Frauke Kreuter

Vanessa Beddo

English

Research Papers in Economics

JSS Journal of Statistical Software
June 2004, Volume 11, Book Review 2/3. http://www.jstatsoft.org/
Reviewer: Vanessa C. Beddo and Frauke Kreuter
University of California at Los Angeles
A Handbook of Statistical Analyses using SPSS
Sabine Landau and Brian Everitt
Chapman & Hall/CRC, Boca Raton, Florida, 2004.
ISBN 1-58488-369-3. vii + 354 pp. $44.95 (P).
A Handbook of Statistical Analyses using Stata (Third Edition)
Sophia Rabe-Hesketh and Brian Everitt
Chapman & Hall/CRC, Boca Raton, Florida, 2004.
ISBN 1-58488-404-5. xiii + 308 pp. $49.95 (P).
A Handbook of Statistical Analyses using SPSS by Landau & Everitt (2004) and A Handbook
of Statistical Analyses using Stata, Third Edition by Rabe-Hesketh & Everitt (2004) join A
Handbook of Statistical Analyses using S-PLUS and SAS. The handbooks, like their predeces-
sors, are collections of applications of statistical techniques for their corresponding software
packages. The handbooks are situation-speciﬁc learning tools with clearly explained step-by-
step examples that can be followed by the reader using an overlapping set of data provided
on the web.1
Most chapters of the two handbooks reviewed here, revolve around a data analysis technique,
span about 15 to 20 pages in length, and are easy to reference in most sections. This is what
a reader would hope for when picking up a handbook. The chapters start with a research
question, followed by a brief (usually between 1 and 5 page) introduction to the statistical
technique used to answer the research question. However, each chapter’s primarily goal is to
present a worked example that shows how the analysis is carried out in SPSS or Stata. A
short interpretation of analysis results and a battery of exercises follow the initial example.
Solutions to the exercises can be found in the back of the book or on the web.
A Handbook of Statistical Analyses using SPSS begins with data entry fundamentals,
explained clearly and concisely, followed by a brief explanation of elementary SPSS data
management procedures, including variable label and missing value assignments. For the
most part, this book assumes that data is ideally formatted for each analysis situation.
1As of June 2004, http://www.iop.kcl.ac.uk/iop/departments/BioComp/SPSSBook.shtml and
http://www.stata.com/texts/stas32 A Handbook of Statistical Analyses using SPSS/Stata
Simple statistical analyses appear in early chapters as descriptive and inferential techniques,
usually involving univariate outcome variables, both continuous and categorical. Several
examples are discussed in the context of using the SPSS user interface, a point-and-click GUI.
The chapters are essentially a map of these menus, providing common analysis examples for
the novice user. The statistical analysis methods within these sections are buried within the
study discussions, which makes referencing analytic techniques somewhat time-consuming.
Subsequent chapters utilize various regression analysis techniques, such as multiple regres-
sion and ANOVA. A chapter dedicated to multiple regression analysis immediately follows
the chapters describing simple analysis techniques for continuous and categorical data. The
ANOVA examples span over three chapters; one-way ANOVA, factorial design ANOVA, and
ANOVA for repeated measures analysis. ANCOVA is brieﬂy discussed in the section on facto-
rial design, while MANOVA is illustrated in the one-way ANOVA section. These chapters are
clearly organized by analysis technique, with the exception of MANOVA and ANCOVA. This
allows the text to genuinely resemble a handbook, since the sections are easier to reference.
Advanced analysis techniques, such as mixed eﬀects models (in the second of two chapters
dedicated to repeated measures), logistic regression, survival analysis (using Cox’s regression)
and various multivariate analysis techniques (such as factor, principal component, cluster
and discriminant function analysis) are treated in a similar fashion. Each discussion of these
analysis techniques is clearly described and demonstrated, again in the spirit of acting as
a statistical handbook. Within a study on mixed eﬀects models, the authors elaborates
eﬀectively on transposing a data matrix (changing variables to cases). This aspect of data
management, as with the rest of the text, focuses on the GUI which makes the process painless
for the user.
Although A Handbook of Statistical Analysis using SPSS initially appears to have two diﬀerent
organizational constructs, a reader should be able to navigate the text in search of a desired
technique, so long as the procedure for which a reader searches has been used in one of the
many example analysis sections. Once located, the examples will aid a user to eﬀectively
navigate the SPSS GUI in order to conduct the statistical analysis required.2
A Handbook of Statistical Analyses using Stata is in its 3rd edition. The text has
been updated to Stata 8, enriched with three new chapters, includes new graphical features
of Stata 8, and emphasizes diagnostics.
The handbook begins with a brief but broad introduction to Stata, from the use of basic
syntax and data management to more advanced topics, such as looping through variables
and observations, reshaping data, matrix commands, programming, and graphics. This in-
troduction provides the reader with the necessary tools to work through the rest of the book.
In addition throughout the following chapters various data management techniques and pro-
gramming tricks are presented in the context of applications of the statistical analyses. This
additional exposure to complex data management examples is a special feature of this book.
The detailed index on techniques and Stata commands will aid the reader in ﬁnding them
throughout the book.
Data description and simple inference form the earlier chapters in the series of data and
techniques examples, followed by multiple regression, analysis of variance, logistic regression
2Editorial note: The header for Chapter 3 is“Data Description and Simple Inference for Continuous Data.”
It should read “Simple Inference for Categorical data.”Journal of Statistical Software – Book Reviews 3
and generalized linear models. After describing the research question and the statistical
technique used to address it, each chapter starts of with reading ASCII raw ﬁles, examining
the data with the help of the new graphic features in Stata 8, followed by the analysis and
diagnostic examples.
The discussion of longitudinal data analysis is expanded in the 3rd edition of this handbook.
New is the chapter on random eﬀect models and generalized estimation equations, along with a
discussion of estimating the parameters of mixed models using gllamm. (Sophia Rabe-Hesketh
is the principal developer of the Stata user written module gllamm.)
Known from former editions are the chapters illustrating diﬀerent epidemiological designs,
survival analysis, mixture models (in the chapter on maximum likelihood estimation), and
principle component analysis. The last chapter on cluster analysis is also new in the 3rd
edition introducing the Stata 8 extensions for cluster analyses.
A Handbook of Statistical Analysis using Stata will be especially useful for those readers who
have some experience with the Stata language and basic programming and who appreciate
examples as a means to work their way through statistical techniques. While the Stata GUI
has been around since release 8.0, commands are used throughout the book, reinforcing the
importance of replication. The advice given in the introductory chapter on how to build a ﬁle
containing the commands necessary to carry out a particular data analysis, should be taken
seriously. Not only because this is useful for a smooth replication of the analyses examples
but as good practice for data analysis in general.
Though brief, this book contains a lot of useful and practical information on how to carry
out statistical analyses in Stata 8. Readers should be able to follow the mix of challenging
programming tools and advanced data analysis techniques after working through the intro-
ductory chapters.
Generally speaking the audience for both handbooks is quite similar, since they assume
previous knowledge of statistics such as linear regression, correlation, signiﬁcance tests and
simple analysis of variance. On the other hand, the books diﬀer in the amount of previous
programming experience they expect from a reader. The SPSS book uses the GUI as the
sole learning tool, and therefore neither demands programming knowledge from its reader
nor introduces SPSS syntax statements, which make for reusable code. In contrast, the Stata
handbook, with its more intensive and instructive concentration on code production, assumes
a reader who is comfortable conducting statistical analysis via the command line.
Both handbooks consist of a healthy volume of actual research examples, many of them
coming from biostatistics, public health, and epidemiology. This selection of interesting ex-
amples, such as the treatment of post-natal depression, schizophrenia or drug and alcohol
dependencies, are well suited to motivate the reader to learn the software capabilities for
various statistical analysis of intermediate and advanced level. However, given the wide range
of topics covered in a rather few number of pages, understandably neither book provides
a comprehensive introduction to either the statistical procedure or the statistical package.
Nevertheless there are excellent hints, literature references, and software examples in either
handbook to move forward on your own.
From a teacher’s perspective, neither one of the books would be an obvious choice for the
sole textbook for a particular class. However, both books have the potential to be used in
lab sessions for graduate level courses. In a data analysis course, it would be recommended4 A Handbook of Statistical Analyses using SPSS/Stata
that instructors elaborate further in places where the authors were understandably brief in
their descriptions of statistical techniques. In a course whose focus is to teach either of these
programming languages, it is recommended that an instructor supplement lessons with syntax
statements (Landau/Everitt) or provides further explanations of the code in more detail, when
necessary (Rabe-Hesketh/Everitt).
Reviewer:
Vanessa C. Beddo and Frauke Kreuter
University of California at Los Angeles
Department of Statistics
8130 Math Science Building
Los Angeles, CA 90095-1554, USA
E-mail: beddo@stat.ucla.edu, fkreuter@stat.ucla.edu
Journal of Statistical Software Published: 2004-06-03
June 2004, Volume 11, Book Review 2/3.
http://www.jstatsoft.org/

A Handbook of Statistical Analyses using SPSS

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