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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.
Getting Started with IBM SPSS
Obtaining, Editing and Saving Statistical Output
Manipulating Data
Descriptive Statistics Procedures
Simple Data Transformations
Evaluating Score Distribution Assumptions
Bivariate Correlation
Regressing (Predicting) Quantitative Variables
Regressing (Predicting) Categorical Variables
Survival Analysis
Reliability as a Gauge of Measurement Quality
Analysis of Structure
Evaluating Causal (Predictive) Models
T Test
Univariate Group Differences: Anova and Ancova
Multivariate Group Differences: Manova and Discriminant Function Analysis
Multidimensional Scaling
Cluster Analysis
Nonparametric Procedures for Analyzing Frequency Data
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
Subject IndexISBN - 9788126556854
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Pages : 308
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