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Description:
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Now available in Paperback!
This book is an introduction to statistics for linguists using the open source
software R. It is aimed at students and instructors/professors with little or no
statistical background and is written in a non-technical and reader-
friendly/accessible style.
It first introduces in detail the overall logic underlying quantitative studies:
exploration, hypothesis formulation and operationalization, and the notion and
meaning of significance tests. It then introduces some basics of the software
R relevant to statistical data analysis. A chapter on descriptive statistics
explains how summary statistics for frequencies, averages, and correlations
are generated with R and how they are graphically represented best. A
chapter on analytical statistics explains how statistical tests are performed in
R on the basis of many different linguistic case studies: For nearly every
single example, it is explained what the structure of the test looks like, how
hypotheses are formulated, explored, and tested for statistical significance,
how the results are graphically represented, and how one would summarize
them in a paper/article. A chapter on selected multifactorial methods
introduces how more complex research designs can be studied: methods for
the study of multifactorial frequency data, correlations, tests for means, and
binary response data are discussed and exemplified step-by-step. Also, the
exploratory approach of hierarchical cluster analysis is illustrated in detail.
The book comes with many exercises, boxes with short think breaks and
warnings, recommendations for further study, and answer keys as well as a
statistics for linguists newsgroup on the companion website.
The volume is aimed at beginners on every level of linguistic education:
undergraduate students, graduate students, and instructors/professors and
can be used in any research methods and statistics class for linguists. It
presupposes no quantitative/statistical knowledge whatsoever and, unlike
most competing books, begins at step 1 for every method and explains
everything explicitly.
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