Lawrence S. Meyers - Performing Data Analysis Using IBM SPSS

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Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Meyers, Lawrence S.

Performing data analysis using IBM SPSS / Lawrence S. Meyers,

Department of Psychology, California State University, Sacramento,

Sacramento, CA, Glenn C. Gamst, Department of Psychology, University of La

Verne, La Verne, CA, A. J. Guarino, Department of Biostatistics, MGH

Institute of Health Professions, Boston, MA.

pages cm

Includes bibliographical references and index.

ISBN 978-1-118-35701-9 (pbk.) ISBN 978-1-118-51494-8 ISBN

978-1-118-51492-4 (ePDF) ISBN 978-1-118-51493-1 (ePub) ISBN

978-1-118-51490-0 1. Social sciencesStatistical methodsComputer

programs. 2. SPSS (Computer file) I. Title.

HA32.M4994 2013

005.5'5dc23

2013002844

The IBM SPSS software package is one of the most widely used statistical applications in academia, business, and government. This book, Performing Data Analysis Using IBM SPSS, provides readers with both a gentle introduction to basic statistical computation with the IBM SPSS software package and a portal to the more comprehensive and statistically robust multivariate procedures. This book was written to be a stand-alone resource as well as a supplementary text for both undergraduate introductory and more advanced graduate-level statistics courses.

For most of the chapters, we provide a consistent structure that includes the following:

• Overview:This is a brief conceptual introduction that furnishes a set of relevant details for each statistical procedure being covered, including a few useful references that supply additional background information.
• Numerical Example:This includes a description of the research problem or question, the name of the data file, a description of the variables and how they are coded, and (often) a screenshot of the IBM SPSS Data View.
• Analysis Strategy:When the analysis is performed in stages, or when alternative data processing strategies are available, we include a description of how we have structured our data analysis and explain the rationale for why we have performed the analyses in the way presented in the chapter.
• Analysis Setup:This includes how to configure each dialog window with screenshots and is accompanied (within reason) with explanations for why we chose the particular options we utilized.
• Analysis Output:This elucidates the major aspects of the statistical output with pertinent screenshots and discussion.

Because of the multiple audience we are attempting to reach with this book, the complexity of the procedures covered varies substantially across the chapters. For example, chapters that cover IBM SPSS basics of data entry and file manipulation, descriptive statistical procedures, correlation, simple linear regression, multiple regression, one-way chi-square, t tests, and one and two-way analysis of variance designs are all appropriate topics for first- or second-level statistics and data analysis courses. The remaining chapters, data transformations, assumption violation assessment, reliability analysis, logistic regression, multivariate analysis of variance, survival analysis, multidimensional scaling, cluster analysis, multilevel modeling, exploratory and confirmatory factor analysis, and structural equation modeling, are all important topics that may be suitable for more advanced statistics courses.

There are 66 chapters in this book. They are organized into 19 sections or Parts. Different authors might organize the chapters in somewhat different ways and present them in a somewhat different order, as there is no fully agreed upon organizational structure for this material. However, except for the chapters presented in the early parts that show readers how to work with IBM SPSS data files, most of the data analysis chapters can be used as a resource on their own, allowing users to work with whatever analysis procedures meet their needs; the order in which users would choose to work with the chapters is really a function of the foundations on which the material is based (e.g., users should undertake structural equation modeling only after acquiring some familiarity with regression techniques and factor analysis).

Part 1, Getting Started With IBM SPSS, consists of three chapters that provide the basics of IBM SPSS. Chapter 1 provides an introduction to IBM SPSS, Chapter 2 describes how to enter data, and Chapter 3 demonstrates how to import data from Excel to IBM SPSS.

Part 2, Obtaining, Editing, and Saving Statistical Output, consists of three chapters that describe ways of manipulating the IBM SPSS statistical output. Chapter 4 conveys how to perform a statistical procedure. Chapter 5 demonstrates how to edit statistical output. Chapter 6 provides information on saving and copying output.

Part 3, Manipulating Data, contains three chapters that focus on how to organize existing data. Chapter 7 examines the sorting and selecting of cases. Chapter 8 demonstrates how to split a data file. Chapter 9 discusses how to merge cases and variables.

Part 4, Descriptive Statistics Procedures, consists of three chapters that provide descriptive statistical summary capabilities. Chapter 10 focuses on the analysis of frequency counts for categorical variables. Chapter 11 describes how to compute measures of central tendency and variability. Chapter 12 provides additional options to examine variables in the data file.