Handbook of Univariate and Multivariate Data Analysis with IBM SPSS

Using 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. Data sets employed in the examples are available on the book's CRC Press web page.

Inferential Statistics and Test Selection Introduction Inferential Statistics Test Selection Introduction to SPSS Introduction Setting 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 (2x2 Factorial) Example 2: Three-Way Factorial (2x2x2 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: 2x2x4 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 (x2) Test for Single Variable Experiments Chi-Square (x2) 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 Bibliography Index