectives eliminated in the first 9 steps using
Procrustes analysis are marked in empty circle.
(a)
(b)
21
Fig. 5. Results of the four adjective groups using K-means clustering against (a) Factor 1-Factor 2 and (b) Factor 3-Factor 2; C1, C2, C3, C4
denote the centroid of each cluster.
(a)
(b)
22
Table 1. Twenty-two initial affective dimensions adopted from Hsu et al. (2000).
1 Traditional-modern
2 Hard-soft
3 Old-new
7 Coarse-delicate
8 Masculine-feminine
9 Rational-emotional
4 Heavy-handy
5 Obedient-rebellious
6 Nostalgic-futuristic
10 Hand made-hi tech
11 Childish-mature
12 Unoriginal-creative
13 Simple-complicated
14 Conservative-avant grade
15 Standard-outstanding
19 Inert-active
20 Personal-professional
21 Obtuse-brilliant
16 Common-particular
17 Plain-gaudy
18 Decorative-practical
22 Discordant-harmonious
23
Table 2. The factor loadings of the 22 pairwise adjectives using three factors.
Adjectives
Factor 1
Factor 2
1 Traditional-modern
0.9604
0.1081
0.1455
6 Nostalgic-futuristic
0.9578
0.0377
0.1745
3 Old-new
0.9569
0.1597
0.1359
21 Obtuse-brilliant
0.9428
－0.1315
0.1398
16 Common-particular
0.9298
0.1543
0.2995
0.9074
0.1778
0.1439
15 Standard-outstanding
0.9043
0.1817
0.3262
12 Unoriginal-creative
0.9031
0.2072
0.2348
14 Conservative-avant grade
0.8643
0.1568
0.4557
17 Plain-gaudy
0.8061
0.1189
0.5247
10 Hand made-hi tech
0.7617
－0.3816
0.0575
22 Discordant-harmonious
0.6652
0.1797
－0.2190
11 Childish-mature
0.6225
－0.5119
0.3132
－0.0020
0.9847
－0.0366
8 Masculine-feminine
0.0442
0.9272
－0.2402
2 Hard-soft
0.1522
0.9112
－0.1429
－0.4663
－0.8183
－0.1881
19 Inert-active
0.5607
0.7684
0.0497
20 Personal-professional
0.5576
－0.7044
0.3799
4 Heavy-handy
0.0265
0.1958
－0.8656
5 Obedient-rebellious
0.3139
－0.1468
0.7682
13 Simple-complicated
0.5253
－0.0711
0.7463
Eigenvalue
12.3
5.4
1.7
Percentage of variance
50.1
23.4
14.3
Cumulative percentage
50.1
73.5
87.8
7 Coarse-delicate
9 Rational-emotional
18 Decorative-practical
Factor 3
Final statistics
The bold underlined numbers indicate the groups of adjectives associates with
factors 1-3.
24
Table 3. The Euclidean distance of the adjective to the centroid in each cluster.
Cluster
1
Adjective
12 Unoriginal-creative
7 Coarse-delicate
2
3
0.088
0.091
16 Common-particular
0.099
1 Traditional-modern
0.099
3 Old-new
0.109
6 Nostalgic-futuristic
0.116
15 Standard-outstanding
0.127
14 Conservative-avant grade
0.245
21 Obtuse-brilliant
0.270
17 Plain-gaudy
0.321
22 Discordant-harmonious
0.492
11 Childish-mature
0.215
20 Personal-professional
0.349
13 Simple-complicated
0.415
10 Hand made-hi tech
0.446
5 Obedient-rebellious
0.453
2 Hard-soft
0.064
8 Masculine-feminine
0.209
9 Rational-emotional
0.217
19 Inert-active
4
distance
4 Heavy-handy
18 Decorative-practical
25
0.419
0.621
0.658
Table 4. Results of adjective ranking obtained from PA.
Rank
Adjective
Rank
1
21 Obtuse-brilliant
12
2
4 Heavy-handy
13
Adjective
2 Hard-soft
10 Hand made-hi tech
3
20 Personal-professional
14
5 Obedient-rebellious
4
6 Nostalgic-futuristic
15
16 Common-particular
5
8 Masculine-feminine
16
19 Inert-active
6
1 Traditional-modern
17
14 Conservative-avant grade
7
3 Old-new
18
15 Standard-outstanding
8
9 Rational-emotional
19
13 Simple-complicated
9
22 Discordant-harmonious
20
12 Unoriginal-creative
10
11 Childish-mature
21
18 Decorative-practical
11
7 Coarse-delicate
22
17 Plain-gaudy
26