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Nonparametric Statistics for Applied Research (Paperback, Softcover reprint of the original 1st ed. 2014)
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Nonparametric Statistics for Applied Research (Paperback, Softcover reprint of the original 1st ed. 2014)
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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 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.
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