Approaching Multivariate Analysis: A practical introduction

✍ Scribed by Pat Dugard, John Todman, Harry Staines

• Publisher: Routledge
• Year: 2022
• Tongue: English
• Leaves: 441
• Edition: 2
• Category: Library

✦ Synopsis

This fully updated new edition not only provides an introduction to a range of advanced statistical techniques that are used in psychology, but has been expanded to include new chapters describing methods and examples of particular interest to medical researchers. It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems.

This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain an intuitive understanding of what each method is for, what it does and how it does it. The first chapter briefly reviews the main concepts of univariate and bivariate methods and provides an overview of the multivariate methods that will be discussed, bringing out the relationships among them, and summarising how to recognise what types of problem each of them may be appropriate for tackling. In the remaining chapters, introductions to the methods and important conceptual points are followed by the presentation of typical applications from psychology and medicine, using examples with fabricated data.

Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, as well as details of how to interpret and report the output, and extracts of SPSS syntax and code from relevant SAS procedures.

This book gets students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, medical students and students or academics in any discipline that uses multivariate methods.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface to the Second Edition
Preface to the First Edition
1: Multivariate Techniques in Context
Using this Book
Statistics in Research
Terminology and Conventions
Testing Hypotheses
The Model and Prediction
Power
The General Linear Model
Generalized Linear Models
Exploratory Methods
Reference
2: Analysis of Variance (ANOVA)
Introduction and Terminology
Assumptions and Transformations
Effect Size and Power
A One-Way ANOVA
A Factorial Between-Subjects Design
A Factorial Within-Subjects Design
A Mixed Factorial Design
Reporting Results
References
3: Multivariate Analysis of Variance (MANOVA)
Introduction
A Between-Subjects Design with Two DVs
A Within-Subjects Design with Two DVs
MANOVA and Repeated Measures ANOVA Compared
Missing Data
Outliers
Reporting Results
4: Multiple Regression
Introduction and Intuitive Explication
Data Requirements
A Psychology Example of a Multiple Regression Problem
Using a Stepwise Method
Categorical Variables
Estimating the Success of Predicting New Cases
Hierarchical Regression
Non-Linear Relationships
Reporting Results
Reference
5: Analysis of Covariance (ANCOVA)
Introduction and Intuitive Explication
ANCOVA: A Psychology Example of a Pretest–Posttest Control Group Design
ANCOVA with More Than One Treatment Factor
Reporting Results
Reference
6: Partial Correlation, Mediation and Moderation
Introduction
Partial Correlation
A Psychology Example Suitable for Partial Correlation Analysis
Semipartial (or Part) Correlations
Reporting Results: Partial Correlation Analysis
Mediation Effects
Reporting Results: Mediation Analysis
Moderating Effects
Reporting Results: Moderation Analysis
Complex Path Models
References
7: Path Analysis
Introduction
Path Diagrams and Terminology
Conducting a Path Analysis Using Regression
Using a Dedicated Package (AMOS) to do Path Analysis
Reporting Results
Reference
8: Factor Analysis
Introduction
Exploratory Factor Analysis (EFA)
The Reliability of Factor Scales: Internal Consistency of Scales
Confirmatory Factor Analysis (CFA)
Structural Equation Modelling
Reporting Results
Reference
9: Discriminant Analysis and Logistic Regression
Discriminant Analysis
A Psychology Example of a Discriminant Analysis Problem
Reporting Results: Discriminant Analysis
Logistic Regression: An Alternative Approach to Classification into Two Groups
An Example with Psychology Data
Reporting Results: Logistic Regression
10: Cluster Analysis
Introduction
Calculating Distance Between Cases
Using the Distance Matrix to Form Clusters
Some Examples and (Fabricated) Data
Results for Other Datasets
Deciding How Many Clusters There are
Clustering Variables and Some (Fabricated) Binary Data
Reporting Results
References
11: Multidimensional Scaling
Introduction and Intuitive Explication
Multidimensional Scaling: A Psychology Example and (Fabricated) Data
Multidimensional Scaling and Seriation
Reporting Results
Reference
12: Loglinear Models
Introduction and Intuitive Explication
A Psychology Example of Loglinear Analysis
Selecting a Reduced Model
Automating Model Selection
Measures of Association and Size of Effects
Variables with More Than Two Categories
Reporting Results
13: Poisson Regression
Introduction
A Psychology Experiment and (Fabricated) Data with Equal Observation Periods
Poisson Models with Unequal Observation Periods
A Psychology Experiment and (Fabricated) Data with Unequal Observation Periods
Reporting Results
14: Survival Analysis
Introduction
A Psychology Example: An Experiment with Fabricated Data
Incomplete Records
Reporting Results
15: Longitudinal Data
Introduction
Some Benefits and Some Problems
ANCOVA
Within-Subjects ANOVA
MANOVA
Regression
Generalized Estimating Equations
Poisson Regression and Survival Analysis
Time Series
Reference
Appendix: SPSS and SAS Syntax
Introduction
An Example of SPSS and Sas Syntax
Uses of SPSS and SAS Syntax
How to Create an SPSS Syntax File
How to Edit an SPSS Syntax File
How to Perform Analyses Using An SPSS Syntax File
SPSS and SAS Syntax for Selected Analyses
Further Reading
Glossary
Abbreviations
Author Index
Subject Index