Statistics for Linguistics with R: A Practical Introduction

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Walter de Gruyter, 2009 - Language Arts & Disciplines - 335 pages
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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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Contents

Chapter
3
introduction
48
Functions and arguments
62
Factors
82
Descriptive statistics
96
Bivariate statistics
127
Chapter 4
148
Dispersions
184
Coefficients of correlation and linear regression
230
Chapter 5
238
Multiple regression analysis
252
ANOVA analysis of variance
274
Binary logistic regression
291
Hierarchical agglomerative cluster analysis
306
Chapter 6
320
Copyright

Means
194

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About the author (2009)

Stefan Th. Gries, University of California, Santa Barbara, USA.

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