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.
A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
Since more and more attention is being focused on customer value management, it's important to have a resource that synthesizes many bodies of research about how to obtain and interpret customer satisfaction data. It also provides the rationale, identifies opportunities, and suggests specific programs to improve the measurement of customer satisfaction in your organization.!--nl--Serving as a single reference for customer satisfaction measurement technology, this book describes and teaches the five critical skills that should be part of each of your projects. *Sampling/customer-participant selection Questionnaire design *Interviewing/survey administration *Data analysis *Quality function deployment-building action plans This book is an ideal follow-up and companion to the book by Bob E. Hayes, Measuring Customer Satisfaction. Contents: The Philosophy of Customer Satisfaction, Gaining Access to Customers, Identifying Key Measurement Issues, Designing the Questionnaire, Collecting Satisfaction Data, The Data Cube-A New Way to Look at CSM Data Analysis, Basic Tools of CSM Analysis, Reporting Basics-A Graphical Approach, Monitoring Changes in Importance, How to Achieve "Buy-In" of Results Globalizing Satisfaction Measurement
Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling ToolsIncorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make o
“...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught." –CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical power SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures Data sets and odd-numbered solutions provided in an appendix, and tables of critical values Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book.
The ability to read published research critically is essential and is different from the skills involved in undertaking research using statistical analysis. This New Edition of Thomas R Black's best-selling text explains in clear and straightforward terms how students can evaluate research, with particular emphasis on research involving some aspect of measurement. The coverage of fundamental concepts is comprehensive and supports topics including research design, data collection and data analysis by addressing the following major issues: Are the questions and hypotheses advanced appropriate and testable? Is the research design sufficient for the hypothesis? Is the data gathered valid, reliable and objective? Are the statistical techniques used to analyze the data appropriate and do they support the conclusions reached? Each of the chapters from the New Edition has been thoroughly updated, with particular emphasis on improving and increasing the range of activities for students. As well, coverage has been broadened to include: a wider range of research designs; a section on research ethics; item analysis; the definition of standard deviation with a guide for calculation; the concept of `power' in statistical inference; calculating correlations; and a description of the difference between parametric and non-parametric tests in terms of research questions. Evaluating Social Science Research An Introduction 2nd Edition will be key reading for undergraduate and postgrduate students in research methodology and evaluation across the social sciences.
Computers have taken a permanent place in almost every human endeavor in the last 20 years. This infiltration requires a learning process on the part of the people utilizing them and realizing where and how computers can be best used beyond the basic and obvious applications. Statistics is an example of their application in many diverse fields to reach conclusions and make projections never before possible. Beyond this, applied statistics is rapidly becoming not only an instrument, but an integral part of the advance of knowledge. There are many fields such as medicine, biology, weather prediction, military planning, and numerous others where the statistical studies are essential before the next step can be taken. This new book presents the latest research in the field from around the globe.
Citizen Satisfaction investigates the topic of satisfaction with government services from a variety of perspectives, using case studies and empirical results from satisfaction studies at the federal level.
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.
