Statistics in Language Studies

by Anthony Woods, Paul Fletcher, Arthur Hughes

Overview

This book demonstrates the contribution that statistics can and should make to linguistic studies. The range of work to which statistical analysis is applicable is vast: including, for example, language acquisition, language variation and many aspects of applied linguistics. The authors give a wide variety of linguistic examples to demonstrate the use of statistics in summarising data in the most appropriate way, and then making helpful inferences from the processed information. The range of techniques introduced by the book will help the reader both to evaluate and make use of literature which employs statistical analysis, and to apply statistics in their own research. Each chapter gives step-by-step explanations of particular techniques using examples from a number of fields, and is followed by extensive exercises. The early part of the book provides a thorough grounding in probability and statistical inference, and then progresses through methods such as chi-squared and analysis of variance, to multivariate methods such as cluster analysis, principal components analysis and factor analysis. None of these techniques requires the reader to have a grasp of mathematics more complex than simple algebra. Students and researchers in many fields of linguistics will find this book an invaluable introduction to the use of statistics, and a practical text for the development of skills in the application of statistics.

Product Details

ISBN-13:	9780521273121
Publisher:	Cambridge University Press
Publication date:	08/14/1986
Series:	Cambridge Textbooks in Linguistics
Edition description:	New Edition
Pages:	336
Product dimensions:	5.98(w) x 9.02(h) x 0.75(d)

Table of Contents

Preface; 1. Why do linguists need statistics?; 2. Tables and graphs; 3. Summary measures; 4. Statistical inference; 5. Probability; 6. Modelling statistical populations; 7. Estimating from samples; 8. Testing hypotheses about population values; 9. Testing the fit of models to data; 10. Measuring the degree of interdependence between two variables; 11. Testing for differences between two populations; 12. Analysis of variance - ANOVA; 13. Linear regression; 14. Searching for groups and clusters; 15. Principal components analysis and factor analysis; Appendices; References; Index.