| 书目名称 | Nonparametric Statistics for Applied Research | | 编辑 | Jared A. Linebach,Brian P. Tesch,Lea M. Kovacsiss | | 视频video | | | 概述 | Showcases "real-life scenarios" from psychology and medicine.Readers learn to analyze data and explain often opaque statistical concepts.Each scenario contains real data sets, questions, and self-lear | | 图书封面 |  | | 描述 | .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. .This is a textbook on non-parametric statistics for applied research. The authors propose to use a realistic yet mostly fictional situation and series of dialogues to illustrate in detail the statistical processes required to complete data analysis. This book draws on a readers existing elementary knowledge of statistical analyses to broaden his/her | | 出版日期 | Textbook 2014 | | 关键词 | Applied research; Non-parametric statistics; Nonparametric; Nonparametric statistics; Ordinal Data; Robus | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-9041-8 | | isbn_softcover | 978-1-4939-5394-3 | | isbn_ebook | 978-1-4614-9041-8 | | copyright | Springer Science+Business Media New York 2014 |
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发表于 2025-3-21 18:37:20
