Dugard P., Todman J., Staines H. Approaching Multivariate Analysis: A practical introduction

London: Routledge, 2022. — 441 p.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 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 recognize 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.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.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.Multivariate Analysis of Variance (MANOVA).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.
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.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.Partial Correlation, Mediation and Moderation.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.Path Analysis.Path Diagrams and Terminology.
Conducting a Path Analysis Using Regression.
Using a Dedicated Package (AMOS) to do Path Analysis.
Reporting Results.Factor Analysis.Exploratory Factor Analysis (EFA).
The Reliability of Factor Scales: Internal Consistency of Scales.
Confirmatory Factor Analysis (CFA).
Structural Equation Modeling.
Reporting Results.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.
Cluster Analysis.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.Multidimensional Scaling.
Introduction and Intuitive Explication.
Multidimensional Scaling: A Psychology Example and (Fabricated) Data.
Multidimensional Scaling and Seriation.
Reporting Results.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.
Poisson Regression.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.
Survival Analysis.A Psychology Example: An Experiment with Fabricated Data.
Incomplete Records.
Reporting Results.
Longitudinal Data.Some Benefits and Some Problems.
ANCOVA.
Within-Subjects ANOVA.
MANOVA.
Regression.
Generalized Estimating Equations.
Poisson Regression and Survival Analysis.
Time Series.Appendix: SPSS and SAS Syntax.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.