Presents a wide variety of linguistic examples to demonstrate the use of statistics in summarizing data appropriately. The range of techniques introduced will help readers to evaluate and use literature employing statistical analysis, and to apply statistics in their own research.

Statistics in Language Research gives a non-technical but more or less complete treatment of Analysis of Variance (ANOVA) for language researchers. ANOVA is the most frequently used technique when handling the outcomes of research designs with more than two treatments or groups. This technique is used in all parts of linguistics which deal with observations obtained in survey studies and in (quasi-)experimental research, like applied linguistics, psycholinguistics, sociolinguistics, language and speech pathology and phonetics. Most statistical textbooks in the social sciences take examples typical of their own field and, in addition, omit subjects which are particularly relevant for language researchers, like power analysis, quasi F, F1, F2 and minF'. This book offers a thorough introduction to the basic principles of analysis of variance, based on examples taken from language research, and goes beyond the conventional topics treated in introductory textbooks, as it covers topics like 'violations of assumptions', 'missing data', 'problems in repeated measures designs', 'alternatives to analysis of variance' (such as randomization tests and multilevel analysis). Each chapter consists of four sections: treatment of the subject under discussion, a summary of relevant terms and concepts, a section devoted to reporting statistics, and finally an exercise section. After the first introductory chapter, in which fundamental concepts like 'variables', 'cases' and SPSS data formats are presented, the book continues with two 'refreshment' chapters, in which the principles of statistical testing are revised, focusing on the well-known t test. These chapters also deal with the essential, but often neglected concepts of 'statistical power' and 'sample size'. In every chapter examples of SPSS input and output are given.

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.

This valuable book shows second language researchers how to use the statistical program SPSS to conduct statistical tests frequently done in SLA research. Using data sets from real SLA studies, A Guide to Doing Statistics in Second Language Research Using SPSS shows newcomers to both statistics and SPSS how to generate descriptive statistics, how to choose a statistical test, and how to conduct and interpret a variety of basic statistical tests. It covers the statistical tests that are most commonly used in second language research, including chi-square, t-tests, correlation, multiple regression, ANOVA and non-parametric analogs to these tests. The text is abundantly illustrated with graphs and tables depicting actual data sets, and exercises throughout the book help readers understand concepts (such as the difference between independent and dependent variables) and work out statistical analyses. Answers to all exercises are provided on the book’s companion website, along with sample data sets and other supplementary material.

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

The linguistic community tend to regard statistical methods, or more generally quantitative techniques, with a certain amount of fear and suspicion. There is a feeling that statistics falls in the province of science and mathematics and such methods may destroy the magic of the literary text. This book seeks to make quantitative methods and statistical techniques less forbidding and show how they can contribute to linguistic analysis and research. It present some mathematical and statistical properties of natural languages and introduces some of the quantitative methods which are of the most value in working empirically with texts and corpora. The various issues are illustrated with helpful examples from the most basic descriptive techniques to decision-taking techniques and to more sophisticated multivariate statistical language models.

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.