Essence of Multivariate Thinking

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A01=Lisa L. Harlow
analysis
Author_Lisa L. Harlow
career
categorical variable modeling
Category=JHBC
Category=JMB
Category=KCH
Category=PBT
CFA Model
climate
Coefficient Omega
confidence intervals analysis
confirmatory
DFA Result
Diet Behavior
Diet Stage
Diet Temptations
Discriminant Loading
educational statistics textbook
effect size interpretation
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
factor
Group Difference Methods
High Fat Foods
Hotelling Lawley Trace
influence
Item Parcels
latent growth modeling applications
LGM
LGM Analysis
LGM Model
Low Fat Foods
Macro-level Effect Size
Macro-Level Significance Test
Measurement Error Variance
model
Multiple Sample Analysis
Multivariate Methods
Pillai's Trace
Pillai’s Trace
Proc CORR
psychometrics
Randomly Assigned
SAS statistical programming
satisfaction
Significant MANOVA
statistical
Unstandardized Parameter Estimate
work

Product details

  • ISBN 9780367219727
  • Weight: 680g
  • Dimensions: 178 x 254mm
  • Publication Date: 31 Jul 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Focusing on the underlying themes that run through most multivariate methods, in this fully updated 3rd edition of The Essence of Multivariate Thinking Dr. Harlow shares the similarities and differences among multiple multivariate methods to help ease the understanding of the basic concepts.

The book continues to highlight the main themes that run through just about every quantitative method, describing the statistical features in clear language. Analyzed examples are presented in 12 of the 15 chapters, showing when and how to use relevant multivariate methods, and how to interpret the findings both from an overarching macro- and more specific micro-level approach that includes focus on statistical tests, effect sizes and confidence intervals. This revised 3rd edition offers thoroughly revised and updated chapters to bring them in line with current information in the field, the addition of R code for all examples, continued SAS and SPSS code for seven chapters, two new chapters on structural equation modeling (SEM) on multiple sample analysis (MSA) and latent growth modeling (LGM), and applications with a large longitudinal dataset in the examples of all methods chapters.

Of interest to those seeking clarity on multivariate methods often covered in a statistics course for first-year graduate students or advanced undergraduates, this book will be key reading and provide greater conceptual understanding and clear input on how to apply basic and SEM multivariate statistics taught in psychology, education, human development, business, nursing, and other social and life sciences.

Lisa L. Harlow is a professor emerita of psychology at the University of Rhode Island, USA

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