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E-grāmata: Modern Analysis of Customer Surveys: with Applications using R

(KPA Ltd., Israel), (KPA Ltd)
  • Formāts: EPUB+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 11-Nov-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119961383
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Modern Analysis of Customer Surveys: with Applications using RPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 11-Nov-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119961383
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Modern Analysis of Customer Surveys: with applications using R

Customer survey studies deal with customer, consumer and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. This book demonstrates how integrating such basic analysis with more advanced tools, provides insights into non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey.

Key features:





Provides an integrated case studies-based approach to analysing customer survey data.

Presents a general introduction to customer surveys, within an organizations business cycle.

Contains classical techniques with modern and non standard tools.

Focuses on probabilistic techniques from the area of statistics/data analysis and covers all major recent developments.

Accompanied by a supporting website containing datasets and R scripts.

Customer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields.

www.wiley.com/go/modern_analysis

STATISTICS IN PRACTICE

A series of practical books outlining the use of statistical techniques in a wide range of applications areas:





HUMAN AND BIOLOGICAL SCIENCES

EARTH AND ENVIRONMENTAL SCIENCES

INDUSTRY, COMMERCE AND FINANCE
Foreword xvii
Preface xix
Contributors xxiii
PART I BASIC ASPECTS OF CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
1 Standards and classical techniques in data analysis of customer satisfaction surveys
3(16)
Silvia Salini
Ron S. Kenett
1.1 Literature on customer satisfaction surveys
4(1)
1.2 Customer satisfaction surveys and the business cycle
4(3)
1.3 Standards used in the analysis of survey data
7(5)
1.4 Measures and models of customer satisfaction
12(3)
1.4.1 The conceptual construct
12(1)
1.4.2 The measurement process
13(2)
1.5 Organization of the book
15(2)
1.6 Summary
17(2)
References
17(2)
2 The ABC annual customer satisfaction survey
19(18)
Ron S. Kenett
Silvia Salini
2.1 The ABC company
19(1)
2.2 ABC 2010 ACSS: Demographics of respondents
20(2)
2.3 ABC 2010 ACSS: Overall satisfaction
22(2)
2.4 ABC 2010 ACSS: Analysis of topics
24(3)
2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers
27(1)
2.6 Summary
28(9)
References
28(1)
Appendix
29(8)
3 Census and sample surveys
37(18)
Giovanna Nicolini
Luciana Dalla Valle
3.1 Introduction
37(2)
3.2 Types of surveys
39(2)
3.2.1 Census and sample surveys
39(1)
3.2.2 Sampling design
40(1)
3.2.3 Managing a survey
40(1)
3.2.4 Frequency of surveys
41(1)
3.3 Non-sampling errors
41(3)
3.3.1 Measurement error
42(1)
3.3.2 Coverage error
42(1)
3.3.3 Unit non-response and non-self-selection errors
43(1)
3.3.4 Item non-response and non-self-selection error
44(1)
3.4 Data collection methods
44(2)
3.5 Methods to correct non-sampling errors
46(5)
3.5.1 Methods to correct unit non-response errors
46(3)
3.5.2 Methods to correct item non-response
49(2)
3.6 Summary
51(4)
References
52(3)
4 Measurement scales
55(16)
Andrea Bonanomi
Gabriele Cantaluppi
4.1 Scale construction
55(5)
4.1.1 Nominal scale
56(1)
4.1.2 Ordinal scale
57(1)
4.1.3 Interval scale
58(1)
4.1.4 Ratio scale
59(1)
4.2 Scale transformations
60(11)
4.2.1 Scale transformations referred to single items
61(5)
4.2.2 Scale transformations to obtain scores on a unique interval scale
66(3)
Acknowledgements
69(1)
References
69(2)
5 Integrated analysis
71(18)
Silvia Biffignandi
5.1 Introduction
71(2)
5.2 Information sources and related problems
73(5)
5.2.1 Types of data sources
73(1)
5.2.2 Advantages of using secondary source data
73(1)
5.2.3 Problems with secondary source data
74(1)
5.2.4 Internal sources of secondary information
75(3)
5.3 Root cause analysis
78(9)
5.3.1 General concepts
78(3)
5.3.2 Methods and tools in RCA
81(4)
5.3.3 Root cause analysis and customer satisfaction
85(2)
5.4 Summary
87(2)
Acknowledgement
87(1)
References
87(2)
6 Web surveys
89(18)
Roberto Furlan
Diego Martone
6.1 Introduction
89(1)
6.2 Main types of web surveys
90(1)
6.3 Economic benefits of web survey research
91(3)
6.3.1 Fixed and variable costs
92(2)
6.4 Non-economic benefits of web survey research
94(2)
6.5 Main drawbacks of web survey research
96(4)
6.6 Web surveys for customer and employee satisfaction projects
100(2)
6.7 Summary
102(5)
References
102(5)
7 The concept and assessment of customer satisfaction
107(22)
Irena Ograjensek
Iddo Gal
7.1 Introduction
107(1)
7.2 The quality-satisfaction-loyalty chain
108(7)
7.2.1 Rationale
108(1)
7.2.2 Definitions of customer satisfaction
108(2)
7.2.3 From general conceptions to a measurement model of customer satisfaction
110(2)
7.2.4 Going beyond Servqual: Other dimensions of relevance to the B2B context
112(1)
7.2.5 From customer satisfaction to customer loyalty
113(2)
7.3 Customer satisfaction assessment: Some methodological considerations
115(4)
7.3.1 Rationale
115(1)
7.3.2 Think big: An assessment programme
115(1)
7.3.3 Back to basics: Questionnaire design
116(2)
7.3.4 Impact of questionnaire design on interpretation
118(1)
7.3.5 Additional concerns in the B2B setting
119(1)
7.4 The ABC ACSS questionnaire: An evaluation
119(2)
7.4.1 Rationale
119(1)
7.4.2 Conceptual issues
119(1)
7.4.3 Methodological issues
120(1)
7.4.4 Overall ABC ACSS questionnaire asssessment
121(1)
7.5 Summary
121(8)
References
122(4)
Appendix
126(3)
8 Missing data and imputation methods
129(26)
Alessandra Mattei
Fabrizia Mealli
Donald B. Rubin
8.1 Introduction
129(2)
8.2 Missing-data patterns and missing-data mechanisms
131(3)
8.2.1 Missing-data patterns
131(1)
8.2.2 Missing-data mechanisms and ignorability
132(2)
8.3 Simple approaches to the missing-data problem
134(2)
8.3.1 Complete-case analysis
134(1)
8.3.2 Available-case analysis
135(1)
8.3.3 Weighting adjustment for unit nonresponse
135(1)
8.4 Single imputation
136(2)
8.5 Multiple imputation
138(6)
8.5.1 Multiple-imputation inference for a scalar estimand
138(1)
8.5.2 Proper multiple imputation
139(1)
8.5.3 Appropriately drawing imputations with monotone missing-data patterns
140(1)
8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns
141(1)
8.5.5 Multiple imputation in practice
142(1)
8.5.6 Software for multiple imputation
143(1)
8.6 Model-based approaches to the analysis of missing data
144(1)
8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example
145(4)
8.8 Summary
149(6)
Acknowledgements
150(1)
References
150(5)
9 Outliers and robustness for ordinal data
155(18)
Marco Riani
Francesca Torti
Sergio Zani
9.1 An overview of outlier detection methods
155(2)
9.2 An example of masking
157(2)
9.3 Detection of outliers in ordinal variables
159(1)
9.4 Detection of bivariate ordinal outliers
160(1)
9.5 Detection of multivariate outliers in ordinal regression
161(7)
9.5.1 Theory
161(2)
9.5.2 Results from the application
163(5)
9.6 Summary
168(5)
References
168(5)
PART II MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
10 Statistical inference for causal effects
173(20)
Fabrizia Mealli
Barbara Pacini
Donald B. Rubin
10.1 Introduction to the potential outcome approach to causal inference
173(6)
10.1.1 Causal inference primitives: Units, treatments, and potential outcomes
175(1)
10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption
176(1)
10.1.3 Defining causal estimands
177(2)
10.2 Assignment mechanisms
179(3)
10.2.1 The criticality of the assignment mechanism
179(1)
10.2.2 Unconfounded and strongly ignorable assignment mechanisms
180(1)
10.2.3 Confounded and ignorable assignment mechanisms
181(1)
10.2.4 Randomized and observational studies
181(1)
10.3 Inference in classical randomized experiments
182(3)
10.3.1 Fisher's approach and extensions
183(1)
10.3.2 Neyman's approach to randomization-based inference
183(1)
10.3.3 Covariates, regression models, and Bayesian model-based inference
184(1)
10.4 Inference in observational studies
185(8)
10.4.1 Inference in regular designs
186(1)
10.4.2 Designing observational studies: The role of the propensity score
186(2)
10.4.3 Estimation methods
188(1)
10.4.4 Inference in irregular designs
188(1)
10.4.5 Sensitivity and bounds
189(1)
10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs
189(1)
References
190(3)
11 Bayesian networks applied to customer surveys
193(24)
Ron S. Kenett
Giovanni Perruca
Silvia Salini
11.1 Introduction to Bayesian networks
193(4)
11.2 The Bayesian network model in practice
197(14)
11.2.1 Bayesian network analysis of the ABC 2010 ACSS
197(4)
11.2.2 Transport data analysis
201(9)
11.2.3 R packages and other software programs used for studying BNs
210(1)
11.3 Prediction and explanation
211(2)
11.4 Summary
213(4)
References
213(4)
12 Log-linear model methods
217(14)
Stephen E. Fienberg
Daniel Manrique-Vallier
12.1 Introduction
217(1)
12.2 Overview of log-linear models and methods
218(6)
12.2.1 Two-way tables
218(2)
12.2.2 Hierarchical log-linear models
220(2)
12.2.3 Model search and selection
222(1)
12.2.4 Sparseness in contingency tables and its implications
223(1)
12.2.5 Computer programs for log-linear model analysis
223(1)
12.3 Application to ABC survey data
224(3)
12.4 Summary
227(4)
References
228(3)
13 CUB models: Statistical methods and empirical evidence
231(28)
Maria Iannario
Domenico Piccolo
13.1 Introduction
231(2)
13.2 Logical foundations and psychological motivations
233(1)
13.3 A class of models for ordinal data
233(3)
13.4 Main inferential issues
236(2)
13.5 Specification of CUB models with subjects' covariates
238(2)
13.6 Interpreting the role of covariates
240(1)
13.7 A more general sampling framework
241(3)
13.7.1 Objects' covariates
241(2)
13.7.2 Contextual covariates
243(1)
13.8 Applications of CUB models
244(4)
13.8.1 Models for the ABC annual customer satisfaction survey
245(1)
13.8.2 Students' satisfaction with a university orientation service
246(2)
13.9 Further generalizations
248(3)
13.10 Concluding remarks
251(8)
Acknowledgements
251(1)
References
251(4)
Appendix
255(1)
A program in R for CUB models
255(1)
A.1 Main structure of the program
255(1)
A.2 Inference on CUB models
255(1)
A.3 Output of CUB models estimation program
256(1)
A.4 Visualization of several CUB models in the parameter space
257(1)
A.5 Inference on CUB models in a multi-object framework
257(1)
A.6 Advanced software support for CUB models
258(1)
14 The Rasch model
259(24)
Francesca De Battisti
Giovanna Nicolini
Silvia Salini
14.1 An overview of the Rasch model
259(8)
14.1.1 The origins and the properties of the model
259(4)
14.1.2 Rasch model for hierarchical and longitudinal data
263(2)
14.1.3 Rasch model applications in customer satisfaction surveys
265(2)
14.2 The Rasch model in practice
267(10)
14.2.1 Single model
267(1)
14.2.2 Overall model
268(4)
14.2.3 Dimension model
272(5)
14.3 Rasch model software
277(1)
14.4 Summary
278(5)
References
279(4)
15 Tree-based methods and decision trees
283(26)
Giuliano Galimberti
Gabriele Soffritti
15.1 An overview of tree-based methods and decision trees
283(17)
15.1.1 The origins of tree-based methods
283(1)
15.1.2 Tree graphs, tree-based methods and decision trees
284(3)
15.1.3 CART
287(6)
15.1.4 CHAID
293(2)
15.1.5 PARTY
295(2)
15.1.6 A comparison of CART, CHAID and PARTY
297(1)
15.1.7 Missing values
297(1)
15.1.8 Tree-based methods for applications in customer satisfaction surveys
298(2)
15.2 Tree-based methods and decision trees in practice
300(4)
15.2.1 ABC ACSS data analysis with tree-based methods
300(3)
15.2.2 Packages and software implementing tree-based methods
303(1)
15.3 Further developments
304(5)
References
304(5)
16 PLS models
309(24)
Giuseppe Boari
Gabriele Cantaluppi
16.1 Introduction
309(1)
16.2 The general formulation of a structural equation model
310(3)
16.2.1 The inner model
310(2)
16.2.2 The outer model
312(1)
16.3 The PLS algorithm
313(6)
16.4 Statistical interpretation of PLS
319(1)
16.5 Geometrical interpretation of PLS
320(1)
16.6 Comparison of the properties of PLS and LISREL procedures
321(2)
16.7 Available software for PLS estimation
323(1)
16.8 Application to real data: Customer satisfaction analysis
323(10)
References
329(4)
17 Nonlinear principal component analysis
333(24)
Pier Alda Ferrari
Alessandro Barbiero
17.1 Introduction
333(1)
17.2 Homogeneity analysis and nonlinear principal component analysis
334(4)
17.2.1 Homogeneity analysis
334(2)
17.2.2 Nonlinear principal component analysis
336(2)
17.3 Analysis of customer satisfaction
338(2)
17.3.1 The setting up of indicator
338(2)
17.3.2 Additional analysis
340(1)
17.4 Dealing with missing data
340(3)
17.5 Nonlinear principal component analysis versus two competitors
343(1)
17.6 Application to the ABC ACSS data
344(11)
17.6.1 Data preparation
344(1)
17.6.2 The homals package
345(1)
17.6.3 Analysis on the `complete subset'
346(4)
17.6.4 Comparison of NLPCA with PCA and Rasch analysis
350(2)
17.6.5 Analysis of `entire data set' for the comparison of missing data treatments
352(3)
17.7 Summary
355(2)
References
355(2)
18 Multidimensional scaling
357(34)
Nadia Solaro
18.1 An overview of multidimensional scaling techniques
357(17)
18.1.1 The origins of MDS models
358(1)
18.1.2 MDS input data
359(3)
18.1.3 MDS models
362(7)
18.1.4 Assessing the goodness of MDS solutions
369(2)
18.1.5 Comparing two MDS solutions: Procrustes analysis
371(1)
18.1.6 Robustness issues in the MDS framework
371(2)
18.1.7 Handling missing values in MDS framework
373(1)
18.1.8 MDS applications in customer satisfaction surveys
373(1)
18.2 Multidimensional scaling in practice
374(12)
18.2.1 Data sets analysed
375(1)
18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set
375(6)
18.2.3 Weighting objects or items
381(1)
18.2.4 Robustness analysis with the forward search
382(1)
18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set
383(1)
18.2.6 Package and software for MDS methods
384(2)
18.3 Multidimensional scaling in a future perspective
386(1)
18.4 Summary
386(5)
References
387(4)
19 Multilevel models for ordinal data
391(22)
Leonardo Grilli
Carla Rampichini
19.1 Ordinal variables
391(2)
19.2 Standard models for ordinal data
393(2)
19.2.1 Cumulative models
394(1)
19.2.2 Other models
395(1)
19.3 Multilevel models for ordinal data
395(9)
19.3.1 Representation as an underlying linear model with thresholds
396(1)
19.3.2 Marginal versus conditional effects
397(1)
19.3.3 Summarizing the cluster-level unobserved heterogeneity
397(1)
19.3.4 Consequences of adding a covariate
398(1)
19.3.5 Predicted probabilities
399(1)
19.3.6 Cluster-level covariates and contextual effects
399(1)
19.3.7 Estimation of model parameters
400(1)
19.3.8 Inference on model parameters
401(1)
19.3.9 Prediction of random effects
402(1)
19.3.10 Software
403(1)
19.4 Multilevel models for ordinal data in practice: An application to student ratings
404(9)
References
408(5)
20 Quality standards and control charts applied to customer surveys
413(26)
Ron S. Kenett
Laura Deldossi
Diego Zappa
20.1 Quality standards and customer satisfaction
413(1)
20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction
414(3)
20.3 Control Charts and ISO 7870
417(3)
20.4 Control charts and customer surveys: Standard assumptions
420(6)
20.4.1 Introduction
420(1)
20.4.2 Standard control charts
420(6)
20.5 Control charts and customer surveys: Non-standard methods
426(7)
20.5.1 Weights on counts: Another application of the c chart
426(1)
20.5.2 The Χ2 chart
427(1)
20.5.3 Sequential probability ratio tests
428(1)
20.5.4 Control chart over items: A non-standard application of SPC methods
429(3)
20.5.5 Bayesian control chart for attributes: A modern application of SPC methods
432(1)
20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated
433(1)
20.6 The M-test for assessing sample representation
433(2)
20.7 Summary
435(4)
References
436(3)
21 Fuzzy Methods and Satisfaction Indices
439(18)
Sergio Zani
Maria Adele Milioli
Isabella Morlini
21.1 Introduction
439(1)
21.2 Basic definitions and operations
440(1)
21.3 Fuzzy numbers
441(2)
21.4 A criterion for fuzzy transformation of variables
443(2)
21.5 Aggregation and weighting of variables
445(1)
21.6 Application to the ABC customer satisfaction survey data
446(7)
21.6.1 The input matrices
446(2)
21.6.2 Main results
448(5)
21.7 Summary
453(4)
References
455(2)
Appendix An introduction to R
457(42)
Stefano Maria Iacus
A.1 Introduction
457(1)
A.2 How to obtain R
457(1)
A.3 Type rather than `point and click'
458(2)
A.3.1 The workspace
458(1)
A.3.2 Graphics
458(1)
A.3.3 Getting help
459(1)
A.3.4 Installing packages
459(1)
A.4 Objects
460(10)
A.4.1 Assignments
460(2)
A.4.2 Basic object types
462(4)
A.4.3 Accessing objects and subsetting
466(3)
A.4.4 Coercion between data types
469(1)
A.5 S4 objects
470(2)
A.6 Functions
472(1)
A.7 Vectorization
473(2)
A.8 Importing data from different sources
475(1)
A.9 Interacting with databases
476(1)
A.10 Simple graphics manipulation
477(4)
A.11 Basic analysis of the ABC data
481(15)
A.12 About this document
496(1)
A.13 Bibliographical notes
496(3)
References
496(3)
Index 499
Edited by

RON S. KENETT, KPA Ltd., Raanana, Israel, University of Turin, Italy, and NYU-Poly, Center for Risk Engineering, New York, USA

SILVIA SALINI, Department of Economics, Business and Statistics, University of Milan, Italy
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811199613836e.html
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