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Author Salini, Silvia
Title Modern Analysis of Customer Surveys : With Applications Using R
Imprint Hoboken : John Wiley & Sons, Incorporated, 2012
©2012
book jacket
Edition 2nd ed
Descript 1 online resource (526 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Statistics in Practice Ser. ; v.116
Statistics in Practice Ser
Note Intro -- Modern Analysis of Customer Surveys -- Contents -- Foreword -- Preface -- Contributors -- PART I BASIC ASPECTS OF CUSTOMER SATISFACTION SURVEY DATA ANALYSIS -- 1 Standards and classical techniques in data analysis of customer satisfaction surveys -- 1.1 Literature on customer satisfaction surveys -- 1.2 Customer satisfaction surveys and the business cycle -- 1.3 Standards used in the analysis of survey data -- 1.4 Measures and models of customer satisfaction -- 1.4.1 The conceptual construct -- 1.4.2 The measurement process -- 1.5 Organization of the book -- 1.6 Summary -- References -- 2 The ABC annual customer satisfaction survey -- 2.1 The ABC company -- 2.2 ABC 2010 ACSS: Demographics of respondents -- 2.3 ABC 2010 ACSS: Overall satisfaction -- 2.4 ABC 2010 ACSS: Analysis of topics -- 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers -- 2.6 Summary -- References -- Appendix -- 3 Census and sample surveys -- 3.1 Introduction -- 3.2 Types of surveys -- 3.2.1 Census and sample surveys -- 3.2.2 Sampling design -- 3.2.3 Managing a survey -- 3.2.4 Frequency of surveys -- 3.3 Non-sampling errors -- 3.3.1 Measurement error -- 3.3.2 Coverage error -- 3.3.3 Unit non-response and non-self-selection errors -- 3.3.4 Item non-response and non-self-selection error -- 3.4 Data collection methods -- 3.5 Methods to correct non-sampling errors -- 3.5.1 Methods to correct unit non-response errors -- 3.5.2 Methods to correct item non-response -- 3.6 Summary -- References -- 4 Measurement scales -- 4.1 Scale construction -- 4.1.1 Nominal scale -- 4.1.2 Ordinal scale -- 4.1.3 Interval scale -- 4.1.4 Ratio scale -- 4.2 Scale transformations -- 4.2.1 Scale transformations referred to single items -- 4.2.2 Scale transformations to obtain scores on a unique interval scale -- Acknowledgements -- References -- 5 Integrated analysis
5.1 Introduction -- 5.2 Information sources and related problems -- 5.2.1 Types of data sources -- 5.2.2 Advantages of using secondary source data -- 5.2.3 Problems with secondary source data -- 5.2.4 Internal sources of secondary information -- 5.3 Root cause analysis -- 5.3.1 General concepts -- 5.3.2 Methods and tools in RCA -- 5.3.3 Root cause analysis and customer satisfaction -- 5.4 Summary -- Acknowledgement -- References -- 6 Web surveys -- 6.1 Introduction -- 6.2 Main types of web surveys -- 6.3 Economic benefits of web survey research -- 6.3.1 Fixed and variable costs -- 6.4 Non-economic benefits of web survey research -- 6.5 Main drawbacks of web survey research -- 6.6 Web surveys for customer and employee satisfaction projects -- 6.7 Summary -- References -- 7 The concept and assessment of customer satisfaction -- 7.1 Introduction -- 7.2 The quality-satisfaction-loyalty chain -- 7.2.1 Rationale -- 7.2.2 Definitions of customer satisfaction -- 7.2.3 From general conceptions to a measurement model of customer satisfaction -- 7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context -- 7.2.5 From customer satisfaction to customer loyalty -- 7.3 Customer satisfaction assessment: Some methodological considerations -- 7.3.1 Rationale -- 7.3.2 Think big: An assessment programme -- 7.3.3 Back to basics: Questionnaire design -- 7.3.4 Impact of questionnaire design on interpretation -- 7.3.5 Additional concerns in the B2B setting -- 7.4 The ABC ACSS questionnaire: An evaluation -- 7.4.1 Rationale -- 7.4.2 Conceptual issues -- 7.4.3 Methodological issues -- 7.4.4 Overall ABC ACSS questionnaire asssessment -- 7.5 Summary -- References -- Appendix -- 8 Missing data and imputation methods -- 8.1 Introduction -- 8.2 Missing-data patterns and missing-data mechanisms -- 8.2.1 Missing-data patterns
8.2.2 Missing-data mechanisms and ignorability -- 8.3 Simple approaches to the missing-data problem -- 8.3.1 Complete-case analysis -- 8.3.2 Available-case analysis -- 8.3.3 Weighting adjustment for unit nonresponse -- 8.4 Single imputation -- 8.5 Multiple imputation -- 8.5.1 Multiple-imputation inference for a scalar estimand -- 8.5.2 Proper multiple imputation -- 8.5.3 Appropriately drawing imputations with monotone missing-data patterns -- 8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns -- 8.5.5 Multiple imputation in practice -- 8.5.6 Software for multiple imputation -- 8.6 Model-based approaches to the analysis of missing data -- 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example -- 8.8 Summary -- Acknowledgements -- References -- 9 Outliers and robustness for ordinal data -- 9.1 An overview of outlier detection methods -- 9.2 An example of masking -- 9.3 Detection of outliers in ordinal variables -- 9.4 Detection of bivariate ordinal outliers -- 9.5 Detection of multivariate outliers in ordinal regression -- 9.5.1 Theory -- 9.5.2 Results from the application -- 9.6 Summary -- References -- PART II MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS -- 10 Statistical inference for causal effects -- 10.1 Introduction to the potential outcome approach to causal inference -- 10.1.1 Causal inference primitives: Units, treatments, and potential outcomes -- 10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption -- 10.1.3 Defining causal estimands -- 10.2 Assignment mechanisms -- 10.2.1 The criticality of the assignment mechanism -- 10.2.2 Unconfounded and strongly ignorable assignment mechanisms -- 10.2.3 Confounded and ignorable assignment mechanisms -- 10.2.4 Randomized and observational studies
10.3 Inference in classical randomized experiments -- 10.3.1 Fisher's approach and extensions -- 10.3.2 Neyman's approach to randomization-based inference -- 10.3.3 Covariates, regression models, and Bayesian model-based inference -- 10.4 Inference in observational studies -- 10.4.1 Inference in regular designs -- 10.4.2 Designing observational studies: The role of the propensity score -- 10.4.3 Estimation methods -- 10.4.4 Inference in irregular designs -- 10.4.5 Sensitivity and bounds -- 10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs -- References -- 11 Bayesian networks applied to customer surveys -- 11.1 Introduction to Bayesian networks -- 11.2 The Bayesian network model in practice -- 11.2.1 Bayesian network analysis of the ABC 2010 ACSS -- 11.2.2 Transport data analysis -- 11.2.3 R packages and other software programs used for studying BNs -- 11.3 Prediction and explanation -- 11.4 Summary -- References -- 12 Log-linear model methods -- 12.1 Introduction -- 12.2 Overview of log-linear models and methods -- 12.2.1 Two-way tables -- 12.2.2 Hierarchical log-linear models -- 12.2.3 Model search and selection -- 12.2.4 Sparseness in contingency tables and its implications -- 12.2.5 Computer programs for log-linear model analysis -- 12.3 Application to ABC survey data -- 12.4 Summary -- References -- 13 CUB models: Statistical methods and empirical evidence -- 13.1 Introduction -- 13.2 Logical foundations and psychological motivations -- 13.3 A class of models for ordinal data -- 13.4 Main inferential issues -- 13.5 Specification of CUB models with subjects' covariates -- 13.6 Interpreting the role of covariates -- 13.7 A more general sampling framework -- 13.7.1 Objects' covariates -- 13.7.2 Contextual covariates -- 13.8 Applications of CUB models
13.8.1 Models for the ABC annual customer satisfaction survey -- 13.8.2 Students' satisfaction with a university orientation service -- 13.9 Further generalizations -- 13.10 Concluding remarks -- Acknowledgements -- References -- Appendix -- A program in R for CUB models -- A.1 Main structure of the program -- A.2 Inference on CUB models -- A.3 Output of CUB models estimation program -- A.4 Visualization of several CUB models in the parameter space -- A.5 Inference on CUB models in a multi-object framework -- A.6 Advanced software support for CUB models -- 14 The Rasch model -- 14.1 An overview of the Rasch model -- 14.1.1 The origins and the properties of the model -- 14.1.2 Rasch model for hierarchical and longitudinal data -- 14.1.3 Rasch model applications in customer satisfaction surveys -- 14.2 The Rasch model in practice -- 14.2.1 Single model -- 14.2.2 Overall model -- 14.2.3 Dimension model -- 14.3 Rasch model software -- 14.4 Summary -- References -- 15 Tree-based methods and decision trees -- 15.1 An overview of tree-based methods and decision trees -- 15.1.1 The origins of tree-based methods -- 15.1.2 Tree graphs, tree-based methods and decision trees -- 15.1.3 CART -- 15.1.4 CHAID -- 15.1.5 PARTY -- 15.1.6 A comparison of CART, CHAID and PARTY -- 15.1.7 Missing values -- 15.1.8 Tree-based methods for applications in customer satisfaction surveys -- 15.2 Tree-based methods and decision trees in practice -- 15.2.1 ABC ACSS data analysis with tree-based methods -- 15.2.2 Packages and software implementing tree-based methods -- 15.3 Further developments -- References -- 16 PLS models -- 16.1 Introduction -- 16.2 The general formulation of a structural equation model -- 16.2.1 The inner model -- 16.2.2 The outer model -- 16.3 The PLS algorithm -- 16.4 Statistical interpretation of PLS -- 16.5 Geometrical interpretation of PLS
16.6 Comparison of the properties of PLS and LISREL procedures
Customer survey studies deals with customers, consumers 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. As demonstrated in this book, integrating such basic analysis with more advanced tools, provides insights on 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 organization's 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
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Salini, Silvia Modern Analysis of Customer Surveys : With Applications Using R Hoboken : John Wiley & Sons, Incorporated,c2012 9780470971284
Subject Consumer satisfaction -- Research -- Statistical methods.;Consumer satisfaction -- Evaluation.;Consumers -- Research -- Statistical methods.;Consumers -- Research -- Data processing.;Sampling (Statistics) -- Evaluation.;Surveys -- Statistical methods.;Surveys -- Data processing
Electronic books
Alt Author Kenett, Ron. S
Kenett, Ron
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