Featuring in-depth coverage of categorical and nonparametric
statistics, this book provides a conceptual framework for choosing
the most appropriate type of test in various research scenarios.
Class tested at the University of Nevada, the book's clear
explanations of the underlying assumptions, computer simulations,
and Exploring the Concept boxes help reduce reader anxiety.
Problems inspired by actual studies provide meaningful
illustrations of the techniques. The underlying assumptions of each
test and the factors that impact validity and statistical power are
reviewed so readers can explain their assumptions and how tests
work in future publications. Numerous examples from psychology,
education, and other social sciences demonstrate varied
applications of the material. Basic statistics and probability are
reviewed for those who need a refresher.Mathematical derivations
are placed in optional appendices for those interested in this
detailed coverage. Highlights include: Unique coverage of
categorical and nonparametric statistics better prepares readers to
select the best technique for their particular research project but
some chapters can be omitted entirely if preferred.Step by step
examples of each test help readers see how the material is applied
in a variety of disciplines. Although the book can be used with any
program, examples of how to use the tests in SPSS & EXCEL
foster conceptual understanding. Exploring the concept boxes
integrated throughout prompt students to review key material and
draw links between the concepts to deepen understanding. Problems
in each chapter help readers test their understanding of the
material. Emphasizes selecting tests that maximize power to help
readers avoid marginally significant results. Website featuring
datasets for the book's examples and problems, and for the
instructor Power Points, author's course syllabus, and answers to
the even numbered problems. Chapters 1-3 cover basic concepts in
probability, especially the binomial formula followed by two
chapters that address the analysis of contingency tables. Chapters
6-8 address nonparametric tests involving at least one ordinal
variable, including testing for nonparametric interaction effects,
a topic omitted from other texts. The book then turns to situations
that involve one metric variable.Chapter 9 reviews concepts that
are foundational to CDA, including linear regression and
generalized linear models. Chapters 10-11 cover logistic, ordinal,
and Poisson regression. Chapters 12 and 13 review loglinear models
and the General Estimating Equations (GEE) methodology for
measuring outcomes from multiple time points. For a deeper
understanding of how various CDA techniques work, chapter 14 covers
estimation methods, such as Newton-Raphson and Fisher scoring. The
book concludes with a summary of factors that need to be considered
when choosing the best statistical technique. Intended for
individual or combined graduate or advanced undergraduate courses
in categorical and nonparametric data analysis, cross-classified
data analysis, advanced statistics and/or quantitative techniques
taught in psychology, education, human development, sociology,
political science, and other social and life sciences, the book
also appeals to researchers in these disciplines. The nonparametric
chapters can be deleted if preferred. Prerequisites include
knowledge of t-tests and ANOVA.
General
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