Ordinal Methods for Behavioral Data Analysis

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A01=Norman Cliff
Adependent Variable
advanced research statistics
Author_Norman Cliff
Category=JMB
Category=JMBT
Da Ta
Data
distribution
distribution-free statistics
Dominance Analysis
Dominance Variables
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
estimate
Factorial ANOVA Model
group comparison techniques
hypothesis
Independent Groups
monotonie
Monotonie Transformation
Monte Carlo Studies
Null Hypothesis
order-based statistical inference
Ordinal Correlation
Ordinal Methods
ordinal regression
Partial Pearson Correlation
Partial Tau
Pearson Correlation
psychological scale analysis
Rank Correlation
rank correlation methods
Rank Differences
Raw Differences
sampie
Sampie Size
sampling
Sampling Distribution
size
Small Sampies
Tau
transformations
unbiased
Unbiased Estimate
Untied Pairs
Vice Versa

Product details

  • ISBN 9781138977631
  • Weight: 453g
  • Dimensions: 152 x 229mm
  • Publication Date: 13 May 2016
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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This book was written with the belief that ordinal statistical methods--sometimes discussed under the title of "nonparametric statistics"--deserve much more serious attention as research tools than they have traditionally had. There are three classes of reasons for this:
*Many behavioral variables constitute only ordinal scales, not interval measurements that are required for traditional statistics.
*Various research issues that are of primary interest in behavioral research are themselves questions about order: Which group scores higher? Is the order on this variable similar to the order on that?
*Inferences from ordinal statistics are less subject to distributional peculiarities of the data than are those from traditional statistics.

Taking an innovative approach, this book treats ordinal methods in an integrated way rather than as a compendium of unrelated methods, and emphasizes that the ordinal quantities are highly meaningful in their own right, not just as stand-ins for more traditional correlations or analyses of variance. In fact, since the ordinal statistics have desirable descriptive properties of their own, the book treats them parametrically, rather then nonparametrically. The author discusses how ordinal statistics can be applied in a much wider set of research situations than has usually been thought, and that they can often come closer to answering the researcher's primary questions than traditional ones can. And he includes some extensions of ordinal methods in order to accomplish that end.

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