Cluster Analysis Assignment 2013-2014(2)

MKT 3004 Analytical Techniques for Marketing
Assignment 2: Cluster Analysis
Name: Tiezheng Yuan
Student Number: 110562836
Degree Title: NN52 Marketing and Management
Word Count: 2618

Table of Contents
Section number and title Page
1. Introduction 1
2. Theory 2
3. Application to Marketing 5
4. Method 7
5. Results 8
6. Marketing Implications 13
7. Summary 16
List of References 18
Appendices
Appendix 1. Saved Factor Scores 19
Appendix 2. Preliminary Hierarchical Cluster Analysis 20
Appendix 3. Proportion of Each Cluster 21
Appendix 4. SPSS Output Chi-Square tests 21
List of Tables
Table 1. ANOVA Table for Three-Cluster Solution 8
Table 2. Average FactorScores for Final Cluster Centres 9
Table 3. Cluster Profiles base on interpretation
of Final Cluster Centres 9
Table 4. Summary of Tests for Cluster Identity and Shopping
Behavioural Characteristics 10
Table 5. Summary of Cluster Profiles 11

Name: Tiezheng Yuan
Student No: 110562836
1
1. Introduction
The aim of this study concerns the application of cluster analysis to data in the form
of factor scores that measure the importance that students attach to supermarket
features representing dimensions of Economy, Payment system, Range and quality of
products, Friendly staff, and Accessibility.
Cluster analysis is a technique concerned with grouping objects on the basis of
numerical measures that reflect characteristics of interest of the objects. Cluster
analysis is commonly used to segment consumers. The application of cluster analysis
to the student data facilitates the segmentation of student shoppers on the basis of the
importance they attach to supermarket store features. Therefore, improving the
understanding of student food shoppers and allowing better marketing strategies to be
devised, targeting students.
This study is structured as follows, Section 2 explains the theoretical aspects of the
cluster analysis. Next, Section 3 will present a review of a research paper on the
application of cluster analysis. Following this, Section 4 will describe the method
used to conduct cluster analysis. Section 5 will present the result of the analysis.
Section 6 will mention the marketing implications of results. Lastly, Section 7 will
present the summary and conclusion of the study.

Name: Tiezheng Yuan
2
2. Theory
This section aims to explain the theoretical aspect of cluster analysis by describing the
objectives of the technique, the data requirement, various techniques of distance
measurement and lastly explanation of main theoretical approaches which includes
both hierarchical and optimisation techniques.
The aim of cluster analysis is to group objects on the basis of numerical measures of
the objects.
Cluster analysis is concerned with deciding the number of clusters, identification of
the membership of each group and profiling the characteristic of each group in terms
of behaviour, attitudes and characteristics.
The criteria that are used to form the clusters are that objects within a group should be
as similar as possible, therefore, variance within group should be as small as possible.
Objects belonging to different groups should be as dissimilar as possible, therefore,
variance between groups should be as large as possible.
The basic data for cluster analysis are presented as a standard data matrix either as
original variables or factor scores. The data are then transformed into measures of the
closeness of the objects. The method of transformation depends on the measurement
properties, non-metric data uses similarity measures and metric data uses distance
measures.
In order to perform cluster analysis, object to object distance and group to group
distance needs to be considered. Typically, object to object distance is measured by
using Euclidean distance. However, other techniques such as City block metric and
Mahalanobis distance could also be used to measure object to object distance.
Group to group distance is most commonly measured using Between groups linkage.
However, other techniques such as Within groups linkage, Nearest neighbour,
Furthest neighbour, Centroid method, Median method and Ward’s method could also
be used to measure group to group distance.

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3
The two broad types of clustering techniques are hierarchical clustering and
optimisation clustering.
Hierarchical clustering is a sequential process that adopts a systematic approach to
establish a range of clusters. It proceeds as a series of stages where at each successive
stage there is one less cluster than at the previous stage. It begins with as many
clusters as there are objects and on completion, there is a single cluster of all objects.
The technique employs the information in a distance matrix and at any stage, merger
takes place between the objects that are nearest (similar) to each other so that at each
stage the number of clusters is reduced by one. The researcher has to decide the
appropriate number of clusters using information from the agglomeration schedule,
dendrogram and Gower diagram.
The optimisation approach groups objects into a pre-specified number of clusters
relative to an objective. It involves 2 stages, the first stage there is an initial grouping
of data and the second stage involves the application of a clustering criterion to reach
a final solution. From initial to final stages relocation of objects may occur.
Initial grouping forms cluster centres, which are specified by the researcher or,
generated from a random selection of centres from the data. The values of the
variables define coordinates which are the cluster centres.
Subsequently, in the second stage the clustering criterion is applied to find a better
solution than the initial stage. As a result, relocation may occur, merging clusters that
are close together, splitting up those which are very large. After initial grouping and
with subsequent relocation of objects, the cluster centres may change.

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The relocation criterion is usually based on the relationship between total variances,
within group variance and between group variance. The group data variance is
defined by the identity:
T = W + B
pxp pxp pxp
T = Total variance/covariance matrix for the pooled data
W = Within group variance and covariance
B = Between group variance and covariance
T is fixed for given data, whilst W and B vary according to location/relocation
decisions. Thus, relocation criteria either minimises W or Maximises B. Due to the
relationship between T, W and B, minimizing W automatically maximizes B.

Name: Tiezheng Yuan
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3. Application to Marketing
This section aims to explain and evaluate the practical application of cluster analysis
through reviewing the study conducted by Sung and Jeon (2009). It will explain the
aim of the study, provide description of data and measures, explain the result and
interpretation, mention the value of study and lastly critic the study.
The study conducted by Sung and Jeon (2009) aim to classify internet users by
fashion lifestyles, to profile the demographic and internet usage characteristics of each
segment, and to examine evaluation for fashion e-retailers’ attributes.
The measure consists of a 21-item scale designed to measure fashion lifestyle of
Korean online shoppers. A five-point Likert scale with anchors of 1 - strongly
disagree to 5 - strongly agree were used.
Factor analysis was applied to the original variables, resulting in five factors which
were defined respectively as ‘Fashion consciousness’, ‘Shopping enjoyment’, ‘Brand
consciousness’, ‘Personality pursuit’ and ‘Economical orientation’.
Factor scores from the five lifestyle factors were used to conduct cluster analysis to
identify market segments. Five clusters were identified through the study. The first
cluster comprising 19.6 percent showed the second highest level of personality and
economical perspectives, but did not care about fashion or brand names, and do not
enjoy shopping at all. It is therefore interpreted as Economical shopper.
Cluster 2 comprising 17.7 percent showed the highest levels of shopping enjoyment
and economical orientation. This group also enjoy shopping for fashion products,
considered values for money, but also considered well-known brands. It is therefore
interpreted as Recreational shopper.
Cluster 3 comprising 20.6 percent showed the highest level of fashion and brand
consciousness, but less cared about shopping enjoyment or economical orientation. It
is therefore interpreted as Fashion/Brand shoppers.

Name: Tiezheng Yuan
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Cluster 4 comprising 23.6 percent showed the second highest levels of fashion, brand
consciousness and shopping enjoyment, but lowest in personality and economical
orientation. It is therefore interpreted as Fashion follower.
Cluster 5 comprising 18.5 percent displayed the highest level of personality and brand
consciousness, but did not care about practicality or fashion. This cluster also had
neither desire for fashion leadership nor any interest in fashion, but cared about
personality and well-dressed appearance. It is therefore interpreted as Individualistic
shopper.
The result of the study contributes to the extant literature by improving the
understanding of Korean online fashion shoppers. Besides that, the study also offers
valuable recommendations to apparel e-retailers in Korea based on characteristics of
each segment. For example, the study characterise Economical shopper segment as
typical, practical online users. Therefore, e-retailers are recommended to sell basic
items at valued prices rather than trendy, well-known brands are also appropriate. The
study also recommends e-retailers to reinforce after service attributes and price-
related promotions to enhance purchase from Economical shopper segment. The
recommendations given by the study allows e-retailers to develop better marketing
strategies, targeting different segments.
There were some limitations of the study conducted by Sung and Jeon (2009). Firstly,
the quality of the data collected is questionable using web survey (Fricker and
Schonlau, 2002). There were about 30 percent of respondents in this study evaluates
e-retailers’ attributes without having past purchase experiences (Sung and Jeon,
2009). These respondents evaluate the web site performances based on indirect
experiences from other product categories. Therefore, the interpreting of findings may
not be accurate when respondents do not have the relevant experience (Forth et al.,
2010). Secondly, the instrument used in the study conducted by Sung and Jeon (2009)
was modified based on previous studies. Therefore, there is a possibility that some
elements of fashion lifestyles associated with apparel purchasing behaviours may not
have been captured.

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4. Method
This section aims to explain and justify the method used by providing explanation of
data and measures, provide explanation of method used in particular hierarchical and
optimisation techniques and also provide justification for technique used.
The data consist of 14-item scale designed to measure the importance of supermarket
features (1 = Not at all important, 5 = Very important).
were defined respectively as ‘Economy’, ‘Payment systems’, ‘Range and quality of
products’, ‘Friendly staff’ and ‘Accessibility’. Factor scores were saved for cluster
analysis (See Appendix 1).
Cluster analysis was applied as a two-stage process to the five factor scores. In the
first stage, a hierarchical analysis was employed using the average linkage method to
provide an indication of the appropriate number of clusters. Information from the
agglomeration schedule in Appendix 2 suggested that the solution range from five-
cluster solution to two-cluster solution.
Consideration of relative cluster size, ANOVA and the desire for simplicity
(parsimony) led to the choice of a three-cluster solution. Subsequently, in the second
stage, the K-Means optimisation method was employed to derive a solution with the
specified number of clusters. A nominal cluster identity variable was saved for profile
analysis.

Name: Tiezheng Yuan
8
5. Results
This section aims to present the results in a logical structure. Firstly, the cluster
analysis results will be presented. Secondly, the cluster profiles in terms of average
factor scores and behaviour shopping variables will be explained. Lastly, summary
table of profiles will be presented.
The cluster analysis results are presented as follows. Cluster analysis was applied to
the factor scores for the factors ‘Economy’, ‘Payment systems’, ‘Range and quality of
products’, ‘Friendly staff’ and ‘Accessibility’. The analysis established three clusters
comprising approximately 27 percent (Cluster 1), 44 percent (Cluster 2) and 29
percent (Cluster 3) of the student body (See Appendix 3). The nominal cluster identity
variable provides detail on individual student membership.
Table 1. ANOVA Table for Three-Cluster Solution
Factor Cluster Error F Sig.
Mean Square df Mean Square df
Economy 145.807 2 .589 705 247.467 .000
Payment systems 53.284 2 .852 705 62.564 .000
Range and quality of
products
63.507 2 .823 705 77.195 .000
Friendly staff 110.464 2 .689 705 160.217 .000
Accessibility 22.137 2 .940 705 23.549 .000
Table 1 presents the results of a descriptive ANOVA that tests the null hypothesis that
the average factor scores for the three clusters are equal against the alternative
hypothesis that they are not equal. Assuming a significance level of 5% (.050) the
significance statistic (Sig) indicates that the null hypothesis is rejected for ‘Economy’
(F(2, 705) = 247.467, Sig = .000), ‘Payment systems’ (F(2, 705) = 62.564, Sig =
.000), ‘Range and quality of products’ (F(2, 705) = 77.195, Sig = .000), ‘Friendly
staff’ (F(2, 705) = 160.217, Sig. = .000) and ‘Accessibility’ (F(2, 705) = 23.549, Sig.
= .000). Hence the results of the test confirm that the clusters are distinct. Next, the
cluster profiles will be explained using average factor scores for final cluster centres.

Name: Tiezheng Yuan
9
Table 2. Average Factor Scores for Final Cluster Centres
Factor
Cluster
1 2 3
Economy -1.05774 .44846 .28904
Payment systems .40466 .13830 -.58140
products
.42505 .16458 -.64004
Friendly staff -.27364 .60530 -.66864
Accessibility .17900 -.27888 .25951
The interpretation of results (Table 2) is presented in Table 3.
Table 3. Cluster Profiles base on interpretation of Final Cluster Centres
Next, the cluster profiles will be explained using nominal measures of shopping
behaviour.
Lower than average
importance within
cluster
Higher than average
importance within
cluster
Least important
within cluster
Most important
within cluster
Least emphasis
among clusters
Highest emphasis
among clusters
Cluster 1 - economy
- friendly staff
- payment system
- range and quality of
products
- accessibility
economy range and quality of
products
economy - payment system
products
Cluster 2 - accessibility - friendly staff
- economy
- payment system
products
accessibility friendly staff accessibility - economy
- friendly staff
Cluster 3 - payment system
products
- friendly staff
- economy
- accessibility
friendly staff economy - payment system
products
- friendly staff
accessibility
Factors
Cluster Profiles

Name: Tiezheng Yuan
10
The behavioural is based upon nominal shopping behaviour measures. The statistical
analysis is based upon a chi-square contingency test. The hypotheses are:
H0: The nominal cluster identity variable and the profile variable are
independent
H1: The nominal cluster identity variable and the profile variable are not
independent (are associated).
A summary of the results of the tests (See Appendix 4) is provided in Table 4. From
the table it is evident that at the five percent significance level, there are significant
differences between clusters with respect to storecard ownership, use of budget and
weekly expenditure. Therefore, the null hypothesis is rejected.
Table 4. Summary of Tests for Cluster Identity and Shopping Behavioural
Characteristics
Behavioural
Characteristic
Chi-square Statistic and
Significance
Null
Hypothesis
Storecard Ownership 2 (2)= 12.387, Sig = 0.002 Reject
Use of Budget 2 (2)= 34.602, Sig = 0.000 Reject
Weekly Expenditure 2 (4)= 28.595, Sig = 0.000 Reject
Storecard Ownership
Cluster 1 typically owns a storecard but with a more even storecard ownership
balance (Yes = 50.8%, No = 49.2%) and it is the least amongst the 3 clusters. Cluster
2 also indicates that a majority owns a storecard but it is the highest amongst the 3
clusters with 66.2% owning one. Cluster 3 indicates that a majority (59.0%) owns a
storecard. However, Cluster 3 has fewer members owning a storecard compared to
cluster 2 but has more compared to Cluster 1.

Name: Tiezheng Yuan
11
Use of Food Budget
Cluster 1 indicates that majority (77.1%) do not use food budget and it has the highest
percentage of not using food budget amongst the 3 clusters. Cluster 2 indicates that
majority (58.5%) do not use food budget however it has fewer not using food budget
compared to Cluster 1. Cluster 3 indicates a marginal majority (51.5%) use food
budget and it has the lowest percentage (48.5%) that do not use food budget amongst
the 3 clusters.
Weekly Expenditure on Food
Cluster 1 typically spend £16-30 but 80.9% spend at least £16 (£16-30 = 59.6%, >£31
= 21.3%). Cluster 2 typically spend £16-30 but 85.3% spend no more than £30 (£0-15
= 37.6%, £16-30 = 47.7%). Similarly Cluster 3 typically spend £16-30 but 91.2%
spend no more than £30 (£0-15 = 39.0%, £16-30 = 52.2%).
Next, the summary table of cluster profile will be presented.

Name: Tiezheng Yuan
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Table 5. Summary of Cluster Profiles
Profile Cluster 1 (27%) Cluster 2 (44%) Cluster 3 (29%)
Descriptive label Rich, quality seeking Price and value, experience
seeking
Budget conscious, convenient
seeking
Importance of store features factors
Economy Least important Most importance Some importance
Payment systems Most important Some importance Least importance
Range and Quality of Products Most important Some importance Least importance
Friendly Staff Less important Most important Least importance
Accessibility Some importance Least important Most important
Shopping behaviour measures:
Storecard Ownership Least ownership Most ownership Some ownership
Use of Budget Least use Some use Most use
Weekly Expenditure Medium to high spenders Low to medium spenders Low to medium spenders

Name: Tiezheng Yuan
13
6. Marketing Implications of Results
This section aims to apply strategic and tactical marketing theory to the results
through the use of Segmentation, Targeting and Positioning (STP) framework.
Implications will be made based on the characteristics of the clusters using marketing
mix 7ps. The 7ps are Product, Price, Place, Promotion, People, Process and Physical
evidence.
Cluster 1
Cluster 1 comprising 27 percent of student body is interpreted as rich and quality
seeking.
Product
Supermarkets could focus on ensuring a wide range of high quality products as range
and quality is most valued in this segment. Supermarkets could also engage more in
purchasing well-known brands from suppliers which have good reputation in product
quality. Besides that, retailers could communicate clearly with its suppliers regarding
quality standards. Furthermore, customer feedback on quality could be conducted by
supermarkets as a way to monitor quality and satisfaction. In addition, internal
(employee) feedback on quality could also be encouraged. Maintaining high quality
standard could be integrated into the organisation culture.
Process
Retailers could introduce convenient transaction methods to improve customer
shopping process as Cluster 1 place most importance on payment system. It could
introduce cash-back service at the tiles. Besides that, retailers could provide variety of
payment methods. For example customers could pay by credit card, direct debit or
cash.
Physical Evidence
The layout and design of the supermarket could be aesthetically appealing to indicate
quality and class. Besides that, posters showing commitment towards quality
assurance could be displayed.

Name: Tiezheng Yuan
14
Cluster 2
Cluster 2 comprising 44 percent of student body is interpreted as price and value, and
experience seeking.
Price
Price and value is most important for Cluster 2. Supermarkets could adopt a cost-plus
pricing strategy when targeting Cluster 2. Besides that, supermarkets could adopt lean
production techniques such as Just-in-time (JIT) stock management approach.
Retailers only hold stocks that it need and therefore reducing storage cost. The cost
saved could be passed on to customers thus lowering product price to attract more
Cluster 2 consumers.
Promotion
Promotional efforts could focus on price and value. Retailers could step up
promotional efforts when there are special discounts. Rewards for frequent usage of
storecards (highest storecard ownership amongst 3 clusters) could also be adopted.
Retailers could use both above and below the line promotion methods. For example
advertise through internet, radio and magazines.
People
Friendly staff is most important for Cluster 2. Organisations could place more
importance in its human resource management. It needs to recruit people with the
right values and attitudes. Adequate training could be provided to equip employees
with the necessary skills to provide quality service. Employees could also be rewarded
for showing consistent good attitude. Customer feedback could be encouraged to
reflect on staff attitude.

Name: Tiezheng Yuan
15
Cluster 3
Cluster 3 comprising 29 percent of student body is interpreted as budget conscious
and convenient seeking.
Price
Setting price within the budget of consumers is important targeting Cluster 3. Package
deals at reasonable price could be one strategy targeting Cluster 3. Retailers have to
find out the budget range of Cluster 3 and set reasonable prices within the range.
Place
Accessibility is most important for Cluster 3. There could be parking facilities nearby
supermarkets. If the supermarket is located at an inconvenient location, the
availability of parking facilities gives students the opportunities to use their cars.

Name: Tiezheng Yuan
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7. Summary
This section will provide a brief summary of research aim, research method, key
results and value of results. It will also evaluate the study and provide suggestions for
future research.
The aim of this study concerns the application of cluster analysis to data in the form
of factor scores that measure the importance that students attach to supermarket
features representing dimensions of Economy, Payment system, Range and quality of
products, Friendly staff, and Accessibility.
The data consist of 14-item scale designed to measure the importance of supermarket
features (1 = Not at all important, 5 = Very important).
were defined respectively as ‘Economy’, ‘Payment systems’, ‘Range and quality of
products’, ‘Friendly staff’ and ‘Accessibility’. Factor scores were saved for cluster
analysis.
Cluster analysis was applied as a two-stage process to the five factor scores. In the
first stage, a hierarchical analysis was employed. Subsequently, in the second stage,
the K-Means optimisation method was employed. Three-cluster solutions was derived
and are interpreted as Rich and quality seeking comprising 27% (Cluster 1), Price and
value, and experience seeking comprising 44% (Cluster 2) and Budget conscious and
convenient seeking comprising 29% (Cluster 3). The application of cluster analysis to
the student data facilitates the segmentation of student shoppers. Therefore, improving
the understanding of student food shoppers and allowing better marketing strategies to
be devised, targeting students.
The study can be further improved by using probability sampling technique such as
stratified sampling to provide a better representation of the population. The use of
quota sampling (non-probability) technique in the study may not provide an unbiased
representation of population (Peterson and O’Dell, 1950). As a result, objective
statistical inferences are difficult to make when non-probability sampling is used
(Ngulube, 2005).

Name: Tiezheng Yuan
17
In future research, the study could be conducted using mixed method approach. For
example, focus group could be conducted before factor and cluster analysis. Such a
way, the data gathered is triangulated and therefore improve the credibility and
validity of result (Homburg et al., 2012). In addition, more information could be
gathered using mixed method approach. Participants might be willing to provide more
information in a focus group compared to face-to-face survey as they feel more secure
answering questions in a group (Powell and Single, 1996). Therefore enable the
researcher to gather more information.

Name: Tiezheng Yuan
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List of References
Forth, J., Bewley, H., Bryson, A., Dix, G. and Oxenbridge, S. (2010) 'Survey errors
and survey costs: a response to Timming’s critique of the survey of employees
questionnaire in WERS 2004', Work Employment and Society, 24(3), pp. 578-590.
Fricker, R.D. and Schonlau, M. (2002) 'Advantages and disadvantages of internet
research surveys: Evidence from the literature', Field Methods, 14(4), pp. 347-367.
Homburg, C., Klarmann, M., Reimann, M. and Schilke, O. (2012) 'What drives key
informant accuracy?', Journal of Marketing Research (JMR), 49(4), pp. 594-608.
IBM SPSS (2012), SPSS for Windows (Version 21.0), Chicago, IL, USA: SPSS Inc.
Ngulube, P. (2005) 'Research procedures used by Master of Information Studies
students at the University of Natal in the period 1982–2002 with special reference to
their sampling techniques and survey response rates: A methodological discourse',
The International Information & Library Review, 37(2), pp. 127–143.
Peterson, P.G. and O'Dell, W.F. (1950) 'Selecting sampling methods in commercial
research', Journal of Marketing, 15(2), pp. 182-189.
Powell, R.A. and Single, H.M. (1996) 'Focus Groups', International Journal for
Quality in Health Care, 8(55), pp. 499-504.
Sung, H. and Jeon, Y. (2009) 'A profile of Koreans: who purchases fashion goods
online?', Journal of Fashion Marketing and Management, 13(1), pp. 79-97.

Name: Tiezheng Yuan
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Appendix 1. Saved FactorScores
Factors, Associated Variables and Interpretation
Factor
Number
Associated Variables Coefficient Interpretation
Factor 1 Low prices
Value for money
Special Offers
.833
.767
.757
Economy
Factor 2 Cash back facilities
Method of payment
.846
.824
Payment systems
Factor 3 Wide Range of well known brands
High quality products .769
.734
products
Factor 4 Friendly, helpful staff .805 Friendly staff
Factor 5 Car parking facilities
Convenient location
.781
-.769
Accessibility

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Appendix 2. Preliminary HierarchicalCluster Analysis
Gower Diagram from Preliminary Hierarchical Analysis
Final Ten Stages of Hierarchical Cluster Analysis
Stage
Cluster Combined
Coefficients
Stage Cluster First Appears
Next Stage
Number of
clustersCluster 1 Cluster 2 Cluster 1 Cluster 2
698 10 85 12.062 683 691 702 10
699 1 18 12.432 697 680 700 9
700 1 62 13.819 699 690 702 8
701 79 226 14.297 533 687 706 7
702 1 10 15.814 700 698 703 6
703 1 38 15.965 702 651 704 5
704 1 48 25.527 703 0 706 4
705 511 518 26.156 639 0 707 3
706 1 79 27.646 704 701 707 2
707 1 511 38.909 706 705 0 1
0
5
10
15
20
25
30
35
40
-1 1 3 5 7 9 11
Distance
Number of Clusters

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Appendix 3. Proportion of EachCluster
Number of Cases in Each Cluster
Cluster
number
Number Per cent
1 189.000 27
2 313.000 44
3 206.000 29
Valid 708.000 100
Missing 23.000
Appendix 4. SPSS Output Chi-Square tests
Crosstab for ClusterIdentity and Storecard Ownership
Cluster number
Storecard Total
Yes No
Cluster identity
Cluster 1
Count 91 88 179
% within Cluster identity 50.8% 49.2% 100.0%
Cluster 2
Count 198 101 299
Cluster 3
Count 111 89 200
Total
Count 400 278 678
Chi-square Test for ClusterIdentity and StorecardOwnership
Test
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 12.387a
2 .002
Likelihood Ratio 12.447 2 .002
Linear-by-Linear Association .632 1 .427
N of Valid Cases 678

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Crosstab for ClusterIdentity and Use of Food Budget
Cluster number
Budget Total
Yes No
Cluster identity
Cluster 1
Count 43 145 188
Cluster 2
Count 129 182 311
Cluster 3
Count 106 100 206
Total
Count 278 427 705
Chi-square Test for ClusterIdentity and Use of a Food Budget
Test
sided)
2 .000
Linear-by-Linear Association 33.201 1 .000

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Crosstab for ClusterIdentity and Weekly Expenditure on Food
Cluster number
Weekly Expenditure Total
£0-15 £16-30 £31+
Cluster identity
Cluster 1
Count 36 112 40 188
% within Cluster identity 19.1% 59.6% 21.3% 100.0%
Cluster 2
Count 112 142 44 298
Cluster 3
Count 80 107 18 205
Total
Count 228 361 102 691
Chi-square Test for Crosstab for ClusterIdentity and Weekly Expenditure on Food
Test
sided)
4 .000
Linear-by-Linear Association 22.649 1 .000

Cluster Analysis Assignment 2013-2014(2)

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