Classification and analysis of management and marketing data [Elektronische Ressource] : statistical applications for strategic planning / by Tina M. Facca

Tfacca

Sujets

• Management

Informations

• Publié par: georg-august-universitat_gottingen
• Publié le: 01 janvier 2010
• Nombre de lectures: 11
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

Classification and Analysis of
Management and Marketing Data
Statistical Applications for Strategic Planning



Dissertation
Presented for the Degree of Doctor of Philosophy
at the Faculty of Economics and Business Administration
of the Georg-August University of Göttingen




by
Tina M. Facca
from
Ohio, United States of America

Göttingen, 2010


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First Examiner: Prof. Dr. Walter Zucchini
Second Examiner: Prof. Dr. Yasemin Boztuğ
Third Examiner: Prof. Dr. Stefan Sperlich

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Contents
Introduction ………………………………………………………………. 11
Classification and Analysis of a Job-Seeking Market
Part 1 Dataset with numerical and graphical description
Section 1.1 Motivation and literature………………………………….. 13
Section 1.2 Loyalty scores…………. 18
Section 1.3 Description of the dataset……………….………………… 20
Section 1.4 General problem…………………………………………… 28
Section 1.5 Problem of classification by subclass……………………... 34
Section 1.6 Conclusion - Better classification method needed ……....... 46

Part 2 Classifying job seeking status based on sub-groups
Section 2.1 Definition of loyalty score ……………….……………….. 47
Section 2.2 Descriptive statistics and graphic normality check of
loyalty score and its components…………………………... 65
Section 2.3 Descriptive statistics of factors by subclass………….…….. 70
Section 2.4 Conditional probability given loyalty score………………. 84
Section 2.5 Classification based on intervals – cluster analysis……….. 88
Section 2.6 Cation using discriminant analysis………………… 94
Section 2.7 Building a generalized additive model to predict job-seeking
status………………………………………………………. 117
Section 2.8 Summary, limitations and future research……………….... 125
References Classification and analysis of a job-seeking market………. 127


Classification and Analysis of College Student Leadership Data

Part 1 Dataset with numerical and graphical description
Introduction …………………………………………………………… 136
Section 1.1 College student leadership literature………..…………… 137
Section 1.2 Description of the dataset …………………………..…… 151
Section 1.3 General problem – measuring leadership………………….. 157
Section 1.4 Problem of classification – consciousness constructs..……. 160

Part 2 Classifying college student leadership based on sub-groups
Section 2.1 Reliability of constructs and factor analysis………………. 173
Section 2.2 Classification by discriminant analysis……………………. 176
Section 2.3 Cluster analysis to segment leadership behaviors…………. 182
Section 2.4 Summary and future research……………………………… 193
References Classification and analysis of College student leadership
data…………………………………………………………. 196



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Classification and Analysis of Franchise Resource Data

Part 1 Dataset with numerical and graphical description
Section 1.1 Franchising market – issues, constraints and literature.….. 204
Section 1.2 Description of the dataset…………………………………. 211
Section 1.3 General problem – where to focus franchisor‘s resources… 219
Section 1.4 Problem of classification by tenure and revenue segments.. 221


Part 2 Classifying franchisees based on sub-groups
Section 2.1 Classification of franchisees using discriminant analysis… 229
Section 2.2 Summary – Classification and analysis of franchise data…. 239
References Classification and analysis of franchise resource data…… 241

APPENDICES
Appendix A Job-Seeking Market - Section 2.3 Kruskal-Wallis test…… 243
Appendix B Job-Seeking Market - Section 2.6 validation test results
from discriminant analysis………………………………… 244


List of abbreviations
SUPV – supervisor
COMP – company
OPPTY – opportunity
QWL – quality of work-life
PCA – principle components analysis
GAM – generalized additive model(s)
HERI – Higher Education Research Institute
EI – Emotional intelligence
EILI – Emotional intelligence leadership inventory



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List of Figures

Classification and Analysis of a Job-Seeking Market

Figure 1.3.1 Respondents‘ employment status………………………… 20
Figure 1.3.2 Rents‘ job seeking status…………………………. 21
Figure 1.3.3 Respondents‘ level of employment……………… 21
Figure 1.3.4 Rents‘ most recent job change…………………….. 22
Figure 1.3.5 Respondents‘ salary level………………………………… 23
Figure 1.3.6 Rents‘ industries represented………... 23
Figure 1.3.7 Respondents‘ gender……………………………………… 24
Figure 1.3.8 Rents‘ age group………………………………………. 24
Figure 1.3.9 Respondents‘ geographic locations within United States 24
Figure 1.3.10 Rents‘ ethnicity……………………………………….. 25
Figure 1.3.11 Respondents‘ education……………………… 25
Figure 1.5.1 Variables listed inside arrows -significant differences ….. 37
Figure 1.5.2 Non-seeker vs. Seeker – significant differences………… 39
Figure 1.5.3 Non-seeker vs. Passive – significant differences… 41
Figure 1.5.4 Passive vs. Seeker – significant differences……………… 43
Figure 2.1.1 Component plot in rotated space..………………………… 51
Figure 2.1.2 Cattell screeplot…………………………………………… 60
Figure 2.2.1 Supervisor not normally distributed………………………. 66
Figure 2.2.2 Company not normally distributed…….………………….. 66
Figure 2.2.3 Opportunitylly distributed …………………….. 67
Figure 2.2.4 Quality of work-life not normally distributed….. ………… 67
Figure 2.2.5 Loyalty score not normally distributed ………………… 68
Figure 2.3.1 Seeker – supervisor not normally distributed …… 72
Figure 2.3.2 Seeker – company not normally distributed …………….. 72
Figure 2.3.3 Seeker – opportunity not normally distributed …………. 72
Figure 2.3.4 Seeker – quality of work-life not normally distributed... 72
Figure 2.3.5 Seeker – loyalty score not normally distributed ……….. 73
Figure 2.3.6 Passive – supervisor not normally distributed …………. 74
Figure 2.3.7 Passive – company lly distributed …………… 74
Figure 2.3.8 Passive - opportunity not normally distributed ………… 74
Figure 2.3.9 Passive - quality of work-life not normally distributed … 74
Figure 2.3.10 Passive- loyalty score not normally distributed ………… 75
Figure 2.3.11 Non-seeker – supervisor not normally distributed 76
Figure 2.3.12 Non-seeker – company not normally distributed ………. 76
Figure 2.3.13 Non-seeker – opportunity not normally distributed ……. 77
Figure 2.3.14 Non-seeker - quality work life not normally distributed 77
Figure 2.3.15 Non-seeker - loyalty score lly distributed..…. 71
Figure 2.3.16 Boxplots for supervisor by subclass……………………… 80
Figure 2.3.17 Boxplots for company by subclass ……………………… 81
Figure 2.3.18 Boxplots for opportunity by subclass…………………...... 81
Figure 2.3.19 Boxplots for quality of work-life by subclass…………… 82
Figure 2.3.20 Boxplots for loyalty score by subclass…………………… 83
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Figure 2.6.1 Two-group discriminant function…………………………. 96
Figure 2.6.2 Solid lines representing fitted LDA boundaries…………… 98
Figure 2.6.3 Separation produced by first discriminant function……….. 113
Figure 2.6.4 Separation produced by second discriminant function…….. 114
Figure 2.7.1 Functions at three degrees of freedom …………………….. 121
Figure 2.7.2 Varying each factor while holding others constant; three
degrees of freedom…………………………………………. 122
Figure 2.7.3 Functions at two degrees of freedom ……………………… 123
Figure 2.7.4 Varying each factor while holding others constant; two
degrees of freedom…………………………………………. 124


Classification and Analysis of College Student Leadership Data

Figure 1.2.1 Respondent gender………………………………………… 154
Figure 1.2.2 Rent ethnicity… 154
Figure 1.2.3 Respondent class rank… 154
Figure 1.2.4 Rent age category………………………………….. 155
Figure 1.2.5 Respondent participation in student organizations………… 155
Figure 1.2.6 Rent in leadership role…………………… 155
Figure 1.2.7 Respondent leadership retreats……….. 156
Figure 1.4.1 Construct score comparisons by level……………………… 161
Figure 1.4.2 Comparison by involvement……………………………….. 166
Figure 1.4.3 Max leaders vs. base on construct scores………………….. 167
Figure 1.4.4 Men in student organizations………………………………. 169
Figure 1.4.5 Women in student organizations………………………….... 169
Figure 2.3.1 Cluster Profiles……………………………………………. 186
Figure 2.3.2 Categorical discriminators for less involved,
less others-oriented group ………………………………… 187
Figure 2.3.3 Level of involvement differentiating third cluster …….. 188
Figure 2.3.4 Discriminant scores for each cluster from first discriminant
function……………………………………………………. 189
Figure 2.3.5 Second discriminant function……………………………… 191


Classification and Analysis of Franchise Resource Data

Figure 1.2.1 Franchisee tenure distribution… ………………………….. 212
Figure 1.2.2 Franchisee sales distribution……………………………….. 212
Figure 1.2.3 Number of employees in franchisee office………………... 213
Figure 1.2.4 Franchisee satisfaction - areas of service………………….. 213