Publisher: Cambridge University Press

ISBN: 1139470736

Pages:

Year: 2008-03-06

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Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.

Publisher:

ISBN: 0511386303

Pages: 369

Year: 2008

View: 226

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A straightforward introduction to the statistical analysis of language data, designed for students with a non-mathematical background.

Publisher:

ISBN: 0511574258

Pages: 353

Year: 2008

View: 697

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Publisher: Walter de Gruyter

ISBN: 3110307472

Pages: 372

Year: 2013-03-22

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Read: 558

This book is the revised and extended second edition of Statistics for Linguistics with R. The volume 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. Just like the first edition, it is aimed at students, faculty, and researchers with little or no statistical background in statistics or the open source programming language R. It avoids mathematical jargon and discusses the logic and structure of quantitative studies and introduces descriptive statistics as well as a range of monofactorial statistical tests for frequencies, distributions, means, dispersions, and correlations. The comprehensive revision includes new small sections on programming topics that facilitate statistical analysis, the addition of a variety of statistical functions readers can apply to their own data, a revision of overview sections on statistical tests and regression modeling, a complete rewrite of the chapter on multifactorial approaches, which now contains sections on linear regression, binary and ordinal logistic regression, multinomial and Poisson regression, and repeated-measures ANOVA, and a new visual tool to identify the right statistical test for a given problem and data set. The amount of code available from the companion website has doubled in size, providing much supplementary material on statistical tests and advanced plotting.

Publisher: John Wiley & Sons

ISBN: 1444360434

Pages: 296

Year: 2011-09-23

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Quantitative Methods in Linguistics offers a practical introduction to statistics and quantitative analysis with data sets drawn from the field and coverage of phonetics, psycholinguistics, sociolinguistics, historical linguistics, and syntax, as well as probability distribution and quantitative methods. Provides balanced treatment of the practical aspects of handling quantitative linguistic data Includes sample datasets contributed by researchers working in a variety of sub-disciplines of linguistics Uses R, the statistical software package most commonly used by linguists, to discover patterns in quantitative data and to test linguistic hypotheses Includes student-friendly end-of-chapter assignments and is accompanied by online resources at available in the 'Downloads' section, below

Publisher: Routledge

ISBN: 1135895597

Pages: 248

Year: 2009-03-04

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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.

Publisher: John Benjamins Publishing Company

ISBN: 9027268452

Pages: 443

Year: 2015-11-25

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This book provides a linguist with a statistical toolkit for exploration and analysis of linguistic data. It employs R, a free software environment for statistical computing, which is increasingly popular among linguists. How to do Linguistics with R: Data exploration and statistical analysis is unique in its scope, as it covers a wide range of classical and cutting-edge statistical methods, including different flavours of regression analysis and ANOVA, random forests and conditional inference trees, as well as specific linguistic approaches, among which are Behavioural Profiles, Vector Space Models and various measures of association between words and constructions. The statistical topics are presented comprehensively, but without too much technical detail, and illustrated with linguistic case studies that answer non-trivial research questions. The book also demonstrates how to visualize linguistic data with the help of attractive informative graphs, including the popular ggplot2 system and Google visualization tools. This book has a companion website: http://doi.org/10.1075/z.195.website

Publisher:

ISBN: 0748608176

Pages: 287

Year: 1998

View: 837

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Statistical techniques and corpus applications - whether oriented towards linguistics or language engineering - often go hand in glove, as Oakes demonstrates in this introduction to the subject which is designed for the use of non-mathematicians.

Publisher: Bloomsbury Publishing

ISBN: 147425179X

Pages: 216

Year: 2018-02-22

View: 729

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An accessible, user-friendly guide to the variety of different experimental methods used in sociolinguistics, Experimental Research Methods in Sociolinguistics walks students through the "how-to†? of experimental methods used to investigate variation in both speech production and perception. Focusing squarely on practice and application, it takes the reader from defining a research question, to choosing an appropriate framework, to completing a research project. Featuring a companion website with information on experiment-friendly software, sample experiments and suggestions for work to undertake, the book also covers: -Ethical concerns -How to measure production and perception -How to construct and use corpora

Publisher: Cambridge Scholars Publishing

ISBN: 1443887765

Pages: 190

Year: 2016-01-14

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Linguists with no background in statistics will find this book to be an accessible introduction to statistics. Concepts are explained in non-technical terms, and mathematical formulas are kept to a minimum. The book incorporates SPSS, which is a statistics package that incorporates a point and click interface rather than complex line-commands. Step-by-step instructions are provided for some of the most widely used statistics in linguistics. At the same time, the concepts behind each procedure are also explained. Traditional analyses such as ANOVA and t-tests are included in the book, but linguistic data is often not amenable to such analyses. For this reason, non-parametric and mixed-effects procedures are also introduced.

Publisher: Oxford University Press

ISBN: 0195347846

Pages: 368

Year: 2005-03-24

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"Theory of mind" is the phrase researchers use to refer to children's understanding of people as mental beings, who have beliefs, desires, emotions, and intentions, and whose actions and interactions can be interpreted and explained by taking account of these mental states. The gradual development of children's theory of mind, particularly during the early years, is by now well described in the research literature. What is lacking, however, is a decisive explanation of how children acquire this understanding. Recent research has shown strong relations between children's linguistic abilities and their theory of mind. Yet exactly what role these abilities play is controversial and uncertain. The purpose of this book is to provide a forum for the leading scholars in the field to explore thoroughly the role of language in the development of the theory of mind. This volume will appeal to students and researchers in developmental and cognitive psychology.

Publisher: A&C Black

ISBN: 1472566963

Pages: 304

Year: 2013-12-05

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Presents a comprehensive introduction to analysing quantitative linguistic data. Starting with an definition of quantitative data, and how it differs from qualitative data, Seb Rasinger examines what the student linguist is trying to find out through analysing data, and how quantitative techniques can help arrive at meaningful and accurate conclusions. This expanded, 2nd edition now also includes a discussion of Analysis of Variance (ANOVA) and MANOVA, and provides a brief introduction to statistical meta-analysis. A companion website allows readers to download crib sheets and Excel templates for the main statistical tools. The book introduces: -using statistics -variables -reliability of data -describing data -analysing data -testing hypotheses -dealing with problematic data. Each chapter includes graphs and figures explaining theory through worked examples, chapter summaries, and exercises to aid student understanding. An appendix containing a summary of statistical formulae, excel commands and statistical tables is included and is an invaluable resource. Presenting a down-to-earth and readable introduction to quantitative research, this book is a useful how-to guide for students encountering quantitative data for the first time, or for postgraduates embarking on linguistic research projects.

Publisher: Springer Science & Business Media

ISBN: 9401008442

Pages: 335

Year: 2012-12-06

View: 1234

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This book is a comprehensive introduction to the statistical analysis of word frequency distributions, intended for computational linguists, corpus linguists, psycholinguists, and researchers in the field of quantitative stylistics. It aims to make these techniques more accessible for non-specialists, both theoretically, by means of a careful introduction to the underlying probabilistic and statistical concepts, and practically, by providing a program library implementing the main models for word frequency distributions.

Publisher: John Wiley & Sons

ISBN: 1118448960

Pages: 1080

Year: 2012-11-07

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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

Publisher: Springer

ISBN: 3319031643

Pages: 194

Year: 2014-06-10

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Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.