Hypothesis Testing and Model Selection in the Social Sciences

218 Pages·2016·3.05 MB·English

by  David L. Weakliem PhD

ebook

THE GUILFORD PRESS
Hypothesis Testing and Model Selection

in the Social Sciences
Methodology in the Social Sciences

David A. Kenny, Founding Editor

Todd D. Little, Series Editor
www.guilford.com/MSS

This series provides applied researchers and students with analysis and research design
books that emphasize the use of methods to answer research questions. Rather than

emphasizing statistical theory, each volume in the series illustrates when a technique
should (and should not) be used and how the output from available software programs

should (and should not) be interpreted. Common pitfalls as well as areas of further
development are clearly articulated.

RECENT VOLUMES

DATA ANALYSIS WITH Mplus
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TO DIARY AND ExpERIENCE SAMpLING RESEARCH
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LONGITUDINAL STRUCTURAL EQUATION MODELING
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pROCESS ANALYSIS: A REGRESSION-BASED AppROACH

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BAYESIAN STATISTICS FOR THE SOCIAL SCIENCES

David Kaplan

CONFIRMATORY FACTOR ANALYSIS FOR AppLIED RESEARCH,

SECOND EDITION
Timothy A. Brown

pRINCIpLES AND pRACTICE OF STRUCTURAL EQUATION MODELING,
FOURTH EDITION

Rex B. Kline

HYpOTHESIS TESTING AND MODEL SELECTION IN THE SOCIAL SCIENCES

David L. Weakliem
Hypothesis Testing and Model

Selection in the Social Sciences

David L. Weakliem

Series Editor’s Note by Todd D. Little

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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

Names: Weakliem, David L., author.
Title: Hypothesis testing and model selection in the social sciences / David
L. Weakliem.

Description: New York : Guilford Press, [2016] | Series: Methodology in the
social sciences | Includes bibliographical references and index.
Identifiers: LCCN 2015043221 | ISBN 9781462525652 (hardcover)
Subjects: LCSH: Social sciences—Statistical methods. | Statistical
hypothesis testing. | Social sciences—Methodology.
Classification: LCC HA29 .W395 2016 | DDC 300.72/7—dc23
LC record available at http://lccn.loc.gov/2015043221
In memory of
Herbert A. Weakliem (1926–2015)

Series Editor’s Note

Why do so many false dichotomies exist, I wonder? Frequentist versus Bayes-
ian seems to be one. Granted, the two camps have very different histori-

cal roots and some purveyors of either camp have strong agendas. David L.

Weakliem provides us with an amazing interplay of depth and balanced com-

parison between these two major traditions. His vast experience is clearly

evident as he walks us through meaningful examples without creating a
sense of right versus wrong; rather he instills the simple yet important value

that the approach to be used is that which serves the analytical goals at hand.
By grounding the statistical theory in its philosophical origins, Weakliem
teaches us one of the most important things we all should learn about sta-
tistics: how to think, instead of what to think. Weakliem carefully provides
arguments that highlight the utility of both frequentist traditions and Bayes-
ian practices, and without advocating for either the status quo or radical rev-

olution, crafts a guide to how traditional practices can be wedded to Bayesian

ideas to improve scientific discovery.

Weakliem also underscores another important feature of being a
thoughtful researcher, namely, model selection. In the world of confirma-

tory modeling, there appears to be a lack of appreciation for the importance

of testing competing models and determining which to select from a set of
possible models. Selecting the appropriate model is a critical issue of social

justice, since the model selected is the one that will inform both policy and
vii
viii Series Editor’s Note

practice. Weakliem is a strong advocate of principled and thoughtful applica-

tion of all our modeling techniques.
Wealkiem is like a master chef who explains the relations among all
the elements of cooking without providing you with a single recipe. You are
given all the ingredients and the principles of combination that allow you to
create your own recipe—one that is the perfect combination for the research
meal you desire to create. He shows us how to create such masterful works by

walking us through real research questions with real data.

One final feature of this book that I love is the wonderful set of bibliog-
raphies that Weakliem has assembled at the end of each chapter. This book
is bound to be a mainstay reference guide to model selection, specification,
estimation, and evaluation, and the bibliographies are rich fodder to feed
the intellectual hunger that he elicits in each chapter. They are a qualifying-
exam’s worth of influential readings that graduate students should take full
advantage of. And, of course, put Weakliem’s book on your exam’s reading

list, because it really will be your roadmap going forward.
As always, “enjoy!” Oh, and “make good choices.”

Todd d. LiTTLe

Snowbound in Boise City, Oklahoma