Description
Modern Analysis of Customer Surveys
with Applications using R
Statistics in Practice Series
Authors: Kenett Ron S., Salini Silvia
Language: English
Subject for Modern Analysis of Customer Surveys:
524 p. · 17.5x25.2 cm · Hardback
Description
/li>Contents
/li>Biography
/li>
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:
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:
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
Silvia Salini and Ron S. Kenett
1.1 Literature on customer satisfaction surveys 4
1.2 Customer satisfaction surveys and the business cycle 4
1.3 Standards used in the analysis of survey data 7
1.4 Measures and models of customer satisfaction 12
1.4.1 The conceptual construct 12
1.4.2 The measurement process 13
1.5 Organization of the book 15
1.6 Summary 17
References 17
2 The ABC Annual Customer Satisfaction Survey 19
Ron S. Kenett and Silvia Salini
2.1 The ABC company 19
2.2 ABC 2010 ACSS: Demographics of respondents 20
2.3 ABC 2010 ACSS: Overall satisfaction 22
2.4 ABC 2010 ACSS: Analysis of topics 24
2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27
2.6 Summary 28
References 28
Appendix 29
3 Census and Sample Surveys 37
Giovanna Nicolini and Luciana Dalla Valle
3.1 Introduction 37
3.2 Types of surveys 39
3.2.1 Census and sample surveys 39
3.2.2 Sampling design 40
3.2.3 Managing a survey 40
3.2.4 Frequency of surveys 41
3.3 Non-sampling errors 41
3.3.1 Measurement error 42
3.3.2 Coverage error 42
3.3.3 Unit non-response and non-self-selection errors 43
3.3.4 Item non-response and non-self-selection error 44
3.4 Data collection methods 44
3.5 Methods to correct non-sampling errors 46
3.5.1 Methods to correct unit non-response errors 46
3.5.2 Methods to correct item non-response 49
3.6 Summary 51
References 52
4 Measurement Scales 55
Andrea Bonanomi and Gabriele Cantaluppi
4.1 Scale construction 55
4.1.1 Nominal scale 56
4.1.2 Ordinal scale 57
4.1.3 Interval scale 58
4.1.4 Ratio scale 59
4.2 Scale transformations 60
4.2.1 Scale transformations referred to single items 61
4.2.2 Scale transformations to obtain scores on a unique interval scale 66
Acknowledgements 69
References 69
5 Integrated Analysis 71
Silvia Biffignandi
5.1 Introduction 71
5.2 Information sources and related problems 73
5.2.1 Types of data sources 73
5.2.2 Advantages of using secondary source data 73
5.2.3 Problems with secondary source data 74
5.2.4 Internal sources of secondary information 75
5.3 Root cause analysis 78
5.3.1 General concepts 78
5.3.2 Methods and tools in RCA 81
5.3.3 Root cause analysis and customer satisfaction 85
5.4 Summary 87
Acknowledgement 87
References 87
6 Web Surveys 89
Roberto Furlan and Diego Martone
6.1 Introduction 89
6.2 Main types of web surveys 90
6.3 Economic benefits of web survey research 91
6.3.1 Fixed and variable costs 92
6.4 Non-economic benefits of web survey research 94
6.5 Main drawbacks of web survey research 96
6.6 Web surveys for customer and employee satisfaction projects 100
6.7 Summary 102
References 102
7 The Concept and Assessment of Customer Satisfaction 107
Irena Ograjenšek and Iddo Gal
7.1 Introduction 107
7.2 The quality–satisfaction–loyalty chain 108
7.2.1 Rationale 108
7.2.2 Definitions of customer satisfaction 108
7.2.3 From general conceptions to a measurement model of customer satisfaction 110
7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112
7.2.5 From customer satisfaction to customer loyalty 113
7.3 Customer satisfaction assessment: Some methodological considerations 115
7.3.1 Rationale 115
7.3.2 Think big: An assessment programme 115
7.3.3 Back to basics: Questionnaire design 116
7.3.4 Impact of questionnaire design on interpretation 118
7.3.5 Additional concerns in the B2B setting 119
7.4 The ABC ACSS questionnaire: An evaluation 119
7.4.1 Rationale 119
7.4.2 Conceptual issues 119
7.4.3 Methodological issues 120
7.4.4 Overall ABC ACSS questionnaire asssessment 121
7.5 Summary 121
References 122
Appendix 126
8 Missing Data and Imputation Methods 129
Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin
8.1 Introduction 129
8.2 Missing-data patterns and missing-data mechanisms 131
8.2.1 Missing-data patterns 131
8.2.2 Missing-data mechanisms and ignorability 132
8.3 Simple approaches to the missing-data problem 134
8.3.1 Complete-case analysis 134
8.3.2 Available-case analysis 135
8.3.3 Weighting adjustment for unit nonresponse 135
8.4 Single imputation 136
8.5 Multiple imputation 138
8.5.1 Multiple-imputation inference for a scalar estimand 138
8.5.2 Proper multiple imputation 139
8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140
8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141
8.5.5 Multiple imputation in practice 142
8.5.6 Software for multiple imputation 143
8.6 Model-based approaches to the analysis of missing data 144
8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145
8.8 Summary 149
Acknowledgements 150
References 150
9 Outliers and Robustness for Ordinal Data 155
Marco Riani, Francesca Torti and Sergio Zani
9.1 An overview of outlier detection methods 155
9.2 An example of masking 157
9.3 Detection of outliers in ordinal variables 159
9.4 Detection of bivariate ordinal outliers 160
9.5 Detection of multivariate outliers in ordinal regression 161
9.5.1 Theory 161
9.5.2 Results from the application 163
9.6 Summary 168
References 168
Part II Modern Techniques in Customer Satisfaction Survey Data Analysis
10 Statistical Inference for Causal Effects 173
Fabrizia Mealli, Barbara Pacini and Donald B. Rubin
10.1 Introduction to the potential outcome approach to causal inference 173
10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175
10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176
10.1.3 Defining causal estimands 177
10.2 Assignment mechanisms 179
10.2.1 The criticality of the assignment mechanism 179
10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180
10.2.3 Confounded and ignorable assignment mechanisms 181
10.2.4 Randomized and observational studies 181
10.3 Inference in classical randomized experiments 182
10.3.1 Fisher’s approach and extensions 183
10.3.2 Neyman’s approach to randomization-based inference 183
10.3.3 Covariates, regression models, and Bayesian model-based inference 184
10.4 Inference in observational studies 185
10.4.1 Inference in regular designs 186
10.4.2 Designing observational studies: The role of the propensity score 186
10.4.3 Estimation methods 188
10.4.4 Inference in irregular designs 188
10.4.5 Sensitivity and bounds 189
10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189
References 190
11 Bayesian Networks Applied to Customer Surveys 193
Ron S. Kenett, Giovanni Perruca and Silvia Salini
11.1 Introduction to Bayesian networks 193
11.2 The Bayesian network model in practice 197
11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197
11.2.2 Transport data analysis 201
11.2.3 R packages and other software programs used for studying BNs 210
11.3 Prediction and explanation 211
11.4 Summary 213
References 213
12 Log-linear Model Methods 217
Stephen E. Fienberg and Daniel Manrique-Vallier
12.1 Introduction 217
12.2 Overview of log-linear models and methods 218
12.2.1 Two-way tables 218
12.2.2 Hierarchical log-linear models 220
12.2.3 Model search and selection 222
12.2.4 Sparseness in contingency tables and its implications 223
12.2.5 Computer programs for log-linear model analysis 223
12.3 Application to ABC survey data 224
12.4 Summary 227
References 228
13 CUB Models: Statistical Methods and Empirical Evidence 231
Maria Iannario and Domenico Piccolo
13.1 Introduction 231
13.2 Logical foundations and psychological motivations 233
13.3 A class of models for ordinal data 233
13.4 Main inferential issues 236
13.5 Specification of CUB models with subjects’ covariates 238
13.6 Interpreting the role of covariates 240
13.7 A more general sampling framework 241
13.7.1 Objects’ covariates 241
13.7.2 Contextual covariates 243
13.8 Applications of CUB models 244
13.8.1 Models for the ABC annual customer satisfaction survey 245
13.8.2 Students’ satisfaction with a university orientation service 246
13.9 Further generalizations 248
13.10 Concluding remarks 251
Acknowledgements 251
References 251
Appendix 255
A program in R for CUB models 255
A.1 Main structure of the program 255
A.2 Inference on CUB models 255
A.3 Output of CUB models estimation program 256
A.4 Visualization of several CUB models in the parameter space 257
A.5 Inference on CUB models in a multi-object framework 257
A.6 Advanced software support for CUB models 258
14 The Rasch Model 259
Francesca De Battisti, Giovanna Nicolini and Silvia Salini
14.1 An overview of the Rasch model 259
14.1.1 The origins and the properties of the model 259
14.1.2 Rasch model for hierarchical and longitudinal data 263
14.1.3 Rasch model applications in customer satisfaction surveys 265
14.2 The Rasch model in practice 267
14.2.1 Single model 267
14.2.2 Overall model 268
14.2.3 Dimension model 272
14.3 Rasch model software 277
14.4 Summary 278
References 279
15 Tree-based Methods and Decision Trees 283
Giuliano Galimberti and Gabriele Soffritti
15.1 An overview of tree-based methods and decision trees 283
15.1.1 The origins of tree-based methods 283
15.1.2 Tree graphs, tree-based methods and decision trees 284
15.1.3 CART 287
15.1.4 CHAID 293
15.1.5 PARTY 295
15.1.6 A comparison of CART, CHAID and PARTY 297
15.1.7 Missing values 297
15.1.8 Tree-based methods for applications in customer satisfaction surveys 298
15.2 Tree-based methods and decision trees in practice 300
15.2.1 ABC ACSS data analysis with tree-based methods 300
15.2.2 Packages and software implementing tree-based methods 303
15.3 Further developments 304
References 304
16 PLS Models 309
Giuseppe Boari and Gabriele Cantaluppi
16.1 Introduction 309
16.2 The general formulation of a structural equation model 310
16.2.1 The inner model 310
16.2.2 The outer model 312
16.3 The PLS algorithm 313
16.4 Statistical interpretation of PLS 319
16.5 Geometrical interpretation of PLS 320
16.6 Comparison of the properties of PLS and LISREL procedures 321
16.7 Available software for PLS estimation 323
16.8 Application to real data: Customer satisfaction analysis 323
References 329
17 Nonlinear Principal Component Analysis 333
Pier Alda Ferrari and Alessandro Barbiero
17.1 Introduction 333
17.2 Homogeneity analysis and nonlinear principal component analysis 334
17.2.1 Homogeneity analysis 334
17.2.2 Nonlinear principal component analysis 336
17.3 Analysis of customer satisfaction 338
17.3.1 The setting up of indicator 338
17.3.2 Additional analysis 340
17.4 Dealing with missing data 340
17.5 Nonlinear principal component analysis versus two competitors 343
17.6 Application to the ABC ACSS data 344
17.6.1 Data preparation 344
17.6.2 The homals package 345
17.6.3 Analysis on the ‘complete subset’ 346
17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350
17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352
17.7 Summary 355
References 355
18 Multidimensional Scaling 357
Nadia Solaro
18.1 An overview of multidimensional scaling techniques 357
18.1.1 The origins of MDS models 358
18.1.2 MDS input data 359
18.1.3 MDS models 362
18.1.4 Assessing the goodness of MDS solutions 369
18.1.5 Comparing two MDS solutions: Procrustes analysis 371
18.1.6 Robustness issues in the MDS framework 371
18.1.7 Handling missing values in MDS framework 373
18.1.8 MDS applications in customer satisfaction surveys 373
18.2 Multidimensional scaling in practice 374
18.2.1 Data sets analysed 375
18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375
18.2.3 Weighting objects or items 381
18.2.4 Robustness analysis with the forward search 382
18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383
18.2.6 Package and software for MDS methods 384
18.3 Multidimensional scaling in a future perspective 386
18.4 Summary 386
References 387
19 Multilevel Models for Ordinal Data 391
Leonardo Grilli and Carla Rampichini
19.1 Ordinal variables 391
19.2 Standard models for ordinal data 393
19.2.1 Cumulative models 394
19.2.2 Other models 395
19.3 Multilevel models for ordinal data 395
19.3.1 Representation as an underlying linear model with thresholds 396
19.3.2 Marginal versus conditional effects 397
19.3.3 Summarizing the cluster-level unobserved heterogeneity 397
19.3.4 Consequences of adding a covariate 398
19.3.5 Predicted probabilities 399
19.3.6 Cluster-level covariates and contextual effects 399
19.3.7 Estimation of model parameters 400
19.3.8 Inference on model parameters 401
19.3.9 Prediction of random effects 402
19.3.10 Software 403
19.4 Multilevel models for ordinal data in practice: An application to student ratings 404
References 408
20 Quality Standards and Control Charts Applied to Customer Surveys 413
Ron S. Kenett, Laura Deldossi and Diego Zappa
20.1 Quality standards and customer satisfaction 413
20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414
20.3 Control Charts and ISO 7870 417
20.4 Control charts and customer surveys: Standard assumptions 420
20.4.1 Introduction 420
20.4.2 Standard control charts 420
20.5 Control charts and customer surveys: Non-standard methods 426
20.5.1 Weights on counts: Another application of the c chart 426
20.5.2 The χ2 chart 427
20.5.3 Sequential probability ratio tests 428
20.5.4 Control chart over items: A non-standard application of SPC methods 429
20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432
20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433
20.6 The M-test for assessing sample representation 433
20.7 Summary 435
References 436
21 Fuzzy Methods and Satisfaction Indices 439
Sergio Zani, Maria Adele Milioli and Isabella Morlini
21.1 Introduction 439
21.2 Basic definitions and operations 440
21.3 Fuzzy numbers 441
21.4 A criterion for fuzzy transformation of variables 443
21.5 Aggregation and weighting of variables 445
21.6 Application to the ABC customer satisfaction survey data 446
21.6.1 The input matrices 446
21.6.2 Main results 448
21.7 Summary 453
References 455
Appendix an Introduction to R 457
Stefano Maria Iacus
A.1 Introduction 457
A.2 How to obtain R 457
A.3 Type rather than ‘point and click’ 458
A.3.1 The workspace 458
A.3.2 Graphics 458
A.3.3 Getting help 459
A.3.4 Installing packages 459
A.4 Objects 460
A.4.1 Assignments 460
A.4.2 Basic object types 462
A.4.3 Accessing objects and subsetting 466
A.4.4 Coercion between data types 469
A.5 S4 objects 470
A.6 Functions 472
A.7 Vectorization 473
A.8 Importing data from different sources 475
A.9 Interacting with databases 476
A.10 Simple graphics manipulation 477
A.11 Basic analysis of the ABC data 481
A.12 About this document 496
A.13 Bibliographical notes 496
References 496
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
