Ho R. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS

Second Edition. — Boca Raton: Chapman&Hall/CRC, 2014. — 545 p. — ISBN: 978-1-4398-9022-6
ISBN10: 1439890218, e-ISBN: 978-1-4398-9022-6Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second Edition:Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation
New section on how to deal with missing data.
Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity.
Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples.
Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling.Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix.Inferential Statistics and Test SelectionInferential Statistics
Test Selection
Introduction to SPSSSetting Up a Data File
SPSS Analysis: Windows Method versus Syntax Method
Missing Data
Multiple Response
Aim
Methods of MULT RESPONSE Procedures
Example of the Multiple-Dichotomy Method
Example of the Multiple-Response Method
Cross-Tabulations
t Test for Independent Groups
Aim
Checklist of Requirements
Assumptions
Example
Paired-Samples t Test
Aim
Checklist of Requirements
Assumption
Example
One-Way Analysis of Variance, with Post Hoc Comparisons
Aim
Checklist of Requirements
Assumptions
Example
Factorial Analysis of Variance
Aim
Checklist of Requirements
Assumptions
Example 1: Two-Way Factorial (2х2 Factorial)
Example 2: Three-Way Factorial (2х2х2 Factorial)
General Linear Model (GLM) Multivariate Analysis
Aim
Checklist of Requirements
Assumptions
Example 1: GLM Multivariate Analysis: One-Sample Test
Example 2: GLM Multivariate Analysis: Two-Sample Test
Example 3: GLM: 2х2х4 Factorial Design
General Linear Model: Repeated Measures Analysis
Aim
Assumption
Example 1: GLM: One-Way Repeated Measures
Example 2: GLM: Two-Way Repeated Measures (Doubly Multivariate Repeated Measures)
Example 3: GLM: Two-Factor Mixed Design (One Between-Groups Variable and One Within-Subjects Variable)
Example 4: GLM: Three-Factor Mixed Design (Two Between-Groups Variables and One Within-Subjects Variable)
Correlation
Aim
Requirements
Assumptions
Example 1: Pearson Product Moment Correlation Coefficient
Testing Statistical Significance between Two Correlation Coefficients Obtained from Two Samples
Example 2: Spearman Rank Order Correlation Coefficient
Linear Regression
Aim
Requirements
Assumptions
Example: Linear Regression
Factor Analysis
Aim
Checklist of Requirements
Assumptions
Factor Analysis: Example 1
Factor Analysis: Example 2
Reliability
Aim
Example: Reliability
Multiple Regression
Aim
Multiple Regression Techniques
Checklist of Requirements
Assumptions
Multicollinearity
Example 1: Prediction Equation and Identification of Independent Relationships (Forward Entry of Predictor Variables)
Example 2: Hierarchical Regression
Example 3: Path Analysis
Example 4: Path Analysis—Test of Significance of the Mediation Hypothesis
Multiple Discriminant Analysis
Aim
Checklist of Requirements
Assumptions
Example 1: Two-Group Discriminant Analysis
Example 2: Three-Group Discriminant Analysis
Logistic Regression
Aim
Checklist of Requirements
Assumptions
Example: Two-Group Logistic Regression
Canonical Correlation Analysis
Aim
Checklist of Requirements
Assumptions
Key Terms in Canonical Correlation Analysis
An Example of Canonical Correlation Analysis
Structural Equation Modeling
What Is Structural Equation Modeling (SEM)?
The Role of Theory in SEM
The Structural Equation Model
Goodness-of-Fit Criteria
Model Assessment
Improving Model Fit
Problems with Estimation
Checklist of Requirements
Assumptions
Examples of Structural Equation Modeling
Example 1: Linear Regression with Observed Variables
Example 2: Regression with Unobserved (Latent) Variables
Example 3: Multi-Model Path Analysis with Latent Variables
Example 4: Multi-Group Analysis
Example 5: Second-Order Confirmatory Factor (CFA) Analysis
Nonparametric Tests
Aim
Chi-Square (χ²) Test for Single Variable Experiments
Chi-Square (χ²) Test of Independence between Two Variables
Mann-Whitney U Test for Two Independent Samples
Kruskal-Wallis Test for Several Independent Samples
Wilcoxon Signed Rank Test for Two Related Samples
Friedman Test for Several Related Samples
Appendix: Summary of SPSS Syntax Files