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Essentials of Multivariate Data Analysis

By Neil H. Spencer

Chapman and Hall/CRC – 2013 – 186 pages

Purchasing Options:

  • Add to CartPaperback: $59.95
    978-1-46-658478-5
    December 16th 2013

Description

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research.

Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for those who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel® can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book’s CRC Press web page.

Providing a firm foundation in the most commonly used multivariate techniques, this text helps readers choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab®, R, SAS, SPSS, and Stata.

Contents

Frequently Asked Questions

What Questions?

What Analysis Should I Use?

What Data Do I Need?

What Data Is the Author Using in This Book?

What about Missing Data?

What about Other Topics?

What about Computer Packages?

Graphical Presentation of Multivariate Data

Why Do I Want to Do Graphical Presentations of Multivariate Data?

What Data Do I Need for Graphical Presentations of Multivariate Data?

The Rest of This Chapter

Comparable Histograms

A Step-by-Step Guide to Obtaining Comparable Histograms Using the Excel Add-In

Multiple Box Plots

A Step-by-Step Guide to Obtaining Multiple Box Plots Using the Excel Add-In

Trellis Plot

A Step-by-Step Guide to Obtaining a Trellis Plot using the Excel Add-In

Star Plots

Chernoff Faces

Andrews’ Plots

A Step-by-Step Guide to Obtaining Andrews’ Plots using the Excel Add-In

Principal Components Plot

A Step-by-Step Guide to Obtaining a Principal Components Plot Using the Excel Add-In

More Information

Multivariate Tests of Significance

Why Do I Want to Do Multivariate Tests of Significance?

What Data Do I Need for Multivariate Tests of Significance?

The Rest of This Chapter

Comparing Two Vectors of Means

Comparing Two Covariance Matrices

Comparing More than Two Vectors of Means

Comparing More than Two Covariance Matrices

More Information

Factor Analysis

Why Do I Want to Do Factor Analysis?

What Data Do I Need for Factor Analysis?

The Rest of This Chapter

How Do We Extract the Factors?

Interpreting the Results of a PCA Factor Analysis

How Many Factors Are There?

Interpreting the Results of a PAF Factor Analysis

Communalities Briefly Revisited

Rotating Factor Loadings

So Which Solution Do We Believe?

Factor Scores

A Step-by-Step Guide to Factor Analysis Using the Excel Add-In

More Information

Cluster Analysis

Why Do I Want to Do Cluster Analysis?

What Data Do I Need for Cluster Analysis?

The Rest of This Chapter

How Do We Decide How Close Together Two Cases Are?

How Do We Decide How Close Together Two Clusters Are?

How Do We Decide which Distance Measure and Linkage Method to Use?

How Do We Decide How Many Clusters There Are?

Interpreting Clusters

Non-Hierarchical Cluster Analysis

A Step-by-Step Guide to Cluster Analysis Using the Excel Add-In

More Information

Discriminant Analysis

Why Do I Want to Do Discriminant Analysis?

What Data Do I Need for Discriminant Analysis?

The Rest of This Chapter

How Do We Decide How Close a Case Is to Different Groups?

Allocating Individual Cases to Groups

Which Variables Discriminate between Groups?

How Accurate Are the Allocations?

Testing a Discriminant Analysis

Other Methods of Discriminant Analysis

A Step-by-Step Guide to Discriminant Analysis Using the Excel Add-In

More Information

Multidimensional Scaling

Why Do I Want to Do Multidimensional Scaling?

What Data Do I Need for Multidimensional Scaling?

The Rest of This Chapter

Classical Multidimensional Scaling

Other Methods of Multidimensional Scaling

A Step-by-Step Guide to Multidimensional Scaling Using the Excel Add-In

More Information

Correspondence Analysis

Why Do I Want to Do Correspondence Analysis?

What Data Do I Need for Correspondence Analysis?

The Rest of This Chapter

Chi-Square Distances, Inertia and Plots

More Dimensions

Row, Column and Symmetric Normalisations

Correspondence Analysis with More than Two Variables

A Step-by-Step Guide to Correspondence Analysis Using the Excel Add-In

More Information

References

Index

Author Bio

Dr. Neil H. Spencer is a reader in applied statistics and director of the Statistical Services and Consultancy Unit at the University of Hertfordshire. His research interests include multilevel models, multivariate methods, statistical computing, multiple testing, and testing for randomness.

Name: Essentials of Multivariate Data Analysis (Paperback)Chapman and Hall/CRC 
Description: By Neil H. Spencer. Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of...
Categories: Psychological Methods & Statistics, Statistics & Probability, Statistics for Business, Finance & Economics