Notesontheuse of Rforpsychology experiments and questionnaires

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Now also, phonologists, syntacticians and historical linguists are finding linguistic research to involve quantitative methods. Consequently, mastery of quantitative methods is becoming a vital component of linguistic training.

University of North Texas Computing Center - Research and Statistical Support

This book has two foci. First, we will introduce and discuss general strategies and methods of quantitative analysis as they apply in several subdisciplines in linguistics.

Second, the book provides detailed instruction in practical aspects of handling quantitative linguistic data, using a particular statistical package R to discover patterns in quantitative data and to test linguistic hypotheses. After two introductory chapters, the book is divided into chapters by subdiscipline of linguistics, though the methods presented in any one chapter are likely to be relevant in almost any other subdiscipline. So, though t-test is presented in the phonetics chapter, for example, you would certainly want to make use of t-tests in psycholinguistics.

Fundamentals of quantitative analysis -- Observations, distributions, central tendency, variability.

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Phonetics -- two-sample and paired t-tests, multiple regression, principle components analysis. Psycholinguistics -- Analysis of variance, between groups and within groups factors, repeated measures.


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Sociolinguistics -- Chi-squared, logistic regression. Historical Linguistics -- Lexicostatistics, language similarity, clustering, and multidimensional scaling.

Syntax -- Magnitude estimation, linear mixed effects models, mixed effects logistic regression. References Appendix 1: Getting started with R Appendix 2: Data sets and scripts used in examples and exercises. While you are at the R Project site, be sure to look at the manuals page as a starting point for documentation. I found "Notes on the use of R for psychology experiments and questionnaires" by Jonathan Baron and Yuelin Li to be particularly useful.

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In this book, I chose to focus on a software package called "R" that is developed under the GNU license agreement. False convergence, numerical instability, and problematic likelihood surfaces can be diagnosed without much agony by most interested social scientists if they have specific advice about how to do so. Straightforward computational techniques, such as data rescaling, changes of starting values, function reparameterization, and proper use of analytic derivatives, can then be used to reduce or eliminate many numerical problems.


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The second aspect of the course is focused on mathematical computing. R is an implementation of the S language, which is the default computational tool for research statisticians. R is 'GNU S': language and environment for statistical computing and graphics. R is similar to the award-winning S system, which was developed at Bell Laboratories by John Chambers et al.

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It provides a wide variety of statistical and graphical techniques linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, R is designed as a true computer language with control-flow constructions for iteration and alternation, and it allows users to add additional functionality by defining new functions.