Table Of ContentPERFORMING DATA ANALYSIS
USING IBM SPSS
PERFORMING DATA ANALYSIS
USING IBM SPSS
LAWRENCE S. MEYERS
Department of Psychology
California State University, Sacramento
Sacramento, California
GLENN C. GAMST
Department of Psychology
University of La Verne
La Verne, California
A. J. GUARINO
Department of Biostatistics
MGH Institute of Health Professions
Boston, Massachusetts
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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 sciences–Statistical methods–Computer
programs. 2. SPSS (Computer file) I. Title.
HA32.M4994 2013
005.5’5–dc23
2013002844
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
CONTENTS
PREFACE ix
PART 1 | GETTING STARTED WITH IBM SPSS 1
CHAPTER 1 | INTRODUCTION TO IBM SPSS 3
CHAPTER 2 | ENTERING DATA IN IBM SPSS 5
CHAPTER 3 | IMPORTING DATA FROM EXCEL TO IBM SPSS 15
PART 2 | OBTAINING, EDITING, AND SAVING STATISTICAL
OUTPUT 19
CHAPTER 4 | PERFORMING STATISTICAL PROCEDURES
IN IBM SPSS 21
CHAPTER 5 | EDITING OUTPUT 27
CHAPTER 6 | SAVING AND COPYING OUTPUT 31
PART 3 | MANIPULATING DATA 37
CHAPTER 7 | SORTING AND SELECTING CASES 39
CHAPTER 8 | SPLITTING DATA FILES 45
CHAPTER 9 | MERGING DATA FROM SEPARATE FILES 51
PART 4 | DESCRIPTIVE STATISTICS PROCEDURES 57
CHAPTER 10 | FREQUENCIES 59
CHAPTER 11 | DESCRIPTIVES 67
CHAPTER 12 | EXPLORE 71
PART 5 | SIMPLE DATA TRANSFORMATIONS 77
CHAPTER 13 | STANDARDIZING VARIABLES TO Z SCORES 79
CHAPTER 14 | RECODING VARIABLES 83
CHAPTER 15 | VISUAL BINNING 97
v
vi CONTENTS
CHAPTER 16 | COMPUTING NEW VARIABLES 103
CHAPTER 17 | TRANSFORMING DATES TO AGE 111
PART 6 | EVALUATING SCORE DISTRIBUTION
ASSUMPTIONS 121
CHAPTER 18 | DETECTING UNIVARIATE OUTLIERS 123
CHAPTER 19 | DETECTING MULTIVARIATE OUTLIERS 131
CHAPTER 20 | ASSESSING DISTRIBUTION SHAPE: NORMALITY,
SKEWNESS, AND KURTOSIS 139
CHAPTER 21 | TRANSFORMING DATA TO REMEDY STATISTICAL
ASSUMPTION VIOLATIONS 147
PART 7 | BIVARIATE CORRELATION 157
CHAPTER 22 | PEARSON CORRELATION 159
CHAPTER 23 | SPEARMAN RHO AND KENDALL TAU-B RANK-ORDER
CORRELATIONS 165
PART 8 | REGRESSING (PREDICTING) QUANTITATIVE
VARIABLES 171
CHAPTER 24 | SIMPLE LINEAR REGRESSION 173
CHAPTER 25 | CENTERING THE PREDICTOR VARIABLE IN SIMPLE
LINEAR REGRESSION 181
CHAPTER 26 | MULTIPLE LINEAR REGRESSION 191
CHAPTER 27 | HIERARCHICAL LINEAR REGRESSION 211
CHAPTER 28 | POLYNOMIAL REGRESSION 217
CHAPTER 29 | MULTILEVEL MODELING 225
PART 9 | REGRESSING (PREDICTING) CATEGORICAL
VARIABLES 253
CHAPTER 30 | BINARY LOGISTIC REGRESSION 255
CHAPTER 31 | ROC ANALYSIS 265
CHAPTER 32 | MULTINOMINAL LOGISTIC REGRESSION 273
PART 10 | SURVIVAL ANALYSIS 281
CHAPTER 33 | SURVIVAL ANALYSIS: LIFE TABLES 283
CHAPTER 34 | THE KAPLAN–MEIER SURVIVAL ANALYSIS 289
CHAPTER 35 | COX REGRESSION 301
CONTENTS vii
PART 11 | RELIABILITY AS A GAUGE OF MEASUREMENT
QUALITY 309
CHAPTER 36 | RELIABILITY ANALYSIS: INTERNAL CONSISTENCY 311
CHAPTER 37 | RELIABILITY ANALYSIS: ASSESSING RATER CONSISTENCY 319
PART 12 | ANALYSIS OF STRUCTURE 329
CHAPTER 38 | PRINCIPAL COMPONENTS AND FACTOR ANALYSIS 331
CHAPTER 39 | CONFIRMATORY FACTOR ANALYSIS 353
PART 13 | EVALUATING CAUSAL (PREDICTIVE) MODELS 379
CHAPTER 40 | SIMPLE MEDIATION 381
CHAPTER 41 | PATH ANALYSIS USING MULTIPLE REGRESSION 389
CHAPTER 42 | PATH ANALYSIS USING STRUCTURAL EQUATION
MODELING 397
CHAPTER 43 | STRUCTURAL EQUATION MODELING 419
PART 14 | t TEST 457
CHAPTER 44 | ONE-SAMPLE t TEST 459
CHAPTER 45 | INDEPENDENT-SAMPLES t TEST 463
CHAPTER 46 | PAIRED-SAMPLES t TEST 471
PART 15 | UNIVARIATE GROUP DIFFERENCES: ANOVA AND
ANCOVA 475
CHAPTER 47 | ONE-WAY BETWEEN-SUBJECTS ANOVA 477
CHAPTER 48 | POLYNOMIAL TREND ANALYSIS 485
CHAPTER 49 | ONE-WAY BETWEEN-SUBJECTS ANCOVA 493
CHAPTER 50 | TWO-WAY BETWEEN-SUBJECTS ANOVA 507
CHAPTER 51 | ONE-WAY WITHIN-SUBJECTS ANOVA 521
CHAPTER 52 | REPEATED MEASURES USING LINEAR MIXED MODELS 531
CHAPTER 53 | TWO-WAY MIXED ANOVA 555
PART 16 | MULTIVARIATE GROUP DIFFERENCES: MANOVA
AND DISCRIMINANT FUNCTION ANALYSIS 567
CHAPTER 54 | ONE-WAY BETWEEN-SUBJECTS MANOVA 569
CHAPTER 55 | DISCRIMINANT FUNCTION ANALYSIS 579
CHAPTER 56 | TWO-WAY BETWEEN-SUBJECTS MANOVA 591
viii CONTENTS
PART 17 | MULTIDIMENSIONAL SCALING 603
CHAPTER 57 | MULTIDIMENSIONAL SCALING: CLASSICAL METRIC 605
CHAPTER 58 | MULTIDIMENSIONAL SCALING: METRIC WEIGHTED 613
PART 18 | CLUSTER ANALYSIS 621
CHAPTER 59 | HIERARCHICAL CLUSTER ANALYSIS 623
CHAPTER 60 | K-MEANS CLUSTER ANALYSIS 631
PART 19 | NONPARAMETRIC PROCEDURES FOR
ANALYZING FREQUENCY DATA 643
CHAPTER 61 | SINGLE-SAMPLE BINOMIAL AND CHI-SQUARE TESTS:
BINARY CATEGORIES 645
CHAPTER 62 | SINGLE-SAMPLE (ONE-WAY) MULTINOMINAL
CHI-SQUARE TESTS 655
CHAPTER 63 | TWO-WAY CHI-SQUARE TEST OF INDEPENDENCE 665
CHAPTER 64 | RISK ANALYSIS 675
CHAPTER 65 | CHI-SQUARE LAYERS 681
CHAPTER 66 | HIERARCHICAL LOGLINEAR ANALYSIS 689
APPENDIX | STATISTICS TABLES 699
REFERENCES 703
AUTHOR INDEX 713
SUBJECT INDEX 715
PREFACE
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 follow-
ing:
• Overview: This is a brief conceptual introduction that furnishes a set of rele-
vant 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 ques-
tion, 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 screen-
shots 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 com-
plexity 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 appro-
priate topics for first- or second-level statistics and data analysis courses. The remain-
ing 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 anal-
ysis, 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
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