Statistics In Language Research

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.

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.

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.

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.

Assuming no familiarity with statistical methods, this text for language education research methods and statistics courses provides detailed guidance and instruction on principles of designing, conducting, interpreting, reading, and evaluating statistical research done in classroom settings or with a small number of participants. While three different types of statistics are addressed (descriptive, parametric, non-parametric) the emphasis is on non-parametric statistics because they are appropriate when the number of participants is small and the conditions for use of parametric statistics are not satisfied. The emphasis on non-parametric statistics is unique and complements the growing interest among second and foreign language educators in doing statistical research in classrooms. Designed to help students and other language education researchers to identify and use analyses that are appropriate for their studies, taking into account the number of participants and the shape of the data distribution, the text includes sample studies to illustrate the important points in each chapter and exercises to promote understanding of the concepts and the development of practical research skills. Mathematical operations are explained in detail, and step-by-step illustrations in the use of R (a very powerful, online, freeware program) to perform all calculations are provided. A Companion Website extends and enhances the text with PowerPoint presentations illustrating how to carry out calculations and use R; practice exercises with answer keys; data sets in Excel MS-DOS format; and quiz, midterm, and final problems with answer keys.

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.

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.

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.