This document presents the results of a cluster analysis study conducted on student supermarket shoppers. The analysis used factor scores measuring the importance students place on supermarket features like price, quality, staff, and accessibility. Three clusters were identified. ANOVA testing showed significant differences between clusters. Cluster profiles were developed based on average factor scores and interpretations identified economically-minded, quality-focused, and convenience-prioritizing shopper segments. The results provide insights to better target marketing strategies at different student shopper groups.
The document discusses various initiation systems used in surface blasting. It describes initiation systems as a combination of explosive devices and components that initiate an explosive charge from a safe distance. The main initiation systems discussed are safety fuse with caps, detonating cord, electric caps/detonators, non-electric assemblies, electronic detonators, and wireless electronic blasting systems. The document provides details on the components, workings, advantages, and disadvantages of each system.
This document provides an overview of descriptive modeling techniques in data mining. It defines descriptive modeling as analyzing past data to gain insights rather than predicting future events. Key techniques discussed include association rule mining to discover relationships between variables, and clustering to group similar objects together. The document outlines different clustering algorithms like k-means, hierarchical, and density-based clustering. It also discusses pros and cons of descriptive modeling, such as the abundance of algorithms but difficulty in evaluating quality.
This document introduces the dplyr package in R for transforming and summarizing tabular data. It explains that dplyr is a powerful, fast, and easy-to-use package for those with SQL experience. The key dplyr verbs like select, filter, mutate, arrange, summarize, and group_by are described. Select filters columns, filter filters rows, mutate adds columns, arrange reorders rows, summarize computes summary statistics, and group_by splits the data for grouping. The pipe operator %>% pipes the output of one function into the next to chain operations from left to right.
The document provides an overview of cluster analysis techniques. It discusses the need for segmentation to group large populations into meaningful subsets. Common clustering algorithms like k-means are introduced, which assign data points to clusters based on similarity. The document also covers calculating distances between observations, defining the distance between clusters, and interpreting the results of clustering analysis. Real-world applications of segmentation and clustering are mentioned such as market research, credit risk analysis, and operations management.
This document summarizes a study that examines using cluster analysis to detect anomalies in accounting data, specifically for detecting discrepancies during audits. The study applies cluster analysis to a dataset from a US insurance company to group similar life insurance claims together and flag small clusters for further investigation. Some characteristics of flagged clusters included large beneficiary payments, large interest payments, and long lags between claim submission and payment. The document reviews literature on anomaly detection and cluster analysis techniques for anomaly detection, and discusses how cluster analysis is well-suited for fraud detection in accounting data since it is difficult to identify abnormal transactions.
West West Auckland Integrated Care Project - Locality and Cluster AnalysisJonathan Simon onzm
This document provides a summary of health data and population characteristics for the West Auckland locality and three clusters within it - Henderson, Massey, and New Lynn. Some key findings include:
1) The West Auckland locality has a growing and increasingly diverse population, with higher deprivation than the overall Waitemata DHB region.
2) Life expectancy is lower in West Auckland compared to the overall DHB region, and varies between ethnic groups.
3) Both primary and secondary health care utilization is higher in West Auckland compared to the DHB as a whole. Rates of long-term conditions and hospital admissions are also generally higher.
4) There is variation in health indicators within the three clusters, with the
Application of Clustering in Data Science using Real-life Examples Edureka!
This document outlines an Edureka webinar on applications of clustering in real life. The webinar instructor is Kumaran Ponnambalam. The objectives are to understand data science applications and prospects, machine learning categories, clustering and k-means clustering. Examples of clustering applications include wine recommendation, pizza delivery optimization, and news summarization. K-means clustering is demonstrated on pizza delivery location data. The webinar also discusses data science job trends and covers 10 modules on data science topics including machine learning techniques in R.
The management of cluster A1 carried out a so-called impact
analysis in co-operation with the Institute for Innovation and
Technology (iit) in April/May 2012. The objective of the investigations
was to find out in which fields and to which extent the
players of cluster A had particularly profited from the networking
and in which fields the members’ requirements, especially
those of the enterprises, could eventually not have been met.
The results of the study clearly show that the enterprises in cluster
A have generally been able to benefit well or even very well
from the net-working activities.
Equally important is the fact that the surveyed enterprises had
achieved excellent effects specifically in those fields that had
been considered particularly important for a large number of
cluster participants.
This fact illustrates that the management of cluster A had predominantly
focused its activities on the fields of high priority
and has been able to achieve very positive effects.
In the context of limited resources available to the cluster
management organisation, this finding is of high relevance.
The performance of the enterprises involved in cluster A can be
described as good.
At least half of the network’s players range above the general
industry average regarding typical indicators like turnover or
productivity.
The impact analysis shows that public investments generally
result in monetary benefits for the companies involved in a
cluster initiative. The monetary effect (output) hereby has turned
out to be larger than the public sector invest-ments (input)
made over the same period of time. The output/input leverage
amounts to 2.3. Thus, each euro invested to the cluster by
public authorities generates a monetary benefit of EUR 2.3
which is an encouraging result.
The overall analysis revealed that the sum of monetary effects
had been larger than the total number of investments made by
the public and private sector (the output-input-rate amounts
to 1.3).
This result can in fact be interpreted as consolidated legitimization to public investments in recent years.
The document discusses various initiation systems used in surface blasting. It describes initiation systems as a combination of explosive devices and components that initiate an explosive charge from a safe distance. The main initiation systems discussed are safety fuse with caps, detonating cord, electric caps/detonators, non-electric assemblies, electronic detonators, and wireless electronic blasting systems. The document provides details on the components, workings, advantages, and disadvantages of each system.
This document provides an overview of descriptive modeling techniques in data mining. It defines descriptive modeling as analyzing past data to gain insights rather than predicting future events. Key techniques discussed include association rule mining to discover relationships between variables, and clustering to group similar objects together. The document outlines different clustering algorithms like k-means, hierarchical, and density-based clustering. It also discusses pros and cons of descriptive modeling, such as the abundance of algorithms but difficulty in evaluating quality.
This document introduces the dplyr package in R for transforming and summarizing tabular data. It explains that dplyr is a powerful, fast, and easy-to-use package for those with SQL experience. The key dplyr verbs like select, filter, mutate, arrange, summarize, and group_by are described. Select filters columns, filter filters rows, mutate adds columns, arrange reorders rows, summarize computes summary statistics, and group_by splits the data for grouping. The pipe operator %>% pipes the output of one function into the next to chain operations from left to right.
The document provides an overview of cluster analysis techniques. It discusses the need for segmentation to group large populations into meaningful subsets. Common clustering algorithms like k-means are introduced, which assign data points to clusters based on similarity. The document also covers calculating distances between observations, defining the distance between clusters, and interpreting the results of clustering analysis. Real-world applications of segmentation and clustering are mentioned such as market research, credit risk analysis, and operations management.
This document summarizes a study that examines using cluster analysis to detect anomalies in accounting data, specifically for detecting discrepancies during audits. The study applies cluster analysis to a dataset from a US insurance company to group similar life insurance claims together and flag small clusters for further investigation. Some characteristics of flagged clusters included large beneficiary payments, large interest payments, and long lags between claim submission and payment. The document reviews literature on anomaly detection and cluster analysis techniques for anomaly detection, and discusses how cluster analysis is well-suited for fraud detection in accounting data since it is difficult to identify abnormal transactions.
West West Auckland Integrated Care Project - Locality and Cluster AnalysisJonathan Simon onzm
This document provides a summary of health data and population characteristics for the West Auckland locality and three clusters within it - Henderson, Massey, and New Lynn. Some key findings include:
1) The West Auckland locality has a growing and increasingly diverse population, with higher deprivation than the overall Waitemata DHB region.
2) Life expectancy is lower in West Auckland compared to the overall DHB region, and varies between ethnic groups.
3) Both primary and secondary health care utilization is higher in West Auckland compared to the DHB as a whole. Rates of long-term conditions and hospital admissions are also generally higher.
4) There is variation in health indicators within the three clusters, with the
Application of Clustering in Data Science using Real-life Examples Edureka!
This document outlines an Edureka webinar on applications of clustering in real life. The webinar instructor is Kumaran Ponnambalam. The objectives are to understand data science applications and prospects, machine learning categories, clustering and k-means clustering. Examples of clustering applications include wine recommendation, pizza delivery optimization, and news summarization. K-means clustering is demonstrated on pizza delivery location data. The webinar also discusses data science job trends and covers 10 modules on data science topics including machine learning techniques in R.
The management of cluster A1 carried out a so-called impact
analysis in co-operation with the Institute for Innovation and
Technology (iit) in April/May 2012. The objective of the investigations
was to find out in which fields and to which extent the
players of cluster A had particularly profited from the networking
and in which fields the members’ requirements, especially
those of the enterprises, could eventually not have been met.
The results of the study clearly show that the enterprises in cluster
A have generally been able to benefit well or even very well
from the net-working activities.
Equally important is the fact that the surveyed enterprises had
achieved excellent effects specifically in those fields that had
been considered particularly important for a large number of
cluster participants.
This fact illustrates that the management of cluster A had predominantly
focused its activities on the fields of high priority
and has been able to achieve very positive effects.
In the context of limited resources available to the cluster
management organisation, this finding is of high relevance.
The performance of the enterprises involved in cluster A can be
described as good.
At least half of the network’s players range above the general
industry average regarding typical indicators like turnover or
productivity.
The impact analysis shows that public investments generally
result in monetary benefits for the companies involved in a
cluster initiative. The monetary effect (output) hereby has turned
out to be larger than the public sector invest-ments (input)
made over the same period of time. The output/input leverage
amounts to 2.3. Thus, each euro invested to the cluster by
public authorities generates a monetary benefit of EUR 2.3
which is an encouraging result.
The overall analysis revealed that the sum of monetary effects
had been larger than the total number of investments made by
the public and private sector (the output-input-rate amounts
to 1.3).
This result can in fact be interpreted as consolidated legitimization to public investments in recent years.
Quantitative techniques refer to scientific, mathematical, and statistical methods for solving complex business problems. These techniques include statistical methods like data collection, analysis, and forecasting as well as operations research techniques like linear programming. Quantitative techniques help organizations make data-driven decisions in areas like marketing, production, finance, personnel management, research and development, and economics. The document then provides details on specific quantitative techniques and the steps involved in marketing research.
Research Methodology Of The Research ApproachJessica Howard
This section discusses the research methodology used in the study. It outlines two main types of research methods: quantitative and qualitative. Quantitative research uses numerical data that can be mathematically analyzed, while qualitative research uses non-numerical data to understand experiences. The study will use qualitative methods as the research involves many social variables best explored through qualitative approaches. Data will be collected through interviews and analyzed thematically to understand perceptions.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Cluster analysis is a technique used to classify objects into homogeneous groups called clusters based on similarities. Objects within each cluster are similar to each other and dissimilar to objects in other clusters. There is a five-step process to conducting cluster analysis: 1) formulate the problem by selecting variables, 2) select a distance measure to determine similarity between objects, 3) decide the number of clusters, 4) interpret and profile the clusters to identify differentiating variables, 5) assess the validity and reliability of the clustering. Common clustering procedures include hierarchical and nonhierarchical approaches.
Effective Feature Selection for Feature Possessing Group Structurerahulmonikasharma
This document proposes a new method called efficient group variable selection (EGVS) for feature selection when features have a group structure. EGVS has two stages: 1) within-group variable selection evaluates each feature individually to select discriminative features within each group. 2) Between-group variable selection re-evaluates all features to remove redundancy and obtain an optimal subset by considering relationships between groups. The method is demonstrated on benchmark datasets, showing it increases classification accuracy by leveraging the group structure during feature selection.
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
This historical research study examines the impact of the Industrial Revolution on urbanization in 19th century England. It will collect primary and secondary sources on both topics and analyze trends, patterns, and social changes revealed in these sources. Key themes like population growth, migration, living conditions, and emergence of industrial cities will be interpreted. The findings will then be synthesized to develop an understanding of how industrial and technological advances influenced urbanization. The conclusion will summarize the main impact and identify areas for further research.
Study to investigate which analysis is the best equipped to understand how co...Charm Rammandala
The purpose of this study is to identify the best method of analysis to deploy to understand how consumers develop preferences for products or services using combination of different attributes.
After conducting a detailed literature review, it was proven that conjoint analysis is the best method to associate for the type of research needed to be carry-out. This study take an in-depth look in to the conjoint analysis method to understand how it use to achieve the intended results
This chapter discusses research methods and procedures. It describes the descriptive method of research, which involves observing and describing phenomena without influencing it. Common data collection methods like interviews and questionnaires are discussed. The document also covers developing a good research instrument, sampling design including different probability sampling techniques, and guidelines for selecting appropriate statistical analysis procedures.
This document is a student assignment applying exploratory factor analysis to survey data on the importance of supermarket features. It includes an introduction outlining the study's purpose and structure. The document then reviews the theory of exploratory factor analysis and applies it to analyze survey data on 14 items measuring the importance of supermarket features. The analysis identifies underlying dimensions or factors in the data. The document presents the results of the factor analysis and discusses implications for marketing grocery stores to students.
An Overview and Application of Discriminant Analysis in Data AnalysisIOSR Journals
This document provides an overview of discriminant analysis, including its history, key assumptions, and different types (e.g. linear, quadratic). It discusses advantages of discriminant analysis compared to logistic regression, such as its ability to handle small sample sizes. The document also describes steps to develop a discriminant model, including variable selection, assumptions checking, and evaluation. It then presents an application of discriminant analysis to classify failed vs successful companies in Nigeria based on financial ratios. The model was able to predict company failure up to 3 years in advance.
This document provides information about getting fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact for assistance with marketing research assignments. It then provides sample answers to 4 marketing research assignment questions, covering topics like primary research processes, types of data, qualitative vs. quantitative research methods, conjoint analysis, and probability sampling methods.
This document provides an overview of unsupervised learning techniques. It begins with introductions to unsupervised learning and clustering as a machine learning task. It then describes different types of clustering techniques including partitioning methods like k-means and k-medoids, hierarchical clustering, and density-based methods. Applications of clustering like customer segmentation and anomaly detection are also discussed. Key aspects of clustering algorithms like determining the optimal number of clusters using the elbow method are explained through examples.
The document discusses data analysis and interpretation. It describes the different scales of measurement used in data analysis including nominal, ordinal, interval, and ratio scales. It also discusses various methods used for interpreting qualitative and quantitative data, such as using statistical techniques like mean and standard deviation for quantitative data. Finally, it covers different visualization techniques used in data interpretation like bar graphs, pie charts, tables, and line graphs.
The document discusses four major types of evaluation methods: case study, statistical analysis, field experiment, and survey research. It provides details on case study methods, including definitions, types of case studies, and steps to conducting a case study. Statistical analysis methods are also summarized, including descriptive statistics such as frequency counts and distributions, and measures of central tendency and variability. Mathematical modeling as a research method is briefly outlined.
1) The document discusses Bharat Sanchar Nigam Limited (BSNL), the largest telecommunications company in India. It provides an overview of BSNL's services, financials, and recruitment and selection processes.
2) BSNL offers various telecom services including basic telephone, cellular, internet, broadband, and enterprise services. It has a subscriber base of millions and annual revenue of over $8 billion.
3) The document examines BSNL's recruitment and selection procedures, with a focus on understanding the processes and ensuring quality. Primary and secondary research methods are used, including questionnaires and data analysis.
This document provides an introduction to statistics and data visualization. It discusses key topics including descriptive and inferential statistics, variables and types of data, sampling techniques, organizing and graphing data, measures of central tendency and variation, and random variables. Specifically, it defines statistics as collecting, organizing, summarizing, analyzing and making decisions from data. It also outlines the main differences between descriptive statistics, which describes data, and inferential statistics, which uses samples to make estimations about populations.
This document summarizes the key components of a research methodology section, including:
1) Explaining how data was collected and analyzed to obtain results.
2) Justifying the methods used by explaining why they were appropriate for the research objectives and data being collected.
3) Discussing any problems encountered and how they were addressed.
1) The document discusses quantitative research methods, providing examples and definitions.
2) Quantitative research is defined as using quantifiable data and statistical analysis to study phenomena. It often uses methods like surveys, experiments, and analyzing numerical results.
3) The document provides examples of different types of quantitative research designs, including experimental, descriptive, correlational, evaluation, survey, and causal-comparative research. It also includes activities for students to differentiate between quantitative and qualitative research.
12Levels of MeasurementNameInstitutionalEttaBenton28
1
2
Levels of Measurement
Name
Institutional affiliation
Professor
Course
Date
Levels of Measurement
Levels of measurement consist of interval, ordinal, ratio, and nominal. Nominal is the lowest level where there is no representation of mathematical variables. For instance, when measuring the gender population in rehabilitation centers with more males than females, nominal variables would be more than the real number of individuals in rehabilitation centers. On the other hand, ordinal measurement is a level where there is a ranking of variables in an orderly way (Vaske, 2019). For instance, a researcher may use the Likert scale to survey people in a community through the agree-strongly disagree method at the end of a meeting. In this case, the survey is rank-ordered to show how effective the meeting was relative to survey results.
Additionally, an interval is a level of measurement where variables have meaningful values with the same space between the values. The space is vital in the data collected because zero has no basic value in this measurement. In this case, one can use interval measurements to track substance abusers between two diverse age groups. Hence, the data is essential in understanding what situations can cause people to abuse substances. Finally, the ratio is the measurement level where variables have meaningful value with the same space between the values but have no true value of zero. Hence, a ratio level is countable. In this case, ratio and interval measurements would be the best levels to gather information on the number of substance abusers in a given year by combining them to compare the information. When one wants to gather data in the criminal justice system, I think these levels would apply to any researcher on a study. However, not all measurement levels are compatible since wrong measurement levels can skew outcomes.
Reference
Vaske, J. J. (2019). Survey research and analysis. Sagamore-Venture. 1807 North Federal Drive, Urbana, IL 61801.
· Grade for Deliverable 3
100% of total grade
A - 4 - Mastery
4
B - 3 - Proficiency
3
C - 2 - Competence
2
F - 1 - No Pass
1
I - 0 - Not Submitted
0
· Criterion 1
0% of total grade
A - 4 - Mastery
Clearly stated, detailed description of all of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; method(s)/design; the theoretical framework; research objectives, questions, and hypotheses.
0
B - 3 - Proficiency
Thorough description of most of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; method(s)/design; the theoretical framework; research objectives, questions, and hypotheses.
0
C - 2 - Competence
Basic description of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; met ...
DELL CASE STUDY - UNDERSTANDING DELL’S CUSTOMERS AS A KEY IN DEVELOPING MARKE...TIEZHENG YUAN
This report summarizes a survey conducted by Dell of recent purchasers of their PCs and notebooks in the UK. The survey aimed to understand customers' demographics, internet usage, satisfaction and loyalty towards Dell, perceptions of Dell's performance, price sensitivity, and personality characteristics. Key findings include that most customers were college-educated, middle-aged, and had household incomes between £30,000-£75,000. Customers spent 1-10 hours online per week and were highly satisfied with Dell's products, services, and prices. A positive relationship was found between satisfaction, loyalty, and perceptions of Dell's performance. Customers were also found to be price sensitive.
UNDERSTANDING CONSUMER PURCHASING BEHAVIOUR AND CONSUMPTION IN THE ORAL CARE ...TIEZHENG YUAN
The key findings from the secondary research are:
1. Young adults aged 16-24 are most interested in teeth whitening benefits from oral care products.
2. While hygiene benefits like preventing cavities and gum disease are important, consumers increasingly seek cosmetic benefits like whitening teeth.
3. Toothpaste manufacturers offer a variety of products focused on both hygiene and cosmetic benefits like whitening teeth.
More Related Content
Similar to Cluster Analysis Assignment 2013-2014(2)
Quantitative techniques refer to scientific, mathematical, and statistical methods for solving complex business problems. These techniques include statistical methods like data collection, analysis, and forecasting as well as operations research techniques like linear programming. Quantitative techniques help organizations make data-driven decisions in areas like marketing, production, finance, personnel management, research and development, and economics. The document then provides details on specific quantitative techniques and the steps involved in marketing research.
Research Methodology Of The Research ApproachJessica Howard
This section discusses the research methodology used in the study. It outlines two main types of research methods: quantitative and qualitative. Quantitative research uses numerical data that can be mathematically analyzed, while qualitative research uses non-numerical data to understand experiences. The study will use qualitative methods as the research involves many social variables best explored through qualitative approaches. Data will be collected through interviews and analyzed thematically to understand perceptions.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Cluster analysis is a technique used to classify objects into homogeneous groups called clusters based on similarities. Objects within each cluster are similar to each other and dissimilar to objects in other clusters. There is a five-step process to conducting cluster analysis: 1) formulate the problem by selecting variables, 2) select a distance measure to determine similarity between objects, 3) decide the number of clusters, 4) interpret and profile the clusters to identify differentiating variables, 5) assess the validity and reliability of the clustering. Common clustering procedures include hierarchical and nonhierarchical approaches.
Effective Feature Selection for Feature Possessing Group Structurerahulmonikasharma
This document proposes a new method called efficient group variable selection (EGVS) for feature selection when features have a group structure. EGVS has two stages: 1) within-group variable selection evaluates each feature individually to select discriminative features within each group. 2) Between-group variable selection re-evaluates all features to remove redundancy and obtain an optimal subset by considering relationships between groups. The method is demonstrated on benchmark datasets, showing it increases classification accuracy by leveraging the group structure during feature selection.
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
This historical research study examines the impact of the Industrial Revolution on urbanization in 19th century England. It will collect primary and secondary sources on both topics and analyze trends, patterns, and social changes revealed in these sources. Key themes like population growth, migration, living conditions, and emergence of industrial cities will be interpreted. The findings will then be synthesized to develop an understanding of how industrial and technological advances influenced urbanization. The conclusion will summarize the main impact and identify areas for further research.
Study to investigate which analysis is the best equipped to understand how co...Charm Rammandala
The purpose of this study is to identify the best method of analysis to deploy to understand how consumers develop preferences for products or services using combination of different attributes.
After conducting a detailed literature review, it was proven that conjoint analysis is the best method to associate for the type of research needed to be carry-out. This study take an in-depth look in to the conjoint analysis method to understand how it use to achieve the intended results
This chapter discusses research methods and procedures. It describes the descriptive method of research, which involves observing and describing phenomena without influencing it. Common data collection methods like interviews and questionnaires are discussed. The document also covers developing a good research instrument, sampling design including different probability sampling techniques, and guidelines for selecting appropriate statistical analysis procedures.
This document is a student assignment applying exploratory factor analysis to survey data on the importance of supermarket features. It includes an introduction outlining the study's purpose and structure. The document then reviews the theory of exploratory factor analysis and applies it to analyze survey data on 14 items measuring the importance of supermarket features. The analysis identifies underlying dimensions or factors in the data. The document presents the results of the factor analysis and discusses implications for marketing grocery stores to students.
An Overview and Application of Discriminant Analysis in Data AnalysisIOSR Journals
This document provides an overview of discriminant analysis, including its history, key assumptions, and different types (e.g. linear, quadratic). It discusses advantages of discriminant analysis compared to logistic regression, such as its ability to handle small sample sizes. The document also describes steps to develop a discriminant model, including variable selection, assumptions checking, and evaluation. It then presents an application of discriminant analysis to classify failed vs successful companies in Nigeria based on financial ratios. The model was able to predict company failure up to 3 years in advance.
This document provides information about getting fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact for assistance with marketing research assignments. It then provides sample answers to 4 marketing research assignment questions, covering topics like primary research processes, types of data, qualitative vs. quantitative research methods, conjoint analysis, and probability sampling methods.
This document provides an overview of unsupervised learning techniques. It begins with introductions to unsupervised learning and clustering as a machine learning task. It then describes different types of clustering techniques including partitioning methods like k-means and k-medoids, hierarchical clustering, and density-based methods. Applications of clustering like customer segmentation and anomaly detection are also discussed. Key aspects of clustering algorithms like determining the optimal number of clusters using the elbow method are explained through examples.
The document discusses data analysis and interpretation. It describes the different scales of measurement used in data analysis including nominal, ordinal, interval, and ratio scales. It also discusses various methods used for interpreting qualitative and quantitative data, such as using statistical techniques like mean and standard deviation for quantitative data. Finally, it covers different visualization techniques used in data interpretation like bar graphs, pie charts, tables, and line graphs.
The document discusses four major types of evaluation methods: case study, statistical analysis, field experiment, and survey research. It provides details on case study methods, including definitions, types of case studies, and steps to conducting a case study. Statistical analysis methods are also summarized, including descriptive statistics such as frequency counts and distributions, and measures of central tendency and variability. Mathematical modeling as a research method is briefly outlined.
1) The document discusses Bharat Sanchar Nigam Limited (BSNL), the largest telecommunications company in India. It provides an overview of BSNL's services, financials, and recruitment and selection processes.
2) BSNL offers various telecom services including basic telephone, cellular, internet, broadband, and enterprise services. It has a subscriber base of millions and annual revenue of over $8 billion.
3) The document examines BSNL's recruitment and selection procedures, with a focus on understanding the processes and ensuring quality. Primary and secondary research methods are used, including questionnaires and data analysis.
This document provides an introduction to statistics and data visualization. It discusses key topics including descriptive and inferential statistics, variables and types of data, sampling techniques, organizing and graphing data, measures of central tendency and variation, and random variables. Specifically, it defines statistics as collecting, organizing, summarizing, analyzing and making decisions from data. It also outlines the main differences between descriptive statistics, which describes data, and inferential statistics, which uses samples to make estimations about populations.
This document summarizes the key components of a research methodology section, including:
1) Explaining how data was collected and analyzed to obtain results.
2) Justifying the methods used by explaining why they were appropriate for the research objectives and data being collected.
3) Discussing any problems encountered and how they were addressed.
1) The document discusses quantitative research methods, providing examples and definitions.
2) Quantitative research is defined as using quantifiable data and statistical analysis to study phenomena. It often uses methods like surveys, experiments, and analyzing numerical results.
3) The document provides examples of different types of quantitative research designs, including experimental, descriptive, correlational, evaluation, survey, and causal-comparative research. It also includes activities for students to differentiate between quantitative and qualitative research.
12Levels of MeasurementNameInstitutionalEttaBenton28
1
2
Levels of Measurement
Name
Institutional affiliation
Professor
Course
Date
Levels of Measurement
Levels of measurement consist of interval, ordinal, ratio, and nominal. Nominal is the lowest level where there is no representation of mathematical variables. For instance, when measuring the gender population in rehabilitation centers with more males than females, nominal variables would be more than the real number of individuals in rehabilitation centers. On the other hand, ordinal measurement is a level where there is a ranking of variables in an orderly way (Vaske, 2019). For instance, a researcher may use the Likert scale to survey people in a community through the agree-strongly disagree method at the end of a meeting. In this case, the survey is rank-ordered to show how effective the meeting was relative to survey results.
Additionally, an interval is a level of measurement where variables have meaningful values with the same space between the values. The space is vital in the data collected because zero has no basic value in this measurement. In this case, one can use interval measurements to track substance abusers between two diverse age groups. Hence, the data is essential in understanding what situations can cause people to abuse substances. Finally, the ratio is the measurement level where variables have meaningful value with the same space between the values but have no true value of zero. Hence, a ratio level is countable. In this case, ratio and interval measurements would be the best levels to gather information on the number of substance abusers in a given year by combining them to compare the information. When one wants to gather data in the criminal justice system, I think these levels would apply to any researcher on a study. However, not all measurement levels are compatible since wrong measurement levels can skew outcomes.
Reference
Vaske, J. J. (2019). Survey research and analysis. Sagamore-Venture. 1807 North Federal Drive, Urbana, IL 61801.
· Grade for Deliverable 3
100% of total grade
A - 4 - Mastery
4
B - 3 - Proficiency
3
C - 2 - Competence
2
F - 1 - No Pass
1
I - 0 - Not Submitted
0
· Criterion 1
0% of total grade
A - 4 - Mastery
Clearly stated, detailed description of all of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; method(s)/design; the theoretical framework; research objectives, questions, and hypotheses.
0
B - 3 - Proficiency
Thorough description of most of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; method(s)/design; the theoretical framework; research objectives, questions, and hypotheses.
0
C - 2 - Competence
Basic description of the steps or elements of the study, and includes: purpose; the problem including significance and background; relevancy of the literature reviewed; met ...
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1. 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
2. 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
3. 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.
4. Name: Tiezheng Yuan
Student No: 110562836
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.
5. Name: Tiezheng Yuan
Student No: 110562836
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.
6. Name: Tiezheng Yuan
Student No: 110562836
4
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.
7. Name: Tiezheng Yuan
Student No: 110562836
5
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.
8. Name: Tiezheng Yuan
Student No: 110562836
6
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.
9. Name: Tiezheng Yuan
Student No: 110562836
7
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).
Factor analysis was applied to the original variables, resulting in five factors which
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.
10. Name: Tiezheng Yuan
Student No: 110562836
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.
11. Name: Tiezheng Yuan
Student No: 110562836
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
Range and quality of
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
- range and quality of
products
Cluster 2 - accessibility - friendly staff
- economy
- payment system
- range and quality of
products
accessibility friendly staff accessibility - economy
- friendly staff
Cluster 3 - payment system
- range and quality of
products
- friendly staff
- economy
- accessibility
friendly staff economy - payment system
- range and quality of
products
- friendly staff
accessibility
Factors
Cluster Profiles
12. Name: Tiezheng Yuan
Student No: 110562836
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.
13. Name: Tiezheng Yuan
Student No: 110562836
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.
14. Name: Tiezheng Yuan
Student No: 110562836
12
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
15. Name: Tiezheng Yuan
Student No: 110562836
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.
16. Name: Tiezheng Yuan
Student No: 110562836
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.
17. Name: Tiezheng Yuan
Student No: 110562836
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.
18. Name: Tiezheng Yuan
Student No: 110562836
16
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).
Factor analysis was applied to the original variables, resulting in five factors which
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).
19. Name: Tiezheng Yuan
Student No: 110562836
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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.
20. Name: Tiezheng Yuan
Student No: 110562836
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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.
21. Name: Tiezheng Yuan
Student No: 110562836
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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
Range and quality of
products
Factor 4 Friendly, helpful staff .805 Friendly staff
Factor 5 Car parking facilities
Convenient location
.781
-.769
Accessibility
23. Name: Tiezheng Yuan
Student No: 110562836
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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
% within Cluster identity 66.2% 33.8% 100.0%
Cluster 3
Count 111 89 200
% within Cluster identity 55.5% 44.5% 100.0%
Total
Count 400 278 678
% within Cluster identity 59.0% 41.0% 100.0%
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
24. Name: Tiezheng Yuan
Student No: 110562836
22
Crosstab for ClusterIdentity and Use of Food Budget
Cluster number
Budget Total
Yes No
Cluster identity
Cluster 1
Count 43 145 188
% within Cluster identity 22.9% 77.1% 100.0%
Cluster 2
Count 129 182 311
% within Cluster identity 41.5% 58.5% 100.0%
Cluster 3
Count 106 100 206
% within Cluster identity 51.5% 48.5% 100.0%
Total
Count 278 427 705
% within Cluster identity 39.4% 60.6% 100.0%
Chi-square Test for ClusterIdentity and Use of a Food Budget
Test
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 34.602a
2 .000
Likelihood Ratio 35.959 2 .000
Linear-by-Linear Association 33.201 1 .000
N of Valid Cases 705
25. Name: Tiezheng Yuan
Student No: 110562836
23
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
% within Cluster identity 37.6% 47.7% 14.8% 100.0%
Cluster 3
Count 80 107 18 205
% within Cluster identity 39.0% 52.2% 8.8% 100.0%
Total
Count 228 361 102 691
% within Cluster identity 33.0% 52.2% 14.8% 100.0%
Chi-square Test for Crosstab for ClusterIdentity and Weekly Expenditure on Food
Test
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 28.595a
4 .000
Likelihood Ratio 30.497 4 .000
Linear-by-Linear Association 22.649 1 .000
N of Valid Cases 691