Essentials of Multivariate Data Analysis
Published on 17. December 2013
186 pages
978-1-040-17695-5 (ISBN)
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 m
Reviews / Votes
"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
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
File size
3,02 MB
ISBN-13
978-1-040-17695-5 (9781040176955)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Neil H. Spencer
Essentials of Multivariate Data Analysis
Book
11/2017
1st Edition
CRC Press
€282.53
Shipment within 10-20 days
Neil H. Spencer
Essentials of Multivariate Data Analysis
Book
12/2013
1st Edition
CRC Press
€84.80
Shipment within 15-20 days
Person
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.
Content
Frequently Asked Questions. Graphical Presentation of Multivariate Data. Multivariate Tests of Significance. Factor Analysis. Cluster Analysis. Discriminant Analysis. Multidimensional Scaling. Correspondence Analysis. References. Index.
