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Nonparametric statistics for applied research [electronic resource] / Jared A. Linebach, Brian P. Tesch, Lea M. Kovacsiss.

Linebach, Jared A., author.; Tesch, Brian P., author.; Kovacsiss, Lea M., author.
2014
QA278.8
Available Online
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Title
Nonparametric statistics for applied research [electronic resource] / Jared A. Linebach, Brian P. Tesch, Lea M. Kovacsiss.
Author
Linebach, Jared A., author.
ISBN
9781461490418 electronic book
1461490413 electronic book
9781461490401
DOI
https://doi.org/10.1007/978-1-4614-9041-8
Published
New York, NY : Springer, 2014.
Language
English
Description
1 online resource (xii, 408 pages) : illustrations (some color)
Item Number
10.1007/978-1-4614-9041-8 doi
Call Number
QA278.8
Dewey Decimal Classification
519.5/4
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed November 18, 2013).
Added Author
Tesch, Brian P., author.
Kovacsiss, Lea M., author.
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