Ordinal methods for behavioral data analysis - Info and Reading Options
By Cliff, Norman
"Ordinal methods for behavioral data analysis" was published by Erlbaum in 1996 - Mahwah, N.J, it has 197 pages and the language of the book is English.
“Ordinal methods for behavioral data analysis” Metadata:
- Title: ➤ Ordinal methods for behavioral data analysis
- Author: Cliff, Norman
- Language: English
- Number of Pages: 197
- Publisher: Erlbaum
- Publish Date: 1996
- Publish Location: Mahwah, N.J
“Ordinal methods for behavioral data analysis” Subjects and Themes:
- Subjects: ➤ Statistical methods - Psychology - Regression analysis - Mathematical models - Social sciences - Analysis of variance - Behaviorism (psychology) - Psychological Models - Statistical Models - Psychologie - Modèles mathématiques - Sciences sociales - Méthodes statistiques - Analyse de variance - Analyse de régression - Méthodes de simulation - SOCIAL SCIENCE - Essays - Reference - Mathematische Psychologie - Ordinale gegevens - Variantieanalyse - Regressieanalyse - Méthodologie
Edition Specifications:
- Pagination: xiii, 197 p. ;
Edition Identifiers:
- The Open Library ID: OL984911M - OL3275522W
- Online Computer Library Center (OCLC) ID: 872114625 - 34875773
- Library of Congress Control Number (LCCN): 96022689
- ISBN-10: 0805813330
- All ISBNs: 0805813330
AI-generated Review of “Ordinal methods for behavioral data analysis”:
"Ordinal methods for behavioral data analysis" Description:
The Open Library:
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 than nonparametrically. The author discusses how ordinal statistics can be applied in a much wider set of research situations than has usually been thought, and shows that they can often come closer to answering the researcher's primary questions than traditional ones can.
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