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008 160915s2014 xxu| s |||| 0|eng d
020 _a9781461490418
_9978-1-4614-9041-8
024 7 _a10.1007/978-1-4614-9041-8
_2doi
035 _ato000541156
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aLinebach, Jared A.
_eauthor.
_9446644
245 1 0 _aNonparametric Statistics for Applied Research
_helectronic resource
_cby Jared A. Linebach, Brian P. Tesch, Lea M. Kovacsiss.
260 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXII, 408 p. 23 illus., 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aIntroduction -- Meeting the Team -- Questions, Assumptions, and Decisions.- Understanding Similarity -- The Bourgeoisie, the Proletariat, and an Unwelcomed Press Conference -- Agreeing to Disagree -- Guesstimating the Fluffy-Maker -- X Marks the Spot Revisited -- Let My People Go! -- Here's Your Sign and the Neighborhood Bowling League -- Geometry on Steroids -- Crunch Time.- Presentation. .
520 _aNon-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 research capabilities.  The material within the book is covered in such a way that someone with a very limited knowledge of statistics would be able to read and understand the concepts detailed in the text. The “real world” scenario to be presented involves a multidisciplinary team of behavioral, medical, crime analysis, and policy analysis professionals work together to answer specific empirical questions regarding real-world applied problems.  The reader is introduced to the team and the data set, and through the course of the text follows the team as they progress through the decision making process of narrowing the data and the research questions to answer the applied problem.  In this way, abstract statistical concepts are translated into concrete and specific language. This text uses one data set from which all examples are taken.  This is radically different from other statistics books which provide a varied array of examples and data sets.  Using only one data set facilitates reader-directed teaching and learning by providing multiple research questions which are integrated rather than using disparate examples and completely unrelated research questions and data.
650 0 _aStatistics.
_9124796
650 0 _aMathematical statistics.
_9566264
650 1 4 _aStatistics.
_9124796
650 2 4 _aStatistical Theory and Methods.
_9303276
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
_9303278
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_9265982
700 1 _aTesch, Brian P.
_eauthor.
_9446645
700 1 _aKovacsiss, Lea M.
_eauthor.
_9446646
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-9041-8
912 _aZDB-2-SMA
999 _c399175