Performing Data Analysis Using IBM SPSS

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS ® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user's guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output Descriptive statistics procedures Score distribution assumption evaluations Bivariate correlation Regressing (predicting) quantitative and categorical variables Survival analysis t Test ANOVA and ANCOVA Multivariate group differences Multidimensional scaling Cluster analysis Nonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.

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 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 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 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

LAWRENCE S. MEYERS, PhD, is Professor in the Depart-ment of Psychology at California State University, Sacramento. The author of numerous books, Dr. Meyers is a member of the Association for Psychological Science and the Society for Industrial and Organiza-tional Psychology. GLENN C. GAMST, PhD, is Chair and Professor in the Department of Psychology at the University of La Verne. His research interests include univariate and multivariate statistics as well as multicultural community mental health outcome research. A. J. Guarino, PhD, is Professor of Biostatistics at Massachusetts General Hospital, Institute of Health Professions, where he serves as the methodologist for capstones and dissertations as well as teaching advanced Biostatistics courses. Dr. Guarino is also the statistician on numerous National Institutes of Health grants and coauthor of several statistical textbooks.