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.