Making Sense of Multivariate Data Analysis: An Intuitive Approach
Autor John Spiceren Limba Engleză Electronic book text – 30 oct 2004
'This book serves as a resource for readers who want to have an overall view of what encompasses multivariate analyses. The author has discussed some important issues rather philosophically (e.g., theory vs. data analysis). These points are valuable even for readers who have extensive training with multivariate analyses'—Jenn-Yun Tein, Arizona State University
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Specificații
ISBN-13: 9781452211596
ISBN-10: 1452211590
Pagini: 256
Dimensiuni: 152 x 229 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
ISBN-10: 1452211590
Pagini: 256
Dimensiuni: 152 x 229 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
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Recenzii
“This
book
serves
as
a
resource
for
readers
who
want
to
have
an
overall
view
of
what
encompasses
multivariate
analyses.
The
author
has
discussed
some
important
issues
rather
philosophically
(e.g.,
theory
vs.
data
analysis).
These
points
are
valuable
even
for
readers
who
have
extensive
training
with
multivariate
analyses.”
“This book is a helpful guide to reading and understanding multivariate data analysis results in social and psychological research.”
"Spicer's book is a superb overview of multivariate statistics, but without formulas. Even though he is trying to offer a nontechnical overview of multivariate analyses, he doesn't shortchange the reader in any way. As much as you might know about stat, you'll learn some more here."
“This book is a helpful guide to reading and understanding multivariate data analysis results in social and psychological research.”
"Spicer's book is a superb overview of multivariate statistics, but without formulas. Even though he is trying to offer a nontechnical overview of multivariate analyses, he doesn't shortchange the reader in any way. As much as you might know about stat, you'll learn some more here."
Cuprins
Preface
Part I. The Core Ideas
1. What Makes a Difference?
1. 1 Analyzing Data in the Form of Scores
1.2 Analyzing Data in the Form of Categories
1.3 Further Reading
2. Deciding Whether Differences Are Trustworthy
2.1 Sampling Issues
2.2 Measurement Issues
2.3 The Role of Chance
2.4 Statistical Assumptions
2.5 Further Reading
3. Accounting for Differences in a Complex World
3.1 Limitations of Bivariate Analysis
3.2 The Multivariate Strategy
3.3 Common Misinterpretations of Multivariate Analyses
3.4 Further Reading
Part II. The Techniques
4. Multiple Regression
4.1 The Composite Variable in Multiple Regression
4.2 Standard Multiple Regression in Action
4.3 Trustworthiness in Regression Analysis
4.4 Accommodating Other Types of Independent Variables
4.5 Sequential Regression Analysis
4.6 Further Reading
5. Logistic Regression and Discriminant Analysis
5.1 Logistic Regression
5.2 Discriminant Analysis
5.3 Further Reading
6. Multivariate Analysis of Variance
6.1 One-Way Analysis of Variance
6.2 Factorial Analysis of Variance
6.3 Multivariate Analysis of Variance
6.4 Within-Subjects ANOVA and MANOVA
6.5 Issues of Trustworthiness in MANOVA
6.6 Analysis of Covariance
6.7 Further Reading
7. Factor Analysis
7.1 The Composite Variable in Factor Analysis
7.2 Factor Analysis in Action
7.3 Issues of Trustworthiness in Factor Analysis
7.4 Confirmatory Factor Analysis
7.5 Further Reading
8. Log-Linear Analysis
8.1 Hierarchical Log-Linear Analysis
8.2 Trustworthiness in Log-Linear Analysis
8.3 Log-Linear Analysis With a Dependent Variable: Logit Analysis
8.4 Further Reading
Bibliography
Index
About the Author
Part I. The Core Ideas
1. What Makes a Difference?
1. 1 Analyzing Data in the Form of Scores
1.2 Analyzing Data in the Form of Categories
1.3 Further Reading
2. Deciding Whether Differences Are Trustworthy
2.1 Sampling Issues
2.2 Measurement Issues
2.3 The Role of Chance
2.4 Statistical Assumptions
2.5 Further Reading
3. Accounting for Differences in a Complex World
3.1 Limitations of Bivariate Analysis
3.2 The Multivariate Strategy
3.3 Common Misinterpretations of Multivariate Analyses
3.4 Further Reading
Part II. The Techniques
4. Multiple Regression
4.1 The Composite Variable in Multiple Regression
4.2 Standard Multiple Regression in Action
4.3 Trustworthiness in Regression Analysis
4.4 Accommodating Other Types of Independent Variables
4.5 Sequential Regression Analysis
4.6 Further Reading
5. Logistic Regression and Discriminant Analysis
5.1 Logistic Regression
5.2 Discriminant Analysis
5.3 Further Reading
6. Multivariate Analysis of Variance
6.1 One-Way Analysis of Variance
6.2 Factorial Analysis of Variance
6.3 Multivariate Analysis of Variance
6.4 Within-Subjects ANOVA and MANOVA
6.5 Issues of Trustworthiness in MANOVA
6.6 Analysis of Covariance
6.7 Further Reading
7. Factor Analysis
7.1 The Composite Variable in Factor Analysis
7.2 Factor Analysis in Action
7.3 Issues of Trustworthiness in Factor Analysis
7.4 Confirmatory Factor Analysis
7.5 Further Reading
8. Log-Linear Analysis
8.1 Hierarchical Log-Linear Analysis
8.2 Trustworthiness in Log-Linear Analysis
8.3 Log-Linear Analysis With a Dependent Variable: Logit Analysis
8.4 Further Reading
Bibliography
Index
About the Author
Descriere
Making
Sense
of
Multivariate
Data
Analysisis
a
short
introduction
to
multivariate
data
analysis
(MDA)
for
students
and
practitioners
in
the
behavioral
and
social
sciences.
It
provides
a
conceptual
overview
of
the
foundations
of
MDA
and
of
a
range
of
specific
techniques
including
multiple
regression,
logistic
regression,
discriminant
analysis,
multivariate
analysis
of
variance,
factor
analysis,
and
log-linear
analysis.
As
a
conceptual
introduction,
the
book
assumes
no
prior
statistical
knowledge,
and
contains
very
few
symbols
or
equations.
Its
primary
objective
is
to
expose
the
conceptual
unity
of
MDA
techniques
both
in
their
foundations
and
in
the
common
analytic
strategies
that
lie
at
the
heart
of
all
of
the
techniques.
Although
introductory,
the
book
encourages
the
reader
to
reflect
critically
on
the
general
strengths
and
limitations
of
MDA
techniques.
Each
chapter
includes
references
for
further
reading
accessible
to
the
beginner.
Notă biografică
John Spicer was an Associate Professor and Head of Psychology at Massey University, New Zealand until the end of 2002, when he took early retirement to devote all of his time to writing books. Earlier, he was a Research Fellow for several years at the University of Auckland, New Zealand, and held Visiting Fellowships at the Universities of Michigan and London. His primary research interests have been in health psychology, and he has published articles mainly on cardiovascular disease and theoretical issues in a variety of international journals. He was coeditor of Social Dimensions of Health and Disease: New Zealand Perspectives (1994). Most of his undergraduate and graduate teaching has focused on research methods, particularly multivariate data analysis. In 2002 he coauthored a chapter on sociological and psychological methods in the fourth edition of the Oxford Textbook of Public Health.