06586nam 2200949Ia 450 991013955210332120220719134932.01-119-96138-61-283-33312-097866133331241-119-96115-71-119-96116-5(CKB)2550000000064929(EBL)822594(SSID)ssj0000555147(PQKBManifestationID)11363456(PQKBTitleCode)TC0000555147(PQKBWorkID)10533515(PQKB)10916845(Au-PeEL)EBL822594(CaPaEBR)ebr10510589(CaONFJC)MIL333312(CaSebORM)9780470971284(MiAaPQ)EBC822594(OCoLC)768230368(EXLCZ)99255000000006492920110721d2011 uy 0engur|n|---|||||txtccrModern analysis of customer surveys[electronic resource] with applications using R /edited by Ron S. Kenett, Silvia Salini1st editionChichester John Wiley & Sons20111 online resource (526 p.)Statistics in PracticeDescription based upon print version of record.0-470-97128-2 Includes bibliographical references and index.Modern Analysis ofCustomer 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; References2 The ABC annual customer satisfaction survey2.1 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 errors3.3.4 Item non-response and non-self-selection error3.4 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 Introduction5.2 Information sources and related problems5.2.1 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 research6.5 Main drawbacks of web survey research6.6 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 considerations7.3.1 RationaleCustomer 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 suStatistics in PracticeConsumer satisfactionResearchStatistical methodsConsumer satisfactionEvaluationConsumersResearchStatistical methodsConsumersResearchData processingSampling (Statistics)EvaluationSurveysStatistical methodsSurveysData processingR (Computer program language)ConsumidorsSatisfaccióEstadístiqueslemacConsumidorsEstadístiqueslemacMostreig (Estadística)lemacEnquesteslemacConsumer satisfactionResearchStatistical methods.Consumer satisfactionEvaluation.ConsumersResearchStatistical methods.ConsumersResearchData processing.Sampling (Statistics)Evaluation.SurveysStatistical methods.SurveysData processing.R (Computer program language).ConsumidorsSatisfaccióEstadístiquesConsumidorsEstadístiquesMostreig (Estadística)Enquestes658.8/3402855282Kenett Ron874200Salini Silvia961344MiAaPQMiAaPQMiAaPQBOOK9910139552103321Modern analysis of customer surveys2179565UNINA