Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods

522 Pages·2016·2.72 MB·English

by  W. Paul Vogt and Elaine R. Vogt

ebook
THE GUILFORD PRESS
Selecting the Right AnAlySeS foR youR DAtA
Also Available
When to Use What Research Design
W. Paul Vogt, Dianne C. Gardner,
and Lynne M. Haeffele
Selecting
the Right Analyses
for your Data
Quantitative, Qualitative,
and Mixed Methods
W. Paul Vogt
Elaine R. Vogt
Dianne C. Gardner
Lynne M. Haeffele
THE GUILFORD PRESS
New York London
© 2014 The Guilford Press
A Division of Guilford Publications, Inc.
72 Spring Street, New York, NY 10012
www.guilford.com
All rights reserved
No part of this book may be reproduced, translated, stored in a retrieval system, or transmitted,
in any form or by any means, electronic, mechanical, photocopying, microfilming, recording,
or otherwise, without written permission from the publisher.
Printed in the United States of America
This book is printed on acid-free paper.
Last digit is print number: 9 8 7 6 5 4 3 2 1
Library of Congress Cataloging-in-Publication Data is available from the publisher.
ISBN: 978-1-4625-1576-9 (paperback)
ISBN: 978-1-4625-1602-5 (hardcover)
Preface and Acknowledgments
Using the right analysis methods leads to more justifiable conclusions and more per-
suasive interpretations of your data. Several plausible coding and analysis options exist
for any set of data—qualitative, quantitative, or graphic/visual. Helping readers select
among those options is our goal in this book. Because the range of choices is broad,
so too is the range of topics we have addressed. In addition to the standard division
between quantitative and qualitative coding methods and analyses, discussed in spe-
cific chapters and sections, we have dealt with graphic data and analyses throughout
the book. We have also addressed in virtually every chapter the issues involved in com-
bining qualitative, quantitative, and graphic data and techniques in mixed methods
approaches. We intentionally cover a very large number of topics and consider this
a strength of the book; it enables readers to consider a broad range of options in one
place.
Analysis choices are usually tied to prior design and sampling decisions. This means
that Selecting the Right Analyses for Your Data is naturally tied to topics addressed in
our companion volume, When to Use What Research Design, published in 2012. In that
book we introduced guidelines for starting along the intricate paths of choices research-
ers face as they wend their way through a research project. Completing the steps of a
research project—from the initial idea through formulating a research question, choos-
ing methods of data collection, and identifying populations and sampling methods to
deciding how to code, analyze, and interpret the data thus collected—is an arduous
process, but few jobs are as rewarding.
We think of the topic—from the research question to the interpretation of evi-
dence—as a unified whole. We have dealt with it in two books, rather than in one huge
volume, mostly for logistical reasons. The two books are free standing. As in a good
marriage, they are distinct but happier as a pair. It has been exciting to bring to frui-
tion the two-volume project, and we hope that you too will find it useful and occasion-
ally provocative as you select effective methods to collect, code, analyze, and interpret
your data.
v
vi Preface and Acknowledgments
To assist you with the selection process, the book uses several organizing tech-
niques to help orient readers, which are often called pedagogical features:
• Opening chapter previews provide readers with a quick way to find the useful
(and often unexpected) topic nuggets in each chapter.
• End-of-chapter Summary Tables recap the dos and don’ts and the advantages and
disadvantages of the various analytic techniques.
• End-of-chapter Suggestions for Further Reading are provided that include detailed
summaries of what readers can find in each one and why they might want to read
them for greater depth or more technical information.
• Chapter 14 concludes the book with aphorisms containing advice on different
themes.
It is a great pleasure to acknowledge the help we have received along the way. This
book would not have been written without the constant support and advice—from the
early planning to the final copyediting—of C. Deborah Laughton, Publisher, Meth-
odology and Statistics, at The Guilford Press. She also recruited a wonderful group of
external reviewers for the manuscript. Their suggestions for improving the book were
exceptionally helpful. These external reviewers were initially anonymous, of course,
but now we can thank at least some of them by name: Theresa E. DiDonato, Depart-
ment of Psychology, Loyola University, Baltimore, Maryland; Marji Erickson Warfield,
The Heller School for Social Policy and Management, Brandeis University, Waltham,
Massachusetts; Janet Salmons, Department of Business, School of Business and Tech-
nology, Capella University, Minneapolis, Minnesota; Ryan Spohn, School of Crimi-
nology and Criminal Justice, University of Nebraska at Omaha, Omaha, Nebraska;
Jerrell C. Cassady, Department of Educational Psychology, Ball State University, Mun-
cie, I ndiana; and Tracey LaPierre, Department of Sociology, University of Kansas, Law-
rence, Kansas.
The editorial and production staff at The Guilford Press, especially Anna Nelson,
have been wonderful to work with. They have been efficient, professional, and friendly
as they turned our rough typescript into a polished work.
This book and its companion volume, When to Use What Research Design, were
written with colleagues and students in mind. These groups helped in ways too numer-
ous to recount, both directly and indirectly. Many of the chapters were field tested in
classes on research design and in several courses on data analysis for graduate students
at Illinois State University. We are especially grateful to students with whom we worked
on dissertation committees as well as in classes. They inspired us to write in ways that
are directly useful for the practice of research.
We have also had opportunities to learn about research practice from working on
several sponsored research projects funded by the U.S. Department of Education, the
National Science Foundation, and the Lumina Foundation. Also important has been
the extensive program evaluation work we have done under the auspices of the Illinois
Board of Higher Education (mostly funded by the U.S. Department of Education).
Although we had help from these sources, it remains true, of course, that we alone
are responsible for the book’s shortcomings.
Abbreviations Used in This Book
The following is a list of abbreviations used in this book. If a term and its abbreviation
are used only once, they are defined where they are used.
ACS American Community Survey A
b
AIK Akaike information criterion b
r
ANCOVA analysis of covariance
e
v
ANOVA analysis of variance
i
A
AUC area under the curve t
i
o
BMI body mass index
n
CAQDAS computer- assisted qualitative data analysis software s
CART classification and regression trees
CDC Centers for Disease Control and Prevention
CFA confirmatory (or common) factor analysis
CI confidence interval
COMPASSS comparative methods for systematic cross-case analysis
CPS Current Population Survey
CRA correlation and regression analysis
CSND cumulative standard normal distribution
DA discriminant analysis
d-i-d difference- in- difference
DIF differential item functioning
DOI digital object identifier
vii
DV dependent variable
E estimate or error or error terms
EDA exploratory data analysis
EFA exploratory factor analysis
ELL English language learner
ES effect size
ESCI effect- size confidence interval
FA factor analysis
GDP gross domestic product
GIS geographic information systems
GLM general (and generalized) linear model
GPA grade point average
GRE Graduate Record Examination
GSS general social survey
s
n GT grounded theory
o
i HLM hierarchical linear modeling
t
A HSD honestly significant difference
i
v
ICC intraclass correlation
e
r
ICPSR Inter- University Consortium for Political and Social Research
b
b
IPEDS integrated postsecondary education data system
A
IQ intelligence quotient
IQR interquartile range
IRB institutional review board
IRT item response theory
I-T information-theoretic analysis
IV independent variable
IVE instrumental variable estimation
JOB Job Outreach Bureau
LGCM latent growth curve modeling
LOVE left-out variable error
LR logit (or logistic) regression
LS least squares
viii