28.4143, Review: General Linguistics; Text/Corpus Linguistics: Eddington (2015)
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Subject: 28.4143, Review: General Linguistics; Text/Corpus Linguistics: Eddington (2015)
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Date: Tue, 10 Oct 2017 15:47:40
From: Chiara Meluzzi [chiara.meluzzi at yahoo.it]
Subject: Statistics for Linguists: A Step-by-Step Guide for Novices
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Book announced at http://linguistlist.org/issues/26/26-5673.html
AUTHOR: David Eddington
TITLE: Statistics for Linguists: A Step-by-Step Guide for Novices
PUBLISHER: Cambridge Scholars Publishing
YEAR: 2015
REVIEWER: Chiara Meluzzi, Scuola Normale Superiore
REVIEWS EDITOR: Helen Aristar-Dry
SUMMARY
David Eddington’s book “Statistics for Linguists. A Step-by-Step Guide for
Novices” presents a detailed account of the main statistical methods in
linguistic analysis, by using the software SPSS 20. From its subtitle, it is
clear that the book is intended for newbies who know little or nothing about
both statistics and the IBM software SPSS. Moreover, even if graphs and
figures refers to the 20th version of the software, in the Introduction the
author states that it will work also for the closest versions, either slightly
older or newer.
The book consists of nine chapters preceded by a short introduction.
Chapter one “Getting to know SPSS” introduces the software from the first
steps (e.g., open or saving a file in SPSS). This first chapter could be
skipped by more expert or semi-expert users of SPSS, as it is intended for
real newbies. It also gives a first view of the whole structure of the book,
which presents many figures to reproduce the software’s windows and boxes.
Chapter two “Descriptive and Inferential Statistics” introduces the
fundamental concepts of quantitative analysis, namely data, variables,
descriptive and inferential statistics. Firstly, the chapter presents the
different types of data that could be used in SPSS: categorical data, also
labelled either as nominal or factor variables, are represented by named and
unordered categories, such as levels of education, or ethnicity. It goes
without saying, that these categorical data are the most frequently used in
linguistics. Ordinal data are represented by labeled intervals in a given
scale of values. An example of ordinal data is the so-called Likert scale,
which is often used in linguistic analysis, for instance, in perception tests
or in testing language attitudes. Continuous data are numeric variables, such
as age, duration of a fricative segment, or months of studying of another
language. All these types of data could be variables, either independent or
dependent ones, but also control and confounding variables. Control variables
are represented by those variables that we know may influence the results
(e.g., textual genre and the presence/absence of different forms of past
tense). Confounding variables are implicit factors of variation that may
influence the results and, in some cases, undermine the whole structure of our
analysis if not satisfactorily pondered in the research design. For instance,
in preparing a word list for a phonetic experiment one must consider not only
the specific variables addressed by the research question (e.g., phonological
context, length of the word, surrounding vowels), but also possible factors
which could affect the production and, as a consequence, the whole research
design of the experiment (in the given case, the influence made by prosody in
a repetitive unnatural task such as word-list reading). It is good practice to
take into account and to control for as many confounding variables as
possible, but Eddington himself admits that sometimes “we only find out about
their existence after the data have been gathered and analyzed” (p. 9).
Finally, the chapter introduces the basic distinction between descriptive
statistics (i.e., how to describe and summarize your data and their
distribution throughout the corpus), and inferential statistics (i.e., the
possibility of applying the results obtained on a small sample of the
population to the entire population). In the so-called “descriptive
statistics”, different measures can be used according to the types of data:
the mean, the median, the mode, the dispersion, etc. The chapter also explains
how to calculate those values in SPSS, and also how to visualize the data
using histograms and boxplots generated by the software, be presenting
pictures not only of the input window but also of the output window, with
accurate descriptions of both. Moreover, Eddington spends some pages on the
notion of the normal distribution (i.e., following the characteristic bell
curve), and the tests to verify the data’s normal distribution. As for
inferential statistics, the traditional experimental practice works with two
hypotheses: the so-called null hypothesis states that there is no relationship
between two variables, whereas the alternative hypothesis states that variable
A influences the behavior of variable B. The main goal of inferential
statistics is to reject the null hypothesis, and to confirm the influence
(and, maybe, the strength) of the relationship between two variables,, not
only in a sample of data but in the entire population. A correlation is said
to be statistically significant if it has a level of significance, or alpha
level, or p value of .05 or lower. This corresponds to a 5% probability that a
given result has been obtained by chance only. Even if the simple p value has
some limitations (pp. 22-23), it is however the most commonly used statistic.
Having established the background, Chapters Three through Seven consider the
most frequently used statistical measures, by discussing some theoretical
issues with concrete examples, as well as the input and output windows given
by the software.
Chapter 3 “Pearson Correlation” moves into the statistical analysis of data,
starting with continuous variables (e.g., percentage of monophthongs, or years
lived outside South Britain). The chapter illustrates how to plot this
correlation using scatter plots (p. 27-29), and how to statistically evaluate
the relationship between the two variables by using the Pearson correlation
coefficient ‘r’, which ranges from +1 to -1. A negative correlation indicates
an inverse relationship between the two variables, whereas a positive
correlation indicates a direct relationship; a value of r = 0 means that there
is no relationship between the two variables. The chapter also explores the
difference between parametric and nonparametric statistics: the first relies
on normal distribution of data, whereas the latter is based on data not
showing any particular distribution, and models that evolve in order to
accommodate to the complexity of the data.
Chapter 4 “Chi-Square” presents the use of a goodness of fit chi-square to
analyze data with a single variable (pp. 43-46), but also uses the chi-square
test of independence (p. 47) to analyze data with more than one independent
variable. It is important to note that the chi-square relies on data not
following a normal distribution since it is a nonparametric statistic. The
chi-square tells us if there is an interaction between two independent
variables, whereas Cramer’s V is used to measure the strength of this
interaction on a 0-to-1 scale, with 0 indicating no relationship. Both these
values could be easily calculated in SPSS by using the “Crosstabs” dialogue
box.
Chapter 5 “T-Test” presents the statistic to be performed to evaluate the
significance of two groups of values, with a continuous dependent variable and
a categorical independent variables with 2 values. This test could be used to
compare two groups of speakers, or the same group after a particular
linguistic training as it is usually done in language acquisition research. As
for the chi-square, after having established that the difference between two
groups is significant, it is possible to use Cohen’s d coefficient to
calculate the effect size distinguishing the two groups. However, it is
important to note that the t-test runs on normally distributed data. A
Mann-Whitney test is more appropriate for skewed data, which may also contain
outliers.
Chapter 6 “ANOVA (Analysis of Variance)” presents several types of variance
analysis: one-way, Welch’s, factorial, and repeated measures . Generally
speaking, the “analysis of variance” refers to the fact that it considers the
means of each set of data (or group of speakers) and the variance of the
scores, that is how much the scores area spread out from the mean (p. 65).
Eddington also points out the importance of running a post hoc analysis to
compare each group to the others, and to test whether they are statistically
different or not: he suggests using the Tukey or Scheffé test, depending on
number of scores, whereas Dunnet test may be preferred when using a control
group, which is often the case in linguistic analysis. However, one-way ANOVA
works on homoscedastic data, that is if variance is homogenous for all random
variables in a sequence or vector. This is also a major concern in regression
analysis. For the analysis of variance, if a group of data is heteroscedastic
(i.e., the inverse of homoscedastic) Welch’s ANOVA has to be preferred.
Factorial ANOVA is designed to check the effect of more than one independent
variable, even the more the variables the harder the interpretation of the
results (p. 74). Finally, repeated measures ANOVA deals with sets of data in
which the same subject is included in more than one group: the classic example
is the phonetic analysis of different tokens as repeated by speakers more than
once. Repeated measures are fully addressed in Chapter 8, together with
mixed-effect models.
Chapter 7 “Multiple Linear Regression” explores maybe the most important tool
for linguistic analysis when dealing with different independent variables,
either categorical or continuous. Eddington firstly explores the key issues in
simple regressions, then the chapter moves to multiple regression. A
particular emphasis is placed on the interpretation of the output, in terms of
the simple visualization of the SPSS charts and, more important, of the
correct evaluation of these numbers within a linguistic research design. The
chapters also explains how to deal with categorical data in multiple
regression analysis by using “dummy code”, that is having only two variables
allowed: for instance, yes/no questions or the sex of the speaker
(male/female). The author emphasizes that it is important to dummy code the
categorical variables in order to avoid error messages from the software (p.
93).
The final chapters present some more complex tools, whose applicability is
increasing recently in linguistic research. Chapter 8 deals with “Mixed-Effect
Models: Analysis of Repeated (and Other Nested) Measures”. Because of the more
complex argument, Eddington first presents a theoretic example, but follows it
with hands-on examples, in order to emphasize the possible use of mixed
models in linguistic research; then, he moves on explaining how to use the
models with SPSS, as usual with an emphasis on the right interpretation of the
output offered by the software. Generally speaking, the main advantage of
using mixed-effect models (MEM) is that they provide a robust analytical
approach for addressing problems associated with hierarchical data. MEMs can
also take into account missing data, and have less restriction in their
applicability if compared to ANOVA, (as shown in Chapter 6). As Eddington
points out “when random factors are included, the results are considered more
generalizable to other members of the random factors (other people and other
test items) that we haven’t actually tested” (p. 118). It is evident that
mixed models represent a very powerful tool for linguistic research, and it
goes without saying that their use is increasing in recent years.
Finally, Chapter 9 presents “Mixed-Effects Logistic Regression”. Logistic
regression is particularly useful when your dependent variable is categorical,
with two or more values: for instance, if you carry out a morpho-syntactic
analysis on the distribution of periphrastic constructions vs. tense marking
according to different contextual variables, as often happens in
sociolinguistic research. As for mixed models, within logistic regression data
can be differently analyzed, according to different types of coding
illustrated in the chapter (e.g., treatment coding, deviation coding). The
important thing to keep in mind is, again, the basic assumptions of logistic
regression: even if the calculation doesn’t have the same requirements as are
needed for continuous variables, it is a good practice to have at least 20
observations for each independent variable included in the analysis.
Conversely, the results of the regression might be cozy and not accurate (p.
154). Like the preceding chapters, the book ends with a list of possible
examples and exercises to apply logistic regression to real data deriving from
a linguistic experiment.
EVALUATION
The main aim of the book is really challenging: explaining statistics to
novices with a focus on linguistic research, and, at the same time,
illustrating how to perform these analyses in a new software (i.e., SPSS 20,
or other versions). Even if complex and not exactly easy reading, Eddington’s
book manages to achieve his goal by providing a very useful survey of the main
statistical tools, moving from the most common ones (e.g., the chi-square or
p-value) to the most complex and recently implemented ones such as the mixed
models. One of the greatest advantages of the book, which definitely
distinguishes it from other “introduction to statistics” books already
available, is that is specifically intended for linguists: this means that
examples are taken from already existing experiments, many of them conducted
by the author himself, or from hypothetical experimental settings without
being limited to a specific linguistic subfield of research. Thus, the book
introduces both the basic concepts in statistics research, and the way to
concretely apply these concepts using the software. For this reason, the book
is also full of images showing both the input and output windows provided by
SPSS: this is incredibly useful for “novices” approaching the IBM software for
the first times. In contrast to other introductions to the software (e.g.,
Gray & Kinnear 2011), Eddington’s book presents only those images strictly
needed for the explanation, without wasting time in addressing subtle
mathematics details usually not of interest to the main audience.
However, a general question could arise with respect of the software chosen.
In fact, to do quantitative analysis there are other powerful tools, which
have also the advantage of being free of charge. The first of the list is,
obviously, R. Indeed, R is, to my knowledge, (one of) the most popular tools
for doing statistics in linguistic research, and there are many introduction
to statistics using this software (e.g., Baayen 2008). Even if free, R
presents some disadvantage in particular for novices in statistics, the main
one being that it is not very user-friendly: in fact, R works with strings of
code, whereas SPSS offers a fancier and “Excel-like” environment which could
be less intimidating to the newbies, and help the sporadic users of the
software, who will not have to remember commands and codes just to open a
folder or create a simple frequency table. Finally, if there are many guides
to statistics in R for linguistic research (e.g., the fundamental Baayen
2008), such a book has not existed for SPSS before Eddington’s guide. Of
course, other scholars have used this software in specific fields of
linguistic research (e.g., Larson-Hall 2010), and for introduction to the
software one cannot mention Andy Fields’s books (e.g., Fields 2009) or the
official guides (e.g., Gray & Kinnear 2011). Eddington, however, clearly
explains the potentiality of the software for its application in linguistic
research in general. Moreover, the book is small, if compared to Fields (2009)
or other guides, since it contains the essential details needed for doing the
analysis, by illustrating the basic assumptions and goals of each test. In
fact, SPSS is a powerful tool, which could really help researchers save time
with analysis and graphs, but it needs to be interpreted with precision.
Eddington’s book is in this respect really a life-savior, since it explains
how to interact with the software not only in the direction of giving
instructions to the machine, but also in interpreting the results. What the
book lacks is a final bibliographical section, for both a general theoretic
perspective (e.g., Johnson 2008; Bod, Hay & Jannedy 2003, just to quote a
few), and its application in specific linguistic fields (for instance,
Larson-Hall 2010 for second language research, which also use SPSS; the old
but gold Oakes 1998 for corpus linguistics, and Macaulay 2009 for
sociolinguistics).
Moreover, Eddington is very aware of the risks of imprecise statistics
research, particularly with regard to: (1) the use of the correct statistical
tool for your set of data; (2) the correct interpretation of the output of
your analysis, given the possible contradictions or unexpected results that
sometimes may appear. In this respect, the author will states that
“correlations are only dangerous when people take them to show causation,
because correlations show relationships but not necessarily causes” (p.32).
Eddington also gives the example of a correlation between height and IQ: even
assuming that such a correlation might exist for a certain sample, this
doesn’t mean that height causes intelligence. Short said, one must always be
cautious in the assumptions and in how statistics is used in a rigorous
scientific paradigm. Similar caveats are proposed throughout the different
chapters of the book.
In conclusion, Eddington’s book represents an exceptional tool for
understanding the possibilities of the quantitative paradigm in linguistic
research. At the same time, the book represents a real “step-by-step” guide to
performing statistical analysis in SPSS.
REFERENCES
Baayen, Harald R. 2008. Analyzing Linguistic Data. A Practical Introduction to
Statistics using R. Cambridge: Cambridge University Press.
Bod, Rens, Hay, Jennifer & Jannedy, Stephanie. 2003. Probabilistic
Linguistics. Cambridge (MA): MIT Press.
Field, Andy. 2009. Discovering Statistics using SPSS (third edition). London:
Sage.
Gray, Colin D. & Kinnear, Paul K. 2011. IBM Statistics 19 Made it simple. Hove
& New York: Psychology Press.
Johnson, Keith. 2008. Quantitative Methods in Linguistics. London: Blackwell.
Larson-Hall, Jennifer. 2010. A guide to Doing Statistics in Second Language
Research Using SPSS. London: Routledge.
Macaulay, Ronald K.S. 2009. Quantitative Methods in Sociolinguistics. New
York: Palgrave.
Oakes, Michael P. 1998.Statistics for Corpus Linguistics. Edinburgh: Edinburgh
University Press.
ABOUT THE REVIEWER
Chiara Meluzzi is Postdoc fellow at Scuola Normale Superiore in Pisa (Italy)
with a project on gestural coordination and speech rhythm in Italian
dysillables. She mainly works in sociophonetics, experimental phonetics and
sociolinguistic research on Italian. Her main publications includes various
articles on the sociophonetic distribution of Italian dental affricates in
Bolzano/Bozen, an analysis of rhotic variation in an Italian and Sicilian
bilingual corpus (Loquens, 3:1, in collaboration with C. Celata and I. Ricci),
and a pragmatic analysis of personal pronouns in Ancient Greek comedies
(Pragmatics 26:3).
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