A comprehensive and accessible introduction to statistics in corpus linguistics, covering multiple techniques of quantitative language analysis and data visualisation.

Traditional approaches focused on significance tests have often been difficult for linguistics researchers to visualise. Statistics in Corpus Linguistics Research: A New Approach breaks these significance tests down for researchers in corpus linguistics and linguistic analysis, promoting a visual approach to understanding the performance of tests with real data, and demonstrating how to derive new intervals and tests. Accessibly written, this book discusses the ‘why’ behind the statistical model, allowing readers a greater facility for choosing their own methodologies. Accessibly written for those with little to no mathematical or statistical background, it explains the mathematical fundamentals of simple significance tests by relating them to confidence intervals. With sample datasets and easy-to-read visuals, this book focuses on practical issues, such as how to: • pose research questions in terms of choice and constraint; • employ confidence intervals correctly (including in graph plots); • select optimal significance tests (and what results mean); • measure the size of the effect of one variable on another; • estimate the similarity of distribution patterns; and • evaluate whether the results of two experiments significantly differ. Appropriate for anyone from the student just beginning their career to the seasoned researcher, this book is both a practical overview and valuable resource.

This book in the Edinburgh Textbooks in Empirical Linguistics series is a comprehensive introduction to the statistics currently used in corpus linguistics. Statistical techniques and corpus applications - whether oriented towards linguistics or language engineering - often go hand in glove, and corpus linguists have used an increasingly wide variety of statistics, drawing on techniques developed in a great many fields. This is the first one-volume introduction to the subject.

This textbook examines empirical linguistics from a theoretical linguist’s perspective. It provides both a theoretical discussion of what quantitative corpus linguistics entails and detailed, hands-on, step-by-step instructions to implement the techniques in the field. The statistical methodology and R-based coding from this book teach readers the basic and then more advanced skills to work with large data sets in their linguistics research and studies. Massive data sets are now more than ever the basis for work that ranges from usage-based linguistics to the far reaches of applied linguistics. This book presents much of the methodology in a corpus-based approach. However, the corpus-based methods in this book are also essential components of recent developments in sociolinguistics, historical linguistics, computational linguistics, and psycholinguistics. Material from the book will also be appealing to researchers in digital humanities and the many non-linguistic fields that use textual data analysis and text-based sensorimetrics. Chapters cover topics including corpus processing, frequencing data, and clustering methods. Case studies illustrate each chapter with accompanying data sets, R code, and exercises for use by readers. This book may be used in advanced undergraduate courses, graduate courses, and self-study.

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.

The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve – searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.

Corpora are used widely in linguistics, but not always wisely. This book attempts to frame corpus linguistics systematically as a variant of the observational method. The first part introduces the reader to the general methodological discussions surrounding corpus data as well as the practice of doing corpus linguistics, including issues such as the scientific research cycle, research design, extraction of corpus data and statistical evaluation. The second part consists of a number of case studies from the main areas of corpus linguistics (lexical associations, morphology, grammar, text and metaphor), surveying the range of issues studied in corpus linguistics while at the same time showing how they fit into the methodology outlined in the first part.