Essentials of Multivariate Data Analysis

by Neil H. Spencer

Overview

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.

Product Details

ISBN-13:	9781138462434
Publisher:	Taylor & Francis
Publication date:	11/13/2017
Pages:	186
Product dimensions:	5.44(w) x 8.50(h) x (d)

About the Author

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.

Table of Contents

Frequently Asked Questions. Graphical Presentation of Multivariate Data. Multivariate Tests of Significance. Factor Analysis. Cluster Analysis. Discriminant Analysis. Multidimensional Scaling. Correspondence Analysis. References. Index.

Editorial Reviews

"Postgraduate students without a mathematical or statistical background may find the book helpful as a first step in investigating which multivariate statistics technique may be appropriate to use in their research. … the writing style is very conversational and the mathematics is kept to a minimum. … This book could also be used in an undergraduate research methods course in the social sciences or any other area where the students have a reputation of fearing mathematics."
—Australian & New Zealand Journal of Statistics, 2016

"… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics."
—The American Statistician, February 2015

"… a good introductory read for students studying more mathematical/statistical courses …"
—International Journal of Environmental Analytical Chemistry, 2015

"Postgraduate students without a mathematical or statistical background may find the book helpful as a first step in investigating which multivariate statistics technique may be appropriate to use in their research. … the writing style is very conversational and the mathematics is kept to a minimum. … This book could also be used in an undergraduate research methods course in the social sciences or any other area where the students have a reputation of fearing mathematics."
—Australian & New Zealand Journal of Statistics, 2016

"… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics."
—The American Statistician, February 2015

"… a good introductory read for students studying more mathematical/statistical courses …"
—International Journal of Environmental Analytical Chemistry, 2015

From the Publisher