Nonparametric statistics for applied research
By: Jared A. Linebach, Brian P. Tesch, Lea M. Kovacsiss
Contributors: Tesch, Brian P., author; Kovacsiss, Lea M., author
Publisher: Springer
Published: 2014
Place of publication: New York
Language: eng
Number of pages: 408
ISBN-13: 9781461490401
Copyright date: 2014
Subjects: Nonparametric statistics; Psychology; Research; Methodology
Description: Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement, non-parametric methods result in "ordinal" data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Non-parametric methods have many popular applications, and are widely used in research in the fields of the behavioral sciences and biomedicine.-
Notes: Includes bibliographical references (pages 403-404) and index.
Table of contents: title: Meeting the Team; title: Questions, Assumptions, and Decisions; title: Understanding Similarity; title: The Bourgeoisie, the Proletariat, and an Unwelcomed Press Conference; title: Agreeing to Disagree; title: Guesstimating the Fluffy-Maker; title: X Marks the Spot Revisited; title: Let My People Go!; title: Here's Your Sign and the Neighborhood Bowling League; title: Geometry on Steroids; title: Crunch Time; title: Presentation to the Governor.