2nd Edition

Handbook of Univariate and Multivariate Data Analysis with IBM SPSS




ISBN 9781439890219
Published October 25, 2013 by Chapman and Hall/CRC
600 Pages 850 B/W Illustrations

USD $105.00

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Book Description

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.

Table of Contents

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

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Author - Robert  Ho
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Reviews

"Using IBM SPSS as the main statistical tool, Ho tries to alleviate the frustration of social sciences students when confronted with data analysis and is more than successful. In addition, this book can be very useful for applied researchers in other disciplines who intend to deal with data. Very efficiently, the author explains how to select and execute appropriate tests, together with interpretation of the relevant SPSS output. … I strongly recommend [the book] to both students and researchers who already deal or will deal with data."
Journal of Applied Statistics, 2015

Praise for the First Edition:
"The click-by-click instructions would clearly be useful for beginners to SPSS … The examples and methods all have a strong social science flavor, which is consistent with the aims of the book. … This book would therefore seem to be most appropriate for statisticians or practitioners in the social sciences … the book can [also] help more experienced SPSS users who want to learn to write syntax files. …"
Biometrics, December 2006

"… main strengths are the choice of easy-to-use software to apply statistical methods and the clarity of explanations. Learning to analyse data with SPSS with this handbook is very easy even for those who are rusty … . I found no typological errors … author’s aims have been achieved. This is the best book I have found for demonstrating statistical methods with SPSS. I recommend it highly for all…"
—Venkata Putcha, Kings College, London, UK

"The main strengths of the book are: (a) its hands-on approach, (b) the choice of a user-friendly software to teach how to apply statistical methods, and (c) the clarity with which the statistical methods and the context of their applicability are explained. Learning to analyze data with SPSS with this handbook is very easy even for those rusty in their introductory statistical background. The reader that completes the book is ready to use the SPSS manuals available elsewhere. The index is very useful. …The authors do indeed provide clear guidelines to both the execution of the specific statistical tests with SPSS and the research designs for which they are relevant."
—Juana Sanchez, University of California, Los Angeles, Journal of Statistical Software, Vol. 16, July 2006

"This hardback covers most statistical methods provided by SPSS Base software in an easily understood manner, due in part to its liberal use of SPSS output and screenshots. … The inclusion of SPSS syntax is a strong selling point, as well as the focus on interpretation of SPSS output. It is an excellent choice for graduate students and researchers outside the statistics community who use SPSS …"
—J. Wade Davis, University of Missouri, The American Statistician, August 2008