47
Akungba journal of Management
ATTITUDE MEASUREMENT IN MARKETING: A COMPARATIVE
ANALYSIS OF MULTIDIMENSIONAL AND UNIDIMENSIONAL
SCALLIN6 DEVICES
BY
JACKSON O. OLUJIDE
Department of Business Administration University of Ilorin, Ilorin.
AND
A. LATEEF BADMUS
Department of Business Administration University of Ilorin, llorin.
INTRODUCTION
In the study of consumer behaviour
attitude is considered an important variable.
Thus, when an individual says "I like this
product." "this restaurant is terrible" ... etc. this
individual solemnly expresses his attitude
towards the product or restaurant in question. In
the study of consumer behaviour, attitude is
certainly the variable that has retained and
retains still most attention.
Consequently, very few concepts have
as much- impact on the practice of marketing as
the motives of attitude of consumers towards
products /services. In fact one of the fundamental
objectives of marketing in general and
advertising in particular is to influence consumer
attitude.
Hence, several marketing researchers
have developed the view that understanding
attitude change should become the primary
objective of marketing. For instance, more and
more psychologists have come to the conclusion
that for any advertising campaign to have any
influence on sales it must lead to a positive
change in the attitude of the audience. Thus, the
practitioners of marketing hold the view that
there is a definite relationship between attitude
change towards a brand and the sales of that
brand This view posits a logical conclusion that
rests on a solid foundation.
Bui the problem that arises is that there
are several divergent points of view on this vital
subject—attitude. The possible explanations are:
1.
The problem resides in the measurement
instrument: or
2.
Attitude is not precisely conceived and
defined.
Little & Hill (1967) indicates that the
relationship between attitude and behaviour is as
function of several variables. Thus, it is possible
that the majority of published research work on
the reliability of the measurement instrument.
But in order to validly study consumer
behaviour in marketing it is necessary, most of
the! time, to collect data by means of inquiry or
experimentation. The majority of data thus
collected are of the nature non-metric and often
ordinal (ranks, preferences, degree of importance
etc).
New techniques of analysis that
facilitate the obtention from these ordinal data, a
metric result were developed about 10 years ago.
This development has led to what some people
call the "Multidimensional Revolution"
The appearance of these multivariable
techniques of data analysis has completely put in
the dark unidimensional scales of measurement
which hitherto now used, essentially a non metric level (Likert. Guttmaa Semantic
differential etc).
The objective of this paper therefore is
to undertake a comparative analysis of
multidimensional and unidimensional scales of
measurement.; This will be done by applying
these two methods to the measurement of
students' attitudes to the marketing of students
restauration service This comparative study is
intended as a means of ascertaining
Akungba journal of Management
the possibility of convergence of results from the
two methods.
A multidimensional analysis—factor
analysis—is used to analyse the data on the first
part of the questionnaire. In the second part. 12
salient attributes of the restauration service were
identified during a session discussion in which
non-directed questions were posed initially to 20
students chosen randomly.
These attributes were presented to another group of 20 students selected randomly for
the pre-test of questionnaire and to another group
of 150 students selected randomly to arrange the
attributes in order of importance by numbering
them as follows:
1.
For the first attribute that seems to be
the most important.
2.
For the second
3.
For the third and so on until 12, for the
attribute the least important. An interval scale
was derived from this Thurstone Scale.
RESULT: Multidimensional Analysis
•
Method of Multivariate data analysis
are divided into 2 classes.
•
Descriptive methods and
•
Explicative methods
Factor analysis used in this study is a descriptive
method and applies in the following areas:
•
To simplify, and summarize data
described by a minimum number of
non-correlated factors
•
To visualize the data, in the form of a
chart
•
To indicate the interdependence among
variables
•
To regroup the variables that are
closest.
Factor analysis consists of 2 models:
48
•
Principal component analysis
•
Common factor model for nominal and
ordinal data
In our case, we have metric results and
therefore we apply principal component analysis.
Factor analysis on the Totality of the
Variables: Correlation Matrice of the
Variables
There is a correlation matrice of 18
variable (see computer print-out). The first
variable is excluded because it does not measure
an attitude with respect to restaurant service.
However, responses to this question permit us to
know if the productive capacity of university
restaurant is well utilized by students.
Cij is the correlation between variable i and variable j;
—
for Cij = 1, and J are perfectly correlated
and vary in the same direction.
—
for Cij = -I, the correlations are perfect
but in an inverse direction.
—
for Cij = 0, the variables are totally independent
Our objective here is to replace the variables
with factors, translated by:
—
a smaller number of factors
—
minimizing the loss of information and to
verify if the principal factors, in a rank order (in
terms of explained variance) attach the same
meanings to the same variables as the Thurstone
method.
We now undertake a linear correlation
of the initial variables and to do this we identify
the variables that are highly correlated and
replace them with a small number of variables
without loss of information.
Considering variables with a correlation
coefficient +- 0.5 we have the following:
Akungba journal of Management
49
TABLE 1:
Correlation between the Variables (+0.5)
Variables
4
7
10
11
12
14
4
7
1.00 1.00
1.050652
10
1.00
1.5:56
0.54639
0.58206
11
12
14
0.50752
0.58206
0.53560
054639
1.00
0.53187
0.53187
1.00
4.00
We observed that the correlations are strong
between variables measuring the following:
(a)
4: rapid service
14:
quality/price
(b)
7:
Useful information
14:
quality / price
(c)
10:
Taste of food
11:
Nutrition
(d)
10:
Taste of food
12:
Variety of food served
(e)
11:
Food
12:
Variety of food served
From this analysis we note that
variables 10, 11 and 12 express ideas that are
very similar. We therefore represent them by
variable 11 that has a meaning that is more
general such trial it could take into consideration
the views expressed by variables 10 and 12. But
when a new factor analysis without variables 10
and 12 was undertaken, the result obtained did
not improve. We therefore decided to retain the
3- variables.
To resolve the number of factors problem where the objective is to find the minimum
number of factors compatible with the data, we
applied one of the most popular criteria for addressing this issue. This criterion is to retain factors with engenvalues greater than 1 when the
correlation (not adjusted) is decomposed. This
criterion seems to work well, in the sense that it
generally gives results consistent with
researcher's expectation and it works well when
-
applied to samples from artificially created
population models. Kaiser (1974) provides
several reasons for the success of this criterion.
He says that its acceptance is still based on
heuristic and practical grounds.
In the first analysis, five factors were
obtained on the basis of engenvalues greater than
1 Translating the explained variance of the 5
factor in percentage, we have the following:
•
1st factor represents 24.83% of the
information (explained variance)
•
2nd factor 14.48%
•
3rd factor 10.64%
•
4th factor 7.37%
•
5th factor 6.65%
The 5 factors explained 63.57% of the
total variation and employing therefore the test
of Kaiser (see figure 1 below) we decided to
retain the first 3 factors that appear to us to be
the most important as these have engenvalues
equal to or greater than 1 (see computer result of
engenvalues) . The rationale behind this citerion
is that in a population matrix it provides a strict
lower-bound for the number of common factors
responsible for the data. But the same may not
necessarily apply to the sample correlation
matrix and application to empirical data usually
produces more factors than normally accepted on
other grounds.
On the basis of the decision to retain 3
factors, a new analysis with the 3 factors was undertaken.
Akungba journal of Management
ANALYSIS OF THE FACTORS F1 F2 F3
Factor 1 is the most important of the 3
because it represents about 47.43% of the total
explained variance; the 2nd factor 27.71% while
the 3rd explained 21.2%. The three factors explained 40.506%.
From the table of principal axes
solution (3 factors) (see computer print out) price
(economy), rapid service and the quality /price
ratio (they have positive coefficient) are represented on the first axe. They represent the
subjective attributes that appear to us as the most
dominant in this axe.
Factor 2 regroups attributes like taste,
variety of food etc. which have negative coefficients. The variables oppose cleanliness, appropriate opening hours and increase in price.
The 3rd factor regroups the variable "location"
and accessibility.
Figure 2 shows the first 2 principal axes.
UNIDIMENSIONAL ANALYSIS
Several methods of measurement employed by
researchers in marketing are application of the
judgement
approach.
Tongerson
(1958)
classified them into two:
—
quantitative methods
—
variability methods
The 2 categories are relative to the manner of the subject's placement or stimuli on a
chosen dimension. In general, the quantitative
judgement and the variability approaches do not
present important differences with respect to the
manner of obtaining the unit of measurement.
We shall use Thurstone law of
comparative judgement which is a method of
variability. Thurstone's law of comparative
judgement is chosen because it has been more or
less totally neglected inspite of its numerous
advantages.
Thurstone's law of comparative
judgement consists, essentially, of the definition
of an interval scale from comparative
50
judgements of the type: "A is better than B, A is
bigger than B, A is more beautiful than B etc.....
In the optic of our previous classification, this
method is grouped with those founded on the
variability of judgement approach.
This technique which would require
each subject to make 10 comparisons according
to a single dimension. If comparison by two
dimensions were required 20 paired comparisons
would be necessary.
Similarly the psychological distance between
two stimuli S and C. is based on a normal curve
transformation of the proportion of respondents
who judge Sj, greater than Cg. In brief, an
interval scale is derived from paired comparisons
in the following manner: the numbers of respondents selecting one stimulus over another is an N
by M matrix. These frequencies are then converted into proportions in which the main
diagonal in the matrix of proportions has
approximately 0.5 in each cell. In other words, if
Sj were compared with Cg approximately half the
respondents would report that S was greater and
half the respondents would report that S was less
than C, which is to say that they are equal. All
other cell entries be greater or less than 0.5.
thereby reflecting a perceived psychological
distance between two stimuli.
Expressing this mathematically we hare:
P (j < g) = P(Sj < Cg) = P(Cg- Sj) < 0
= P (Dig < 0) if we let Cg - S, = D^
This difference is a normal random
variable and transforming this in a reduced
normal variable (tjg)
= normal variable Djg - its mean (Xp_) its
standard deviation (Dy)
Looking at the following probability
Pjg = P (tjg > - XJg
D*
In effect (DJg =0) implies that
o* > -x*)
DJg
Representing this graphically
Akungba journal of Management
We now apply the developments in the
previous page to our data which represent the
responses of students to the significance and importance of attributes of university restauration.
This involved comparing the 12 attributes of the
University restauration service in order of importance vis - a-vis a series of criteria that are likely
to be considered in the purchase decision. The
calculations and transformations to interval scale
are developed here after. Table 2 shows for example that 5% of respondents considered that
attribute 2 (courteous and experienced personnel)
51
is more important than attribute 1 (rapid service)
and that others, about 95% consider 1 more
important. Next we calculate on the basis of the
distribution function of the normal deviates
corresponding to cumulated proportions.
These appear in table 3.
Thurstone's
law of categorical judgment is
established as follows:
Cg – Sj = Yjg Sj2 + Cg2 - 2jg Sj Cg
Where
Yjg = Value obtained by consultation with table
Akungba journal of Management
52
of the normal deviate
Sj = standard deviation of the value attributed to
stimulus j
Cg = standard deviation of value attributed to
stimulus j and the index of category g
To identify the parameters of our scale (i.
e. Cg, g = 1,2.........M and Sj = 1, 2,
......n) we mast introduce classic but simple hypotheses with a view to reducing the number of
unknowns, which in our case, is greater than the
number of equations. Thurstone assumes that the
deviation of values attributed to the stimuli and
the index of category are constants and that the
correlation coefficients between the values attributed to the stimuli and those attributed to the index of category are identical. From here the system of equations is simplified and it becomes:
Cg - Sj = YJg
We fix k = 1 because we can choose
arbitrarily the unit with which we work, and we
now have Cg - Sj =. Yjg. We now look for the
estimates of the values of the scale of n stimuli
(that is to say, in our example 12 attributes of
university restauration service) and M index of
categories that minimize the sum of squares of
the distances between the observed Yjg and the
adjusted Yjg
We can therefore locate the category indices and calculate the scale values of the stimuli
obtained with the help of the following formulae.
Ck = 1 Σn Yjk
n j=l
The results are as follows:
Thurstone Scale Analysis Unidimensional
(Thurstone case 5 model
Importance of Attributes
A.
Much Importance
Price.........................0.814
Nutrition....................0.804
Rapid Service..............0.660
B.
Average Importance
Respond to need of
Appropriate hours......0.240
C.
Low Importance
Clean Environment.....0.000
Location...................0.2%
Likert Scale Analysis Multidimensional (Factor
Analysis)
1st Factor: Explains 47.43% of variance
Σn Yjg + 1
Σn
Σn Yjg
g=l
m+n g= 1 j=l
These value were calculated for the example considered and are presented in figure 3
which has enabled us position each of the attributes of the restauration service with reference to
the criteria: significance and importance.
S1 = 1
m
Comparative Analysis of the result of the Two
Scales of Measurement
The two approaches for measuring
affect, preference and identification of attribute
that are characteristic of student attributes
regarding university restauaration services give
almost the same result in spite of differences in
the methods.
Variables Factor Score price 0.76607
Rapid Service 0.75880
Quality/Price 0.74129
2nd Factor: Explains 27.71% of variance
Clientele.......0.593 Appropriate hours 0 68078
Nutritious food
0.47929
3rd factor: Explains 21.2% of variance
Meeting
0.68160
Arrangement 0.56024
Courteous & Experienced
Personnel
0.48417
53
Akungba journal of Management
The results of our analysis show that the
multidimensional and undimensional approaches
to measuring students' attitude toward university
restauration service emphasize to a large extent,
subjective characteristics such as economy and
rapidity of service. These variables are contained
in the first factor on the multidimensional scale
and on A (attributes with much importance) on
the undimensional scale.
(See figures 1 & 3).
We have therefore show in this paper
that the undimensional approach to attitude
measurement could complement the more recent
multidimensional scale of measurement. It is
therefore not circumspect to have abandoned
urridimen-sional scale of attitude measurement.
Equally important is the fact that by using well
known models such as Thurstone law of
categorical judgement, we have been able to
develop an algorithm that facilitates the passage
from a non-metric to a metric scale. This passage
is particularly rich in complementary
information.
We should however, not be too carried
away by the spectacular result of the
undimensional scaling measures because new
trends in attitudinal research are towards
multidimensionality. multivariate, non metric
analysis, more complete models of the decision
process and more efficient means for testing
hypotheses and developing new models. What
we need are model are more than single
attitudinal equations, complete systems of
equations that will buying behaviors to
demographic, economical and psychological
variables. Also needed models that reflect the
marry dimensions of hour. At the present time,
behaviour is regard isolated forms - buying,
visiting a dealer, advertisements, talking to
friends which an actually related forms of
behaviour. Further behaviour is defined in
undimensional terms more powerful insights will
be possible when attitudes can be mapped into
multidimensional haviour.
In conclusion, what we have done in
paper is to apply undimensional and
multidimensional scaling devices to describe or
diagnosed needs of the market for the
restauration very for university students. Thus
the main criticizing attitude scale, their inability
to predict behaviour may seem to be misplaced
here inability to stems less from the limitations
of scaling devices but more from the role of
attitudes in the decision process. We do not have
models that describe adequately the relationships
among changes in behaviour attitudes, group
membership, roles and systems. Thus, this
criticism applies to the lack models rather than
measurement scale.
Figure3.
IMPORTANCE OF ATTRIBUTES (Thurstone
Case 5 Model)
THE LAW OF COMPARATIVE JUDGEMENT
Much importance
0.814 Price
0.804 Nutrition
0.660 Rapid Service
0.64
0.593 Respond to the need of the client
Average Importance
0.240 Appropriate opening hours
0.01
0.00 Clean Environment
Little Importance
-0.2% Appropriate location
-0.456 Adequate space and arrangement of chairs and tables.
0.62
-0.685 Experienced and courteous personnel
No importance
-0.845 Supplementary service (e.g. oven extra food
-1.098 Useful Information.
etc).
Akungba journal of Management
BIBLIOGRAPHY
ANDREASEN, A. R.: Attitudes and Customer
Behaviour. A decision Model, in L. E.
Prestoa ed. New Research in Marketing.
Berkley: Institute of Business and
Economic Research. University of
California. 1965
BEM. J.D.. Beliefs. Attitudes, and Human
Affairs.
Behnont.
California.
Wadsworth 1970.
DAY, G. S. Buyer Attitude and Brand Choice
Behaviour. New York: The Free Press
1970.
EDWARDS, A. L. Techniques of Attitude Scale
Construction. New York: AppletonCentury- Crofts. 1957.
FISHBIEN, M.,: A Behaviour Theory Approach
to the Relations between Beliefs about
an Object and the Altitude toward the
54
Object. Fishbein. M, ed Readings in
Attitude Theory and Measurement, New
York: Wiley. 1967.
GREEN. P. & TULL, D.: Research for
Marketing Decisions. Prentice-Hall,
1970.
GREEN. P. E. & CARMONE, F. J.:
Multidimensional Scaling and related
Techniques. Boston: A. Ally & Bacon
1970.
GUTTMAN. L.. The problem of Attitude and
Opinion Measurement, in S.A. Stouffer
ed.. Measurement and Prediction;
Princeton University Press 1950.
HUGHES. CD: Attitude Measurement for
Marketing Strategies: Glenview, III,
Scott. Foresman 1971.
Akungba journal of Management
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Akungba journal of Management
TABLE 2
Observed ratios in cases where attributes (=quality of service) X is indicated in A
and attribute Y is shown in the first column starting from
A
B
C
D
E
F
G
H
1
J
K
L
A
000
-1.65
-1.65
4>.67
-0.30
-0.73
0.21
0.39
-1.19
-2.12
-0.78
-1.82
B
1.65
000
0.62
1.04
1.04
0.28
1.28
1.19
0.17
-0.62
048
41.30
C
1.65
-0.62
000
0.90
1.3S
-1.84
1.13
1.04
-0.30
•0.93
1.50
-0.73
D
0.67
-1.04
-0.90
000
0.57
-1.11
0.57
0.73
-0.73
-1.50 ,
-0.34
-1.28
E
0.30
-1.04
-1.39
-0.57
000
-2.12
043
0.39
-0.95
-1.50
-0.78
-1.28
F
0.73
-0.28
1.04
1.11
2.12
000
2.12
1.84
0.30
-0.34
0.53
4.43
G
-0.21
-1.28
-1.13
-0.57
•0.43
-2.12
000
•0.13
-1.04
-1.84
0.78
-0.65
H
-0.39
-1.19
-1.04
-0.73
-0.39
-1.84
0.13
000
-1.39
-1.39
-1.28
-1.50
1
1.19
-0.17
0.30
-0.73
0.95
•0.30
1.04
1.39
000
-0.67
0.30
4.73
J
2.12
0.62
0.95
1.50
1.50
0.34
1.84
1.39
0.67
000
1.11
0.30
K
8.78
•043
-1.50
0.34
0.73
-0.53
0.78
1.28
-0.30
-1.11
000
-1.50
L
1.28
0.30
0.73
1.28
1.28
0.43
1.65
1.50
0.73
0.30
1.50
000
SOMME 9.77
-6.78
-3.97
4.36
846
8.74
11.19
11.01
-4.14
12.34
146
-10.38
Z
0.814
-0.565
-0.331
0.363
0.705
0.728
.933
0.918
-0.345
-1.028
0.122
-0.865
R
0.692
-0.687
-0453
0.241
0.583
0.606
0.811
0.796
-0467
-1.15
0.00
-0.987
TABLE 3
Value related to Preference Proportions as indicated in the table
A
B
C
D
E
F
G
H
I
J
K
L
A
0.000
0305
0.505
0350
0.383
0.233
0383
0.650
0.117
0.017
0.217
0.100
B
0.950
0300
0.733
0350
0350
0.617
0300
0.883
0.567
4.267
0.667
.0.383
C
0350
0.267
0300
0317
0317
0.150
0367
0350
0.677
0.167
0333
0.233
D
0.750
0.150
0.183
0.000
0.717
0.133
0.717
0.783
0.233
0367
0367
0.100
E
0317
0.150
0.083
0.283
0.000
0.017
0.667
0350
0.167
0.867
0.233
0.100
F
0.767
0323
0350
0367
0383
0.000
0383
0367
0317
0307
0.700
0333
G
0417
0.100
0.133
0.283
0333
0.017
0300
0460
0.150
0333
0.217
0360
H
0360
0.117
0.150
0.233
0350
0.033
0.550
0.000
03*3
0.0*3
0.100
0357
I
0383
0433
0317
0.767
0313
0.383
0350
0317
0.000
0320
0.617
0.233
J
0383
0.733
0333
0333
0333
0.633
0367
0317
0.7S0 0333
0357
0353
K
0.783
0333
0367
0333
0.783
0300
0.743
0300
0373
0.133
0.050
0367
L
0300
0.617
0.767
0300
0300
0.667
0350
0.883
0.767
0450
0333
0.000