Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data

This book deals with problems related to the evaluation of customer satisfaction in very different contexts and ways. Often satisfaction about a product or service is investigated through suitable surveys which try to capture the satisfaction about several partial aspects which characterize the perc...

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Hlavný autor: Arboretti, Rosa (Autor)
Médium: Elektronický zdroj E-kniha
Jazyk:English
Vydavateľské údaje: Cham : Springer International Publishing, 2018.
Vydanie:1st ed. 2018.
Edícia:SpringerBriefs in Statistics,
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ISBN:9783319917405
ISSN:2191-544X
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245 1 0 |a Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data  |h [electronic resource] /  |c by Rosa Arboretti, Arne Bathke, Stefano Bonnini, Paolo Bordignon, Eleonora Carrozzo, Livio Corain, Luigi Salmaso. 
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505 0 |a Chapter 1. The CUB models -- Chapter 2. Customer satisfaction heterogeneity -- Chapter 3. Ranking multivariate populations -- Chapter 4. Composite indicators and satisfaction profiles -- Chapter 5. Analyzing Survey Data Using Multivariate Rank-Based Inference. 
516 |a text file PDF 
520 |a This book deals with problems related to the evaluation of customer satisfaction in very different contexts and ways. Often satisfaction about a product or service is investigated through suitable surveys which try to capture the satisfaction about several partial aspects which characterize the perceived quality of that product or service. This book presents a series of statistical techniques adopted to analyze data from real situations where customer satisfaction surveys were performed. The aim is to give a simple guide of the variety of analysis that can be performed when analyzing data from sample surveys: starting from latent variable models to heterogeneity in satisfaction and also introducing some testing methods for comparing different customers. The book also discusses the construction of composite indicators including different benchmarks of satisfaction. Finally, some rank-based procedures for analyzing survey data are also shown. 
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