27.1198, Review: Applied Ling; Lang Acq: Larson-Hall (2015)

Mon Mar 7 21:59:01 UTC 2016

LINGUIST List: Vol-27-1198. Mon Mar 07 2016. ISSN: 1069 - 4875.

Subject: 27.1198, Review: Applied Ling; Lang Acq: Larson-Hall (2015)

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Date: Mon, 07 Mar 2016 16:58:34
From: Page Piccinini [ppiccinini at ucsd.edu]
Subject: A Guide to Doing Statistics in Second Language Research Using SPSS and R

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Book announced at http://linguistlist.org/issues/26/26-2986.html

AUTHOR: Jenifer  Larson-Hall
TITLE: A Guide to Doing Statistics in Second Language Research Using SPSS and R
SUBTITLE: 2nd Edition
SERIES TITLE: Second Language Acquisition Research Series
PUBLISHER: Routledge (Taylor and Francis)
YEAR: 2015

REVIEWER: Page E Piccinini, University of California, San Diego

Reviews Editor: Helen Aristar-Dry

SUMMARY

‘A Guide to Doing Statistics in Second Language Research Using SPSS and R’ by
Jenifer Larson-Hall provides an introduction to hypothesis testing and
statistics, specifically aimed at second language researchers. The book is
split into two parts. The first, ‘Statistical Ideas’, introduces researchers
to the general assumptions of statistics and hypothesis testing, while the
second, ‘Statistical Tests’, demonstrates how to conducted specific tests in
SPSS and R. All practice data comes from real published second language
research experiments, allowing the reader to find the examples directly
relevant to their own work. At several points in each chapter the reader is
given exercises with this data to apply the skills they learned in the most
recent lesson.

Part I: Statistical Ideas

Before introducing the statistical theory in Part I, ‘Chapter 1: Getting
Started with the Software and Using the Computer for Experimental Details’
begins by acquainting the reader with SPSS and R. The reader is shown how to
read in data, create data, and deal with missing data in the SPSS and R
environments. Instead of using the standard R GUI, the reader is instructed to
use R Commander, a wrapper for R installed through a package to allow the user
to rely less on the command line. Larson-Hall says that R Commander can ease
the way for users who do not have previous experience with coding or a command
line language.

In ‘Chapter 2: Some Preliminaries to Understanding Statistics’ the reader is
introduced to basic concepts surrounding statistical analysis. These concepts
include dependent and independent variables, hypothesis testing, p-values, as
well as a push for robust statistics (this is later discussed in more detail
in Chapter 4). This chapter is designed to introduce new researchers to the
principles necessary for clean experimental design and later data analysis.
All of this is presented with exercises focusing on second language research
data sets, by asking the reader to determine the appropriate variables in each
design. The discussion of hypothesis testing is very clear and detailed in
order to give the reader a solid foundation in the assumptions of most
statistics.

‘Chapter 3: Describing Data Numerically and Graphically and Assessing
Assumptions for Parametric Tests’ shows the reader how to get descriptive
values of their data (e.g. mean, median), as well as how to plot the data for
visual inspection. How values such as mean and standard deviation are obtained
is explained in detail. There is also discussion of the assumptions of
parametric tests, such as the fact that data must be normally distributed.
Readers also learn about ways to transform their data to meet these
assumptions. Larson-Hall emphasizes the need to examine data before beginning
analysis to be sure it meets the requirements of any planned tests.

‘Chapter 4: Changing the Way We Do Statistics’ presents a call to move away
from classical statistics that focus on p-values as the ultimate decider of
significance, and instead turn to looking at confidence intervals and effect
sizes. This chapter corresponds with a general trend in the field of
linguistics and psychology to focus more on what it means to have an effect
and how large that effect is, instead of being beholden to a specific p-value
cutoff. In order to allow the reader to fully understand the ‘new statistics’,
the reader is also given a detailed discussion of the ‘old statistics’. Topics
covered in the ‘old statistics’ section include null hypothesis testing and
power analyses.

Part II: Statistical Tests

Before being shown how to run specific tests, in ‘Chapter 5: Choosing a
Statistical Test’ the reader is given an overview of the tests covered in the
book (correlation, regression, t-test, ANOVA, repeated-measures ANOVA).
Additional tests are also made available in online materials. Each test is
given a general summary as well as provided with a mnemonic device for the
reader. There is a distinction made between ‘tests of relationship’
(correlation, regression) and ‘tests of group difference’ (t-test, ANOVA,
repeated-measures ANOVA). For each test the reader is given example papers and
data appropriate for use with that test.

The same format is displayed in each of the following chapters (‘Chapter 6:
Finding Relationships Using Correlation’, ‘Chapter 7: Looking for Groups of
Explanatory Variables through Multiple Regression’, ‘Chapter 8: Looking for
Differences between Two Means with T-Tests’, ‘Chapter 9: Looking for Group
Differences with a One-Way Analysis of Variance’, ‘Chapter 10: Looking for
Group Differences with Factorial Analysis of Variance When there is More than
One Independent Variable’, and ‘Chapter 11: Looking for Group Differences When
the Same People are Tested More than Once’) . Each of the chapters begins with
some recent examples from second language acquisition literature of the
analysis under discussion. The reader is then walked through the steps in both
SPSS and R (using the R commander) to first visually examine the data and then
run the specific analysis. Assumptions for the tests are clearly stated to
ensure the reader is using the tests appropriately. For tests with multiple
comparisons, there are also discussions of how best to conduct post-hoc
analyses. The chapter ends by showing the reader how to report the results of
their test, both numerically and in prose.

Throughout the chapters the reader learns how to make a variety of figures
including scatterplots with regression lines, scatterplot matrices, boxplots,
histograms, Q-Q plots, interaction plots, and parallel coordinate plots. For
any R code used, all of the arguments are explained in tables so the reader
can better understand each part of the code. Several R packages are also
introduced and used to help with manipulating data, making figures, and
running statistical tests.

EVALUATION

The book is a good introduction to new researchers on how to frame their
research questions and collect data for later statistical analysis. Part I is
thus a good read for any new researcher confused by how to approach their
experimental questions. Having such a large number of real second language
research data sets is a huge benefit, as most statistics books use data sets
that are less accessible (or less interesting) to second language researchers.
The fact that the data is from actual published papers also goes a long way
toward showing how messy real data can be.

The specific tests chosen for explanation felt a little deficient. While very
good for an introductory textbook, the tests chosen for explanation in this
book are unlikely to teach anything to someone with experience in statistical
analysis. Furthermore, while it is important for new researchers to have a
basis in the more classical tests,  in the current world of more advanced
statistical analyses, having knowledge of only these tests will be
insufficient. For example, more and more reviewers have come to expect linear
mixed effects models as a replacement to ANOVAs. Similarly, the chi-squared
test, a useful non-parametric test, is missing from the book. Materials for
both of these tests are available online in the new ‘A Guide to Doing
Statistics in Second Language Research Using R’ which is free to download, but
it is unfortunate that the material is not included in the main textbook.
While more advanced topics such as these may be outside of the scope of an
introductory book, this omission is worth considering if indeed the goal of
this book is to take the reader from data analysis to publication.

Additionally, I would not recommend this book to someone who wants to become
newly acquainted with R. The book is heavily reliant upon the R Commander GUI,
which uses dropdown menus similar to SPSS to make figures and perform
statistical tests. While this can be appealing for new users to R, I do not
think it is a good long-term practice. For example, one of the benefits of
using a command line language for statistics instead of one with a GUI, is the
ability to easily share data and scripts so that other researchers can
reproduce your analyses with old or new data. Saving code was mentioned only
in a tip which stated “Because the command can get long, you may want to paste
it onto a different place, like a Word document, to add in different arguments
easily and then paste the result into the R Console” (p. 199). This is not a
good practice and can lead to problems, as Larson-Hall herself notes, “One
thing to be careful of here, however, is your quotation marks. R will not
accept Word’s ‘smart quotes’ which are curly. Copy and use the quotes that you
pasted over from R which are just straight up and down”. A much better
practice would be to simply save the code in an R document, which will not
have formatting errors and can be opened in the future to conduct the analysis
with the same or different data. Again, while I appreciate that using a GUI
like the R Commander can seem attractive to someone new to R, in the long run
I think it would be much better to risk initial frustrations by teaching the
command line and scripts.

Overall I would recommend this book to a new researcher who wants a better
understanding of how to ask research questions, and then design experiments to
statistically answer those questions. The book is also a very good
introduction to more traditional statistical tests, providing in-depth
discussions of when and how they should be used. This level of detail,
complemented by real second language research data sets, makes it a good
introductory textbook for new second language researchers.

ABOUT THE REVIEWER

Page Piccinini is a Ph.D. candidate at University of California, San Diego.
Her research interests are bilingualism, psycholinguistics, and phonetics. Her
dissertation focuses on the phonetics of code-switching, and its implications
on theories of bilingual speech production and perception.

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