Modern Analysis of Customer Surveys with Applications using R [Other digital carrier]

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 Ograjen sek 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 2 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

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