Nonparametric Measures of Association


Book Description

Aimed at helping the researcher select the most appropriate measure of association for two or more variables, the author clearly describes such techniques as Spearman's rho, Kendall's tau, Goodman and Kruskals' gamma and Somer's d and carefully explains the calculation procedures as well as the substantive meaning of each measure.







Data Analysis in Business Research


Book Description

While there are books focusing on parametric tests, the domain of nonparametric tests is mostly unexplored. Data Analysis in Business Research: A Step by Step Nonparametric Approach brings under one umbrella all the major nonparametric statistical tools that can be used by undergraduate and postgraduate students of all disciplines, especially students of Research Methods in Social Sciences and Management Studies, in their dissertation work. Students face difficulty in analyzing data collected from small samples; they end up reporting mere percentage analysis which results in the loss of information collected. Hence there is a need to create awareness among students and researchers about the application of major nonparametric tools that can be applied confidently without worrying about sample size, scale of measurement, normality assumptions or other parameters of that nature. The lucid presentation of the step-by-step procedures, explaining in simple English how to perform each of the major nonparametric tests, is a major attraction of the book. The book, which also has a comprehensive question bank, assumes minimal or little knowledge of statistics on the part of the reader. This book will also be informative for Marketing Research professionals and organisations, consultancies and organisations of economic research.




Correlation


Book Description

Correlations, in general, and the Pearson product-moment correlation in particular, can be used for many research purposes, ranging from describing a relationship between two variables as a descriptive statistic to examining a relationship between two variables in a population as an inferential statistic, or to gauge the strength of an effect, or to conduct a meta-analytic study. How can correlation be more effectively used so that one doesn't misinterpret the data? This book reveals how to do this by examining Pearson r from its conceptual meaning, to assumptions, special cases of the Pearson r, the biserial coefficient and tetrachoric coefficient estimates of the Pearson r, its uses in research (including effect size, power analysis, meta-analysis, utility analysis, reliability estimates and validation), factors that affect the Pearson r, and finally to additional nonparametric correlation indexes. After reading this book, the reader will be able to compare and distinguish the concepts of similarity and relationship, identify the distinction between correlation and causation, and to interpret correlations correctly.




Applied Nonparametric Statistics


Book Description

Introduction and review; Procedures that utilize data from a single sample; Procedures that utilize data from two independent samples; Procedures that utilize data from two related samples; Chi-square tests of independence and homogeneity; Procedures that utilize data from three or more independent samples; Procedures that utilize data from three or more related; Coodness-of-fit tests; Rank correlation and other measures of association; Simple linear regression analysis.




Nonparametric Statistics


Book Description

“...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.




Measures of Association


Book Description

Clearly reviews the properties of important contemporary measures of association and correlation. Liebetrau devotes full chapters to measures for nominal, ordinal, and continuous (interval) data, paying special attention to the sampling distributions needed to determine levels of significance and confidence intervals. Valuable discussions also focus on the relationships between various measures, the sampling properties of their estimators and the comparative advantages and disadvantages of different approaches.




Nonparametric Statistical Inference


Book Description

Praise for previous editions: "... a classic with a long history." – Statistical Papers "The fact that the first edition of this book was published in 1971 ... [is] testimony to the book’s success over a long period." – ISI Short Book Reviews "... one of the best books available for a theory course on nonparametric statistics. ... very well written and organized ... recommended for teachers and graduate students." – Biometrics "... There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition." – Technometrics "... Useful to students and research workers ... a good textbook for a beginning graduate-level course in nonparametric statistics." – Journal of the American Statistical Association Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new references Nonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.