This book is designed to be a user's guide for students and other interested readers to perform statistical data analysis with IBM SPSS, which is a major statistical software package used extensively in academic, government and business settings. IBM SPSS has a user-friendly point-and-click interface and a robust selection of statistical and data analytic procedures. This book addresses the needs, level of sophistication and interest in introductory statistical methodology on the part of undergraduate students as well as more advanced students in social and behavioral science, business, health-related and education programs. Each chapter covers a particular statistical procedure and has the following format: 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.

Preface



Part 1 Getting Started with IBM SPSS®

Chapter 1 Introduction to IBM SPSS®

Chapter 2 Entering Data in IBM SPSS®

Chapter 3 Importing Data from Excel to IBM SPSS®



Part 2 Obtaining, Editing and Saving Statistical Output

Chapter 4 Performing Statistical Procedures in IBM SPSS®

Chapter 5 Editing Output

Chapter 6 Saving and Copying Output



Part 3 Manipulating Data

Chapter 7 Sorting and Selecting Cases

Chapter 8 Splitting Data Files

Chapter 9 Merging Data from Separate Files



Part 4 Descriptive Statistics Procedures

Chapter 10 Frequencies

Chapter 11 Descriptive

Chapter 12 Explore



Part 5 Simple Data Transformations

Chapter 13 Standardizing Variables to Z Scores

Chapter 14 Recoding Variables

Chapter 15 Visual Binning

Chapter 16 Computing New Variables

Chapter 17 Transforming Dates to Age



Part 6 Evaluating Score Distribution Assumptions

Chapter 18 Detecting Univariate Outliers

Chapter 19 Detecting Multivariate Outliers

Chapter 20 Assessing Distribution Shape: Normality, Skewness and Kurtosis

Chapter 21 Transforming Data to Remedy Statistical Assumption Violations



Part 7 Bivariate Correlation

Chapter 22 Pearson Correlation

Chapter 23 Spearman Rho and Kendall Tau-B Rank-Order Correlations



Part 8 Regressing (Predicting) Quantitative Variables

Chapter 24 Simple Linear Regression

Chapter 25 Centering the Predictor Variable in Simple Linear Regression

Chapter 26 Multiple Linear Regression

Chapter 27 Hierarchical Linear Regression

Chapter 28 Polynomial Regression

Chapter 29 Multilevel Modeling



Part 9 Regressing (Predicting) Categorical Variables

Chapter 30 Binary Logistic Regression

Chapter 31 ROC Analysis

Chapter 32 Multinomial Logistic Regression



Part 10 Survival Analysis

Chapter 33 Survival Analysis: Life Tables

Chapter 34 The Kaplan--Meier Survival Analysis

Chapter 35 Cox Regression



Part 11 Reliability as a Gauge of Measurement Quality

Chapter 36 Reliability Analysis: Internal Consistency

Chapter 37 Reliability Analysis: Assessing Rater Consistency



Part 12 Analysis of Structure

Chapter 38 Principal Components and Factor Analysis

Chapter 39 Confirmatory Factor Analysis



Part 13 Evaluating Causal (Predictive) Models

Chapter 40 Simple Mediation

Chapter 41 Path Analysis Using Multiple Regressions

Chapter 42 Path Analysis Using Structural Equation Modeling

Chapter 43 Structural Equation Modeling



Part 14 T Test

Chapter 44 One-Sample T Test

Chapter 45 Independent-Samples T Test

Chapter 46 Paired-Samples T Test



Part 15 Univariate Group Differences: Anova and Ancova

Chapter 47 One-Way Between-Subjects Anova

Chapter 48 Polynomial Trend Analysis

Chapter 49 One-Way Between-Subjects Ancova

Chapter 50 Two-Way Between-Subjects Anova

Chapter 51 One-Way Within-Subjects Anova

Chapter 52 Repeated Measures Using Linear Mixed Models

Chapter 53 Two-Way Mixed Anova



Part 16 Multivariate Group Differences: Manova and Discriminant Function Analysis

Chapter 54 One-Way Between-Subjects Manova

Chapter 55 Discriminant Function Analysis

Chapter 56 Two-Way Between-Subjects Manova



Part 17 Multidimensional Scaling

Chapter 57 Multidimensional Scaling: Classical Metric

Chapter 58 Multidimensional Scaling: Metric Weighted



Part 18 Cluster Analysis

Chapter 59 Hierarchical Cluster Analysis

Chapter 60 K-Means Cluster Analysis



Part 19 Nonparametric Procedures for Analyzing Frequency Data

Chapter 61 Single-Sample Binomial and Chi-Square Tests: Binary Categories

Chapter 62 Single-Sample (One-Way) Multinominal Chi-Square Tests

Chapter 63 Two-Way Chi-Square Test of Independence

Chapter 64 Risk Analysis

Chapter 65 Chi-Square Layers

Chapter 66 Hierarchical Log linear Analysis



Appendix Statistics Tables

References

Author Index