Cohen - Explaining Psychological Statistics

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Library of Congress Cataloging-in-Publication Data:

Cohen, Barry H.

Explaining psychological statistics / Barry H. Cohen, New York University. Fourth Edition.

pages cm..(Coursesmart)

Includes bibliographical references and index.

ISBN 978-1-118-43660-8 (hardback : alk. paper)

ISBN 978-1-118-25950-4 (ebk.)

ISBN 978-1-118-23485-3 (ebk.)

ISBN 978-1-118-22110-5 (ebk.)

1. Psychometrics. 2. Psychology.Mathematical models. 3. StatisticsStudy and teaching

(Higher). I. Title.

BF39.C56 2013

150.15195dc23

2013028064

For Leona

Preface to the Fourth Edition

This edition marks the first time that I have included detailed instructions for the use of IBM SPSS Statistics (SPSS, for short) in the text itself, and not merely in supplemental material on the web. Not every instructor wants to teach SPSS as part of his or her statistics course, but such a large proportion of my adopters, and would-be adopters, do incorporate SPSS instruction in their courses that I felt it would greatly enhance the usefulness of my text to add a section on SPSS to every chapter. To keep the text down to a manageable size, I had to modify the ABC section format that I have used since the first edition of this text, as described next.

As in previous editions, Section A of each chapter provides the Conceptual Foundation for the topics covered in that chapter. In Section A, I focus on the simplest case of the procedure dealt with in that chapter (e.g., one-way ANOVA with equal-sized groups), and explain the definitional formulas thoroughly, so that students can gain some insight into why and how statistical formulas work the way they do. The emphasis is on the underlying similarity of formulas that look very different (e.g., it is shown that, in the two-group case, the MSW of a one-way ANOVA is exactly the same as the pooled-variance estimate in a two-group t test). In my experience, students learn statistics more easily when statistical formulas are not presented as arbitrary strings of characters to be memorized, or even just looked up when needed, but rather when the structures of the formulas are made clear (e.g., the sample size usually appears in the denominator of the denominator of the formula for the one-sample t test, which means that it is effectively in the numeratorso, making the sample size larger, with all else remaining the same, will increase the size of the t value). Some instructors may prefer an approach in which concepts are explained first without reference to statistical formulas at all. I don't feel I could do that well, so I have not attempted that approach in this text. However, I believe that all of the formulas in this text can be made understandable to the average graduate (or above-average undergraduate) student.

Section A has its own detailed summary, followed by exercises that help ensure that students grasp the basic concepts and definitional formulas before moving on to the complications of Section B. Section B, Basic Statistical Procedures, presents the more general case of that chapter's procedure and includes computational formulas, significance tests, and comments on research design so that students will be equipped to analyze real data and interpret their results. In addition to the basics of null hypothesis testing, Section B also includes supplementary statistical procedures (e.g., confidence intervals, effect sizes), and information on how to report such statistical results in the latest APA format, usually illustrated with an excerpt from a published journal article. Section B ends with a thorough summary and a variety of exercises so that students can practice the basic computations. Moreover, these exercises often refer to exercises in Section A of that chapter, or exercises from previous chapters, to make instructive comparisons (e.g., that a one-way RM ANOVA can be calculated on the same data that had been used to illustrate the computation of a matched t test).

In previous editions, Section C presented Optional Material that was usually more conceptually advanced and less central to students' needs than the topics covered in Sections A and B. In this edition, the former Section C material that was most relevant to the chapter has been incorporated in Section B, or in some cases in a separate section labeled Advanced Material, which does not appear in all chapters. The more specialized material from the previous C sections will be included in new supplements that I am preparing for each chapter, which will eventually be made available on the web. The new C Sections explain how to use SPSS to perform the statistical procedures in the B sections they follow. I have included some little-known, but useful, options that are available only by using a Syntax window (e.g., obtaining simple main effects from the two-way ANOVA procedure). I have also included explanations of SPSS's most important data management tools (e.g., Split File, Recode), spread across several C sections and illustrated in terms of the procedures of the chapter in which each is introduced.