{"product_id":"modern-analysis-of-customer-surveys-isbn-9780470971284","title":"Modern Analysis of Customer Surveys","description":"\u003cp\u003e\u003cb\u003eModern Analysis of Customer Surveys: with applications using R\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eCustomer 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features:\u003c\/b\u003e \u003cb\u003e\u003cul\u003e\u003cli\u003e Provides an integrated case studies-based approach to analysing customer survey data.\u003c\/li\u003e\u003c\/ul\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003ePresents a general introduction to customer surveys, within an organization’s business cycle.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eContains classical techniques with modern and non standard tools.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eFocuses on probabilistic techniques from the area of statistics\/data analysis and covers all major recent developments.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eAccompanied by a supporting website containing datasets and R scripts.\u003c\/li\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eCustomer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields. \u003c\/p\u003e\u003cp\u003ewww.wiley.com\/go\/modern_analysis \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSTATISTICS IN PRACTICE\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eA series of practical books outlining the use of statistical techniques in a wide range of applications areas: \u003cb\u003e\u003cul\u003e\u003cli\u003eHUMAN AND BIOLOGICAL SCIENCES\u003c\/li\u003e\u003c\/ul\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eEARTH AND ENVIRONMENTAL SCIENCES\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eINDUSTRY, COMMERCE AND FINANCE\u003c\/li\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eContributors xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Basic Aspects of Customer Satisfaction Survey Data Analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSilvia Salini and Ron S. Kenett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Literature on customer satisfaction surveys 4\u003c\/p\u003e \u003cp\u003e1.2 Customer satisfaction surveys and the business cycle 4\u003c\/p\u003e \u003cp\u003e1.3 Standards used in the analysis of survey data 7\u003c\/p\u003e \u003cp\u003e1.4 Measures and models of customer satisfaction 12\u003c\/p\u003e \u003cp\u003e1.4.1 The conceptual construct 12\u003c\/p\u003e \u003cp\u003e1.4.2 The measurement process 13\u003c\/p\u003e \u003cp\u003e1.5 Organization of the book 15\u003c\/p\u003e \u003cp\u003e1.6 Summary 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The ABC Annual Customer Satisfaction Survey 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 The ABC company 19\u003c\/p\u003e \u003cp\u003e2.2 ABC 2010 ACSS: Demographics of respondents 20\u003c\/p\u003e \u003cp\u003e2.3 ABC 2010 ACSS: Overall satisfaction 22\u003c\/p\u003e \u003cp\u003e2.4 ABC 2010 ACSS: Analysis of topics 24\u003c\/p\u003e \u003cp\u003e2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27\u003c\/p\u003e \u003cp\u003e2.6 Summary 28\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003eAppendix 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Census and Sample Surveys 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiovanna Nicolini and Luciana Dalla Valle\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 37\u003c\/p\u003e \u003cp\u003e3.2 Types of surveys 39\u003c\/p\u003e \u003cp\u003e3.2.1 Census and sample surveys 39\u003c\/p\u003e \u003cp\u003e3.2.2 Sampling design 40\u003c\/p\u003e \u003cp\u003e3.2.3 Managing a survey 40\u003c\/p\u003e \u003cp\u003e3.2.4 Frequency of surveys 41\u003c\/p\u003e \u003cp\u003e3.3 Non-sampling errors 41\u003c\/p\u003e \u003cp\u003e3.3.1 Measurement error 42\u003c\/p\u003e \u003cp\u003e3.3.2 Coverage error 42\u003c\/p\u003e \u003cp\u003e3.3.3 Unit non-response and non-self-selection errors 43\u003c\/p\u003e \u003cp\u003e3.3.4 Item non-response and non-self-selection error 44\u003c\/p\u003e \u003cp\u003e3.4 Data collection methods 44\u003c\/p\u003e \u003cp\u003e3.5 Methods to correct non-sampling errors 46\u003c\/p\u003e \u003cp\u003e3.5.1 Methods to correct unit non-response errors 46\u003c\/p\u003e \u003cp\u003e3.5.2 Methods to correct item non-response 49\u003c\/p\u003e \u003cp\u003e3.6 Summary 51\u003c\/p\u003e \u003cp\u003eReferences 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Measurement Scales 55\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrea Bonanomi and Gabriele Cantaluppi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Scale construction 55\u003c\/p\u003e \u003cp\u003e4.1.1 Nominal scale 56\u003c\/p\u003e \u003cp\u003e4.1.2 Ordinal scale 57\u003c\/p\u003e \u003cp\u003e4.1.3 Interval scale 58\u003c\/p\u003e \u003cp\u003e4.1.4 Ratio scale 59\u003c\/p\u003e \u003cp\u003e4.2 Scale transformations 60\u003c\/p\u003e \u003cp\u003e4.2.1 Scale transformations referred to single items 61\u003c\/p\u003e \u003cp\u003e4.2.2 Scale transformations to obtain scores on a unique interval scale 66\u003c\/p\u003e \u003cp\u003eAcknowledgements 69\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Integrated Analysis 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSilvia Biffignandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 71\u003c\/p\u003e \u003cp\u003e5.2 Information sources and related problems 73\u003c\/p\u003e \u003cp\u003e5.2.1 Types of data sources 73\u003c\/p\u003e \u003cp\u003e5.2.2 Advantages of using secondary source data 73\u003c\/p\u003e \u003cp\u003e5.2.3 Problems with secondary source data 74\u003c\/p\u003e \u003cp\u003e5.2.4 Internal sources of secondary information 75\u003c\/p\u003e \u003cp\u003e5.3 Root cause analysis 78\u003c\/p\u003e \u003cp\u003e5.3.1 General concepts 78\u003c\/p\u003e \u003cp\u003e5.3.2 Methods and tools in RCA 81\u003c\/p\u003e \u003cp\u003e5.3.3 Root cause analysis and customer satisfaction 85\u003c\/p\u003e \u003cp\u003e5.4 Summary 87\u003c\/p\u003e \u003cp\u003eAcknowledgement 87\u003c\/p\u003e \u003cp\u003eReferences 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Web Surveys 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRoberto Furlan and Diego Martone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 89\u003c\/p\u003e \u003cp\u003e6.2 Main types of web surveys 90\u003c\/p\u003e \u003cp\u003e6.3 Economic benefits of web survey research 91\u003c\/p\u003e \u003cp\u003e6.3.1 Fixed and variable costs 92\u003c\/p\u003e \u003cp\u003e6.4 Non-economic benefits of web survey research 94\u003c\/p\u003e \u003cp\u003e6.5 Main drawbacks of web survey research 96\u003c\/p\u003e \u003cp\u003e6.6 Web surveys for customer and employee satisfaction projects 100\u003c\/p\u003e \u003cp\u003e6.7 Summary 102\u003c\/p\u003e \u003cp\u003eReferences 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Concept and Assessment of Customer Satisfaction 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIrena Ograjenšek and Iddo Gal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 The quality–satisfaction–loyalty chain 108\u003c\/p\u003e \u003cp\u003e7.2.1 Rationale 108\u003c\/p\u003e \u003cp\u003e7.2.2 Definitions of customer satisfaction 108\u003c\/p\u003e \u003cp\u003e7.2.3 From general conceptions to a measurement model of customer satisfaction 110\u003c\/p\u003e \u003cp\u003e7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112\u003c\/p\u003e \u003cp\u003e7.2.5 From customer satisfaction to customer loyalty 113\u003c\/p\u003e \u003cp\u003e7.3 Customer satisfaction assessment: Some methodological considerations 115\u003c\/p\u003e \u003cp\u003e7.3.1 Rationale 115\u003c\/p\u003e \u003cp\u003e7.3.2 Think big: An assessment programme 115\u003c\/p\u003e \u003cp\u003e7.3.3 Back to basics: Questionnaire design 116\u003c\/p\u003e \u003cp\u003e7.3.4 Impact of questionnaire design on interpretation 118\u003c\/p\u003e \u003cp\u003e7.3.5 Additional concerns in the B2B setting 119\u003c\/p\u003e \u003cp\u003e7.4 The ABC ACSS questionnaire: An evaluation 119\u003c\/p\u003e \u003cp\u003e7.4.1 Rationale 119\u003c\/p\u003e \u003cp\u003e7.4.2 Conceptual issues 119\u003c\/p\u003e \u003cp\u003e7.4.3 Methodological issues 120\u003c\/p\u003e \u003cp\u003e7.4.4 Overall ABC ACSS questionnaire asssessment 121\u003c\/p\u003e \u003cp\u003e7.5 Summary 121\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003eAppendix 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Missing Data and Imputation Methods 129\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlessandra Mattei, Fabrizia Mealli and Donald B. Rubin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 129\u003c\/p\u003e \u003cp\u003e8.2 Missing-data patterns and missing-data mechanisms 131\u003c\/p\u003e \u003cp\u003e8.2.1 Missing-data patterns 131\u003c\/p\u003e \u003cp\u003e8.2.2 Missing-data mechanisms and ignorability 132\u003c\/p\u003e \u003cp\u003e8.3 Simple approaches to the missing-data problem 134\u003c\/p\u003e \u003cp\u003e8.3.1 Complete-case analysis 134\u003c\/p\u003e \u003cp\u003e8.3.2 Available-case analysis 135\u003c\/p\u003e \u003cp\u003e8.3.3 Weighting adjustment for unit nonresponse 135\u003c\/p\u003e \u003cp\u003e8.4 Single imputation 136\u003c\/p\u003e \u003cp\u003e8.5 Multiple imputation 138\u003c\/p\u003e \u003cp\u003e8.5.1 Multiple-imputation inference for a scalar estimand 138\u003c\/p\u003e \u003cp\u003e8.5.2 Proper multiple imputation 139\u003c\/p\u003e \u003cp\u003e8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140\u003c\/p\u003e \u003cp\u003e8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141\u003c\/p\u003e \u003cp\u003e8.5.5 Multiple imputation in practice 142\u003c\/p\u003e \u003cp\u003e8.5.6 Software for multiple imputation 143\u003c\/p\u003e \u003cp\u003e8.6 Model-based approaches to the analysis of missing data 144\u003c\/p\u003e \u003cp\u003e8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145\u003c\/p\u003e \u003cp\u003e8.8 Summary 149\u003c\/p\u003e \u003cp\u003eAcknowledgements 150\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Outliers and Robustness for Ordinal Data 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMarco Riani, Francesca Torti and Sergio Zani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 An overview of outlier detection methods 155\u003c\/p\u003e \u003cp\u003e9.2 An example of masking 157\u003c\/p\u003e \u003cp\u003e9.3 Detection of outliers in ordinal variables 159\u003c\/p\u003e \u003cp\u003e9.4 Detection of bivariate ordinal outliers 160\u003c\/p\u003e \u003cp\u003e9.5 Detection of multivariate outliers in ordinal regression 161\u003c\/p\u003e \u003cp\u003e9.5.1 Theory 161\u003c\/p\u003e \u003cp\u003e9.5.2 Results from the application 163\u003c\/p\u003e \u003cp\u003e9.6 Summary 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Modern Techniques in Customer Satisfaction Survey Data Analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Statistical Inference for Causal Effects 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFabrizia Mealli, Barbara Pacini and Donald B. Rubin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction to the potential outcome approach to causal inference 173\u003c\/p\u003e \u003cp\u003e10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175\u003c\/p\u003e \u003cp\u003e10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176\u003c\/p\u003e \u003cp\u003e10.1.3 Defining causal estimands 177\u003c\/p\u003e \u003cp\u003e10.2 Assignment mechanisms 179\u003c\/p\u003e \u003cp\u003e10.2.1 The criticality of the assignment mechanism 179\u003c\/p\u003e \u003cp\u003e10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180\u003c\/p\u003e \u003cp\u003e10.2.3 Confounded and ignorable assignment mechanisms 181\u003c\/p\u003e \u003cp\u003e10.2.4 Randomized and observational studies 181\u003c\/p\u003e \u003cp\u003e10.3 Inference in classical randomized experiments 182\u003c\/p\u003e \u003cp\u003e10.3.1 Fisher’s approach and extensions 183\u003c\/p\u003e \u003cp\u003e10.3.2 Neyman’s approach to randomization-based inference 183\u003c\/p\u003e \u003cp\u003e10.3.3 Covariates, regression models, and Bayesian model-based inference 184\u003c\/p\u003e \u003cp\u003e10.4 Inference in observational studies 185\u003c\/p\u003e \u003cp\u003e10.4.1 Inference in regular designs 186\u003c\/p\u003e \u003cp\u003e10.4.2 Designing observational studies: The role of the propensity score 186\u003c\/p\u003e \u003cp\u003e10.4.3 Estimation methods 188\u003c\/p\u003e \u003cp\u003e10.4.4 Inference in irregular designs 188\u003c\/p\u003e \u003cp\u003e10.4.5 Sensitivity and bounds 189\u003c\/p\u003e \u003cp\u003e10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bayesian Networks Applied to Customer Surveys 193\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett, Giovanni Perruca and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction to Bayesian networks 193\u003c\/p\u003e \u003cp\u003e11.2 The Bayesian network model in practice 197\u003c\/p\u003e \u003cp\u003e11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197\u003c\/p\u003e \u003cp\u003e11.2.2 Transport data analysis 201\u003c\/p\u003e \u003cp\u003e11.2.3 R packages and other software programs used for studying BNs 210\u003c\/p\u003e \u003cp\u003e11.3 Prediction and explanation 211\u003c\/p\u003e \u003cp\u003e11.4 Summary 213\u003c\/p\u003e \u003cp\u003eReferences 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Log-linear Model Methods 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eStephen E. Fienberg and Daniel Manrique-Vallier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 217\u003c\/p\u003e \u003cp\u003e12.2 Overview of log-linear models and methods 218\u003c\/p\u003e \u003cp\u003e12.2.1 Two-way tables 218\u003c\/p\u003e \u003cp\u003e12.2.2 Hierarchical log-linear models 220\u003c\/p\u003e \u003cp\u003e12.2.3 Model search and selection 222\u003c\/p\u003e \u003cp\u003e12.2.4 Sparseness in contingency tables and its implications 223\u003c\/p\u003e \u003cp\u003e12.2.5 Computer programs for log-linear model analysis 223\u003c\/p\u003e \u003cp\u003e12.3 Application to ABC survey data 224\u003c\/p\u003e \u003cp\u003e12.4 Summary 227\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 CUB Models: Statistical Methods and Empirical Evidence 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMaria Iannario and Domenico Piccolo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 231\u003c\/p\u003e \u003cp\u003e13.2 Logical foundations and psychological motivations 233\u003c\/p\u003e \u003cp\u003e13.3 A class of models for ordinal data 233\u003c\/p\u003e \u003cp\u003e13.4 Main inferential issues 236\u003c\/p\u003e \u003cp\u003e13.5 Specification of CUB models with subjects’ covariates 238\u003c\/p\u003e \u003cp\u003e13.6 Interpreting the role of covariates 240\u003c\/p\u003e \u003cp\u003e13.7 A more general sampling framework 241\u003c\/p\u003e \u003cp\u003e13.7.1 Objects’ covariates 241\u003c\/p\u003e \u003cp\u003e13.7.2 Contextual covariates 243\u003c\/p\u003e \u003cp\u003e13.8 Applications of CUB models 244\u003c\/p\u003e \u003cp\u003e13.8.1 Models for the ABC annual customer satisfaction survey 245\u003c\/p\u003e \u003cp\u003e13.8.2 Students’ satisfaction with a university orientation service 246\u003c\/p\u003e \u003cp\u003e13.9 Further generalizations 248\u003c\/p\u003e \u003cp\u003e13.10 Concluding remarks 251\u003c\/p\u003e \u003cp\u003eAcknowledgements 251\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003eAppendix 255\u003c\/p\u003e \u003cp\u003eA program in R for CUB models 255\u003c\/p\u003e \u003cp\u003eA.1 Main structure of the program 255\u003c\/p\u003e \u003cp\u003eA.2 Inference on CUB models 255\u003c\/p\u003e \u003cp\u003eA.3 Output of CUB models estimation program 256\u003c\/p\u003e \u003cp\u003eA.4 Visualization of several CUB models in the parameter space 257\u003c\/p\u003e \u003cp\u003eA.5 Inference on CUB models in a multi-object framework 257\u003c\/p\u003e \u003cp\u003eA.6 Advanced software support for CUB models 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 The Rasch Model 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFrancesca De Battisti, Giovanna Nicolini and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 An overview of the Rasch model 259\u003c\/p\u003e \u003cp\u003e14.1.1 The origins and the properties of the model 259\u003c\/p\u003e \u003cp\u003e14.1.2 Rasch model for hierarchical and longitudinal data 263\u003c\/p\u003e \u003cp\u003e14.1.3 Rasch model applications in customer satisfaction surveys 265\u003c\/p\u003e \u003cp\u003e14.2 The Rasch model in practice 267\u003c\/p\u003e \u003cp\u003e14.2.1 Single model 267\u003c\/p\u003e \u003cp\u003e14.2.2 Overall model 268\u003c\/p\u003e \u003cp\u003e14.2.3 Dimension model 272\u003c\/p\u003e \u003cp\u003e14.3 Rasch model software 277\u003c\/p\u003e \u003cp\u003e14.4 Summary 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Tree-based Methods and Decision Trees 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiuliano Galimberti and Gabriele Soffritti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 An overview of tree-based methods and decision trees 283\u003c\/p\u003e \u003cp\u003e15.1.1 The origins of tree-based methods 283\u003c\/p\u003e \u003cp\u003e15.1.2 Tree graphs, tree-based methods and decision trees 284\u003c\/p\u003e \u003cp\u003e15.1.3 CART 287\u003c\/p\u003e \u003cp\u003e15.1.4 CHAID 293\u003c\/p\u003e \u003cp\u003e15.1.5 PARTY 295\u003c\/p\u003e \u003cp\u003e15.1.6 A comparison of CART, CHAID and PARTY 297\u003c\/p\u003e \u003cp\u003e15.1.7 Missing values 297\u003c\/p\u003e \u003cp\u003e15.1.8 Tree-based methods for applications in customer satisfaction surveys 298\u003c\/p\u003e \u003cp\u003e15.2 Tree-based methods and decision trees in practice 300\u003c\/p\u003e \u003cp\u003e15.2.1 ABC ACSS data analysis with tree-based methods 300\u003c\/p\u003e \u003cp\u003e15.2.2 Packages and software implementing tree-based methods 303\u003c\/p\u003e \u003cp\u003e15.3 Further developments 304\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 PLS Models 309\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiuseppe Boari and Gabriele Cantaluppi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 309\u003c\/p\u003e \u003cp\u003e16.2 The general formulation of a structural equation model 310\u003c\/p\u003e \u003cp\u003e16.2.1 The inner model 310\u003c\/p\u003e \u003cp\u003e16.2.2 The outer model 312\u003c\/p\u003e \u003cp\u003e16.3 The PLS algorithm 313\u003c\/p\u003e \u003cp\u003e16.4 Statistical interpretation of PLS 319\u003c\/p\u003e \u003cp\u003e16.5 Geometrical interpretation of PLS 320\u003c\/p\u003e \u003cp\u003e16.6 Comparison of the properties of PLS and LISREL procedures 321\u003c\/p\u003e \u003cp\u003e16.7 Available software for PLS estimation 323\u003c\/p\u003e \u003cp\u003e16.8 Application to real data: Customer satisfaction analysis 323\u003c\/p\u003e \u003cp\u003eReferences 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Nonlinear Principal Component Analysis 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePier Alda Ferrari and Alessandro Barbiero\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 333\u003c\/p\u003e \u003cp\u003e17.2 Homogeneity analysis and nonlinear principal component analysis 334\u003c\/p\u003e \u003cp\u003e17.2.1 Homogeneity analysis 334\u003c\/p\u003e \u003cp\u003e17.2.2 Nonlinear principal component analysis 336\u003c\/p\u003e \u003cp\u003e17.3 Analysis of customer satisfaction 338\u003c\/p\u003e \u003cp\u003e17.3.1 The setting up of indicator 338\u003c\/p\u003e \u003cp\u003e17.3.2 Additional analysis 340\u003c\/p\u003e \u003cp\u003e17.4 Dealing with missing data 340\u003c\/p\u003e \u003cp\u003e17.5 Nonlinear principal component analysis versus two competitors 343\u003c\/p\u003e \u003cp\u003e17.6 Application to the ABC ACSS data 344\u003c\/p\u003e \u003cp\u003e17.6.1 Data preparation 344\u003c\/p\u003e \u003cp\u003e17.6.2 The homals package 345\u003c\/p\u003e \u003cp\u003e17.6.3 Analysis on the ‘complete subset’ 346\u003c\/p\u003e \u003cp\u003e17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350\u003c\/p\u003e \u003cp\u003e17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352\u003c\/p\u003e \u003cp\u003e17.7 Summary 355\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Multidimensional Scaling 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNadia Solaro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 An overview of multidimensional scaling techniques 357\u003c\/p\u003e \u003cp\u003e18.1.1 The origins of MDS models 358\u003c\/p\u003e \u003cp\u003e18.1.2 MDS input data 359\u003c\/p\u003e \u003cp\u003e18.1.3 MDS models 362\u003c\/p\u003e \u003cp\u003e18.1.4 Assessing the goodness of MDS solutions 369\u003c\/p\u003e \u003cp\u003e18.1.5 Comparing two MDS solutions: Procrustes analysis 371\u003c\/p\u003e \u003cp\u003e18.1.6 Robustness issues in the MDS framework 371\u003c\/p\u003e \u003cp\u003e18.1.7 Handling missing values in MDS framework 373\u003c\/p\u003e \u003cp\u003e18.1.8 MDS applications in customer satisfaction surveys 373\u003c\/p\u003e \u003cp\u003e18.2 Multidimensional scaling in practice 374\u003c\/p\u003e \u003cp\u003e18.2.1 Data sets analysed 375\u003c\/p\u003e \u003cp\u003e18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375\u003c\/p\u003e \u003cp\u003e18.2.3 Weighting objects or items 381\u003c\/p\u003e \u003cp\u003e18.2.4 Robustness analysis with the forward search 382\u003c\/p\u003e \u003cp\u003e18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383\u003c\/p\u003e \u003cp\u003e18.2.6 Package and software for MDS methods 384\u003c\/p\u003e \u003cp\u003e18.3 Multidimensional scaling in a future perspective 386\u003c\/p\u003e \u003cp\u003e18.4 Summary 386\u003c\/p\u003e \u003cp\u003eReferences 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Multilevel Models for Ordinal Data 391\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLeonardo Grilli and Carla Rampichini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Ordinal variables 391\u003c\/p\u003e \u003cp\u003e19.2 Standard models for ordinal data 393\u003c\/p\u003e \u003cp\u003e19.2.1 Cumulative models 394\u003c\/p\u003e \u003cp\u003e19.2.2 Other models 395\u003c\/p\u003e \u003cp\u003e19.3 Multilevel models for ordinal data 395\u003c\/p\u003e \u003cp\u003e19.3.1 Representation as an underlying linear model with thresholds 396\u003c\/p\u003e \u003cp\u003e19.3.2 Marginal versus conditional effects 397\u003c\/p\u003e \u003cp\u003e19.3.3 Summarizing the cluster-level unobserved heterogeneity 397\u003c\/p\u003e \u003cp\u003e19.3.4 Consequences of adding a covariate 398\u003c\/p\u003e \u003cp\u003e19.3.5 Predicted probabilities 399\u003c\/p\u003e \u003cp\u003e19.3.6 Cluster-level covariates and contextual effects 399\u003c\/p\u003e \u003cp\u003e19.3.7 Estimation of model parameters 400\u003c\/p\u003e \u003cp\u003e19.3.8 Inference on model parameters 401\u003c\/p\u003e \u003cp\u003e19.3.9 Prediction of random effects 402\u003c\/p\u003e \u003cp\u003e19.3.10 Software 403\u003c\/p\u003e \u003cp\u003e19.4 Multilevel models for ordinal data in practice: An application to student ratings 404\u003c\/p\u003e \u003cp\u003eReferences 408\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Quality Standards and Control Charts Applied to Customer Surveys 413\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett, Laura Deldossi and Diego Zappa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Quality standards and customer satisfaction 413\u003c\/p\u003e \u003cp\u003e20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414\u003c\/p\u003e \u003cp\u003e20.3 Control Charts and ISO 7870 417\u003c\/p\u003e \u003cp\u003e20.4 Control charts and customer surveys: Standard assumptions 420\u003c\/p\u003e \u003cp\u003e20.4.1 Introduction 420\u003c\/p\u003e \u003cp\u003e20.4.2 Standard control charts 420\u003c\/p\u003e \u003cp\u003e20.5 Control charts and customer surveys: Non-standard methods 426\u003c\/p\u003e \u003cp\u003e20.5.1 Weights on counts: Another application of the c chart 426\u003c\/p\u003e \u003cp\u003e20.5.2 The χ\u003csup\u003e2\u003c\/sup\u003e chart 427\u003c\/p\u003e \u003cp\u003e20.5.3 Sequential probability ratio tests 428\u003c\/p\u003e \u003cp\u003e20.5.4 Control chart over items: A non-standard application of SPC methods 429\u003c\/p\u003e \u003cp\u003e20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432\u003c\/p\u003e \u003cp\u003e20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433\u003c\/p\u003e \u003cp\u003e20.6 The \u003ci\u003eM\u003c\/i\u003e-test for assessing sample representation 433\u003c\/p\u003e \u003cp\u003e20.7 Summary 435\u003c\/p\u003e \u003cp\u003eReferences 436\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Fuzzy Methods and Satisfaction Indices 439\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSergio Zani, Maria Adele Milioli and Isabella Morlini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 439\u003c\/p\u003e \u003cp\u003e21.2 Basic definitions and operations 440\u003c\/p\u003e \u003cp\u003e21.3 Fuzzy numbers 441\u003c\/p\u003e \u003cp\u003e21.4 A criterion for fuzzy transformation of variables 443\u003c\/p\u003e \u003cp\u003e21.5 Aggregation and weighting of variables 445\u003c\/p\u003e \u003cp\u003e21.6 Application to the ABC customer satisfaction survey data 446\u003c\/p\u003e \u003cp\u003e21.6.1 The input matrices 446\u003c\/p\u003e \u003cp\u003e21.6.2 Main results 448\u003c\/p\u003e \u003cp\u003e21.7 Summary 453\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix an Introduction to R 457\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eStefano Maria Iacus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.1 Introduction 457\u003c\/p\u003e \u003cp\u003eA.2 How to obtain R 457\u003c\/p\u003e \u003cp\u003eA.3 Type rather than ‘point and click’ 458\u003c\/p\u003e \u003cp\u003eA.3.1 The workspace 458\u003c\/p\u003e \u003cp\u003eA.3.2 Graphics 458\u003c\/p\u003e \u003cp\u003eA.3.3 Getting help 459\u003c\/p\u003e \u003cp\u003eA.3.4 Installing packages 459\u003c\/p\u003e \u003cp\u003eA.4 Objects 460\u003c\/p\u003e \u003cp\u003eA.4.1 Assignments 460\u003c\/p\u003e \u003cp\u003eA.4.2 Basic object types 462\u003c\/p\u003e \u003cp\u003eA.4.3 Accessing objects and subsetting 466\u003c\/p\u003e \u003cp\u003eA.4.4 Coercion between data types 469\u003c\/p\u003e \u003cp\u003eA.5 S4 objects 470\u003c\/p\u003e \u003cp\u003eA.6 Functions 472\u003c\/p\u003e \u003cp\u003eA.7 Vectorization 473\u003c\/p\u003e \u003cp\u003eA.8 Importing data from different sources 475\u003c\/p\u003e \u003cp\u003eA.9 Interacting with databases 476\u003c\/p\u003e \u003cp\u003eA.10 Simple graphics manipulation 477\u003c\/p\u003e \u003cp\u003eA.11 Basic analysis of the ABC data 481\u003c\/p\u003e \u003cp\u003eA.12 About this document 496\u003c\/p\u003e \u003cp\u003eA.13 Bibliographical notes 496\u003c\/p\u003e \u003cp\u003eReferences 496\u003c\/p\u003e \u003cp\u003eIndex 499\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eEdited by\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRON S. KENETT\u003c\/b\u003e\u003ci\u003e, KPA Ltd., Raanana, Israel, University of Turin, Italy, and NYU-Poly, Center for Risk Engineering, New York, USA\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSILVIA SALINI\u003c\/b\u003e\u003ci\u003e, Department of Economics, Business and Statistics, University of Milan, Italy\u003c\/i\u003e    \u003c\/p\u003e\u003cp\u003e\u003cb\u003eModern Analysis of Customer Surveys: with applications using R\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eCustomer 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features:\u003c\/b\u003e \u003cb\u003e\u003cul\u003e\u003cli\u003e Provides an integrated case studies-based approach to analysing customer survey data.\u003c\/li\u003e\u003c\/ul\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003ePresents a general introduction to customer surveys, within an organization’s business cycle.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eContains classical techniques with modern and non standard tools.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eFocuses on probabilistic techniques from the area of statistics\/data analysis and covers all major recent developments.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eAccompanied by a supporting website containing datasets and R scripts.\u003c\/li\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eCustomer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields. \u003c\/p\u003e\u003cp\u003ewww.wiley.com\/go\/modern_analysis \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSTATISTICS IN PRACTICE\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eA series of practical books outlining the use of statistical techniques in a wide range of applications areas: \u003cb\u003e\u003cul\u003e\u003cli\u003eHUMAN AND BIOLOGICAL SCIENCES\u003c\/li\u003e\u003c\/ul\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eEARTH AND ENVIRONMENTAL SCIENCES\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eINDUSTRY, COMMERCE AND FINANCE\u003c\/li\u003e\u003c\/b\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989639446757,"sku":"NP9780470971284","price":136.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470971284.jpg?v=1761784915","url":"https:\/\/k12savings.com\/products\/modern-analysis-of-customer-surveys-isbn-9780470971284","provider":"K12savings","version":"1.0","type":"link"}