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Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition

By Robert Ho

Chapman and Hall/CRC – 2013

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  • Add to CartHardback: $89.95
    978-1-43-989021-9
    October 25th 2013

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.

Reviews

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

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

Name: Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition (Hardback)Chapman and Hall/CRC 
Description: By Robert Ho. 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...
Categories: Statistics & Computing, Statistical Theory & Methods, Regression Analysis and Multivariate Statistics