Nonparametric statistics for applied research / Jared A Linebach, Brian P. Tesch and Lea M. Kovacsiss.
Material type:
TextPublication details: New York : Springer, 2014.Description: xii, 408 p. ; illustrations (some color)ISBN: - 9781461490401
- 23 L754 000SA.12
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.12 L754 (Browse shelf(Opens below)) | Available | 135390 |
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| 000SA.12 K66 Nonparametric statistical methods using R / | 000SA.12 K84 Nonparametric methods in statistics with SAS applications / | 000SA.12 L523 Nonparametrics | 000SA.12 L754 Nonparametric statistics for applied research / | 000SA.12 P314 Nonparametric statistics on manifolds and their applications to object data analysis / | 000SA.12 P798 Statistical tests of nonparametric hypotheses : | 000SA.12 Si571 Nonparametric statistics for the behavioural sciences |
Includes bibliographical references and index.
1. Introduction--
2. Meeting the Team --
3. Questions, Assumptions, and Decisions --
4. Understanding Similarity --
5. The Bourgeoisie, the Proletariat, and an Unwelcomed Press Conference --
6. Agreeing to Disagree --
7. Guesstimating the Fluffy-Maker --
8. X Marks the Spot Revisited --
9. Let My People Go! --
10. Here's Your Sign and the Neighborhood Bowling League --
11. Geometry on Steroids --
12. Crunch Time --
13. Presentation to the Governor--
Appendices--
Answers to "Check Your Understanding" Questions--
Glossary--
Bibliography--
Index--
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.
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