It is also a guidebook one that leads its readers through the sometimes daunting process of setting up and carrying out a quantitative study from start to finish. And finally, it is a manual, as it provides its readers with hands-on practical advice on quantitative research in general and statistical methods in particular.

The original idea for the first edition of this book, published in , followed an argument with some of my MA students. Having discussed the factors influencing second-language acquisition success or failure, we had started to look at the issue of learner motivation, and I came to ask a how we could empirically prove how motivated a learner was and b how we could draw sound conclusions that motivation and outcome are causally related.

The result was grim. Several students, all of them language teachers with several years experience, argued that any good teacher is able to see that this is the case. And, finally, one of them accused me of trying to measure everything you just have a too scientific mind. It took some not- so-gentle encouragement to persuade them that any academically sound piece work would inevitably have to use methodologically proper methods and techniques, or else.

Five years down the line, I am still having these discussions, but at least I have a book to refer them to. Quantitative research need not to be difficult!

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The subject benchmarks for linguistics undergraduate degrees by the UK Quality Assurance Agency the organization monitoring academic standards in higher education state that:. Subject benchmarks. My emphasis. It might be rather odd to start an introductory textbook with quotes from a policy document; yet, it shows just how important a thorough understanding of research methods is. Over the years, I have marked a number of student assignments at various universities and at various levels, which started off as excellent pieces of work, but once it came to the data analysis part it went downhill: all too often, the author just did not know how to properly handle the data they collected, analyses are flawed and basic mathematical operations simply wrong.

Even worse, some continued to make the argument they had started, whether the empirical findings support it or not. Hardly anything is worse than getting or giving! There is another side to this, too. Some of us may not want to conduct a quantitative study ourselves, for whatever reason; however, this does not mean that we do not need to understand the basics of quantitative research. The majority of studies in psycholinguistics and sociolinguistics particularly those dealing with linguistics variation are based on quantitative data and analysis; and if we want to fully understand these studies, we need to have at least some idea what all those terms, indices, figures and graphs mean.

## Quantitative Research in Linguistics : Sebastian M. Rasinger :

There is, undoubtedly, a myriad of books on quantitative methods and statistics on the market. Predominantly, these inhabit the bookshelves of disciplines such as social and natural sciences, psychology or economics. Indeed, there are statistics books which cater specifically for linguists, too, for example Butler or Woods et al. All of them comprehensive pieces of work which leave little to add to. Yet, experience in teaching quantitative methods and statistics to students and professional linguists alike has shown that what many people read is not only an introduction to statistics the number crunching bit but also the general approach that is quantitative research: frequently it is not only the maths that is the problem, but the entire methodological setup.

## Quantitative Research in Linguistics

And hence, this book is not one on statistics alone, but an introduction to quantitative methods in general, specifically written for students and researchers in languages and linguistics. As such, this book is written for three particular types of people, which, in some form or other, all feature in the life of its author:.

This book has no prerequisites other than very basic numeracy skills and some basic knowledge of using a computer and standard software: being able to do basic mathematic operations such as addition, subtraction, multiplication and division, by hand or with the use of a calculator is all that is needed. And, of course, being a linguist helps! This book does not intend, nor claim, to make its reader love statistics. Alternatively, you may want to use the book or individual chapters to understand particular concepts and tools of quantitative research when reading studies using these tools.

This book does not aim at elaborating or explaining the mathematical intricacies of the methods discussed. Rather, it has been written as a practical hands-on guide and should enable its readers to get going and actually do quantitative research from the very first pages onwards. Throughout the book, we try and look at examples or real linguistic research, and will discuss the methods and tools employed in research such as Labovs Language in the Inner City , Trudgills work on sociolinguistic variation in Norwich , or Johnson and Newports studies on the Critical Period in second-language acquisition , , all of which is aimed to help readers understanding the concepts introduced.

This has the advantage that readers can read the original studies simultaneously with the relevant chapter, and increase understanding of the issues discussed.

Reviews of the previous edition have criticized that the examples focus somewhat heavily on sociolinguistic and applied linguistic examples something I entirely agree with. This, in part, reflects my own research interests, but, more importantly, a lot of the examples are comparatively easy to understand and often part of a teaching curriculum.

Trying to explain statistics while simultaneously explaining complicated linguistic concepts is beyond the scope of this book. Furthermore, with computers being omnipresent, we will introduce the appropriate steps for performing a statistical analysis using spreadsheet software, specifically Microsoft Excel. Again, this is to avoid complicated manual calculations and will give readers the opportunity to actually do statistics as quickly as possible. While this is not a textbook on using Excel, readers will need no more than basic knowledge of how to enter data into a spreadsheet software the title of Harveys Excel for Dummies might scratch peoples egos a bit but it is a good starting point for those who have not previously used the programme.

The task of writing a second edition of an introductory textbook is not an easy one: on the one hand, it ought to be different, but on the other, it ought to retain its scope and depth, too. I have hence both expanded and gently updated the previous edition. The book is divided into three parts: Part One Chapters Two to Four introduces readers to the basics of quantitative research, by discussing fundamental concepts and common methods. Chapter Two introduces the key concepts of quantitative research and takes a close look at different types of variables and measurement as well as reliability and validity.

The third chapter introduces readers to various different research designs and provides information on how to choose an appropriate design for a particular research questions. Chapter Four is dedicated to an in-depth discussion of questionnaires, their design and use in quantitative research. Part Two Chapters Five to Nine covers statistical tools most commonly used in linguistic research. Specifically, we will discuss how to describe data properly Chapters Five and Six , how to test relationships between variables using standard statistical tools Chapter Seven , and how to check whether different sets of data are significantly different from each other, for example in pre- and post-test situations Chapter Eight.

Chapter Nine provides an overview of statistical tools for the analysis of non-normal data. Part Two has been written with the motto in mind, get as much as possible out of your data with as little effort as possible, and readers should be able to perform simple but thorough analyses of any data set, using Microsoft Excel or similar software. The second part contains many tasks and exercises readers are encouraged to do.

Many of these tasks are discussed in detail on the spot to ensure understanding nothing is worse than having a generic answer somewhere on the back pages but no one really knows where the solution comes from, letalone how to interpret it! Solutions for some additional exercises can be found in Chapter Eleven, together with the statistical tables necessary for the interpretation of results. The final part, consisting of Chapter Ten, is new: it provides an overview of more advanced methods. The first section discusses Multivariate Analysis of Variances MANOVAs , followed by a discussion of statistical meta- analysis a method to combine not to say, recycle previous results in a meaningful manner.

In Part Two, I have included Quick Fix boxes which summarize the main statistical tools discussed in the chapter. You can also find cheat sheets for these tools on the companion website, together with Excel templates and short video clips demonstrating how to conduct certain statistical tools in Excel.

All parts now also include a further reading section at the end. Before you start reading the book, a word of advice from my experience as both a student and a teacher of quantitative methods: do try and actually apply what you read as soon as possible. I have written the book in such a way that it should be hopefully relatively easy to simultaneously read and practically follow the issues discussed.

So get pen and paper, or your computer, ready. As with many things in life, you will improve with practice so start as early as you can. Key words: causality concept dependent variable hypothesis independent variable latent variable law level of measurement measurement qualitative research quantitative research reliability theory type of variable validity.

This chapter discusses some of the basics of quantitative research the main focus of this book. After an outline of the differences between qualitative and quantitative research, we will look in some detail at the concepts of measurement and different types of variables, followed by a discussion of causal relationships.

Lastly, the concepts of validity and reliability are introduced. Qualitative and quantitative data While most people have some idea that there is a difference between quantitative and qualitative research, the general concept they have often seems fundamentally flawed. Having taught research methods courses at several universities and at several levels, the most common answer to my question whats the difference between qualitative and quantitative data has usually been somewhere along the lines of quantitative data means much data, and qualitative data is good data.

In the worst-case scenario, this may lead to statements such as I want to do qualitative research because I want it to be really good or, alternatively, I cant be bothered to collect all that data. The idea that quantitative data refers to large amounts of data is only partially true. In fact, the vast majority of quantitative data analysis tools which will be discussed in this book require a data set of a decent size in order to work properly.

Small amounts of data can often lead to insignificant, inconclusive or flawed results because the mathematical procedures involved in quantitative or statistical analyses require a certain amount of data in order to work properly we discuss this in more detail from Chapter Five onwards. Nevertheless, the main characteristics of quantitative data is that it consists of information that is, in some way or other, quantifiably. In other words, we can put quantitative data into numbers, figures and graphs, and process it using statistical i.

A typical quantitative variable i. In fact, counting occurrences of particular features or, technically correct, the outcome of a particular variable is the simplest form of quantitative analysis yes, quantitative analysis can be as easy as that! We have a closer look at the different types of quantitative data further down in this chapter.

When using qualitative data,. Qualitative analysis in linguistics is most commonly found in discourse analytic research.