Performing Data Analysis Using IBM SPSS
John Wiley & Sons Inc (Verlag)
978-1-118-35701-9 (ISBN)
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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.
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.
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
| Verlagsort | New York |
|---|---|
| Sprache | englisch |
| Maße | 218 x 281 mm |
| Gewicht | 1683 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| ISBN-10 | 1-118-35701-9 / 1118357019 |
| ISBN-13 | 978-1-118-35701-9 / 9781118357019 |
| Zustand | Neuware |
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