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E-grāmata: Approaching Multivariate Analysis, 2nd Edition: A Practical Introduction 2nd edition [Taylor & Francis e-book]

(Dundee University, UK), (Department of Psychology, University of Dundee),
  • Formāts: 440 pages, 78 Tables, black and white; 160 Line drawings, black and white; 67 Halftones, black and white; 227 Illustrations, black and white
  • Izdošanas datums: 27-Nov-2009
  • Izdevniecība: Routledge
  • ISBN-13: 9781003343097
  • Taylor & Francis e-book
  • Cena: 155,64 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standarta cena: 222,34 €
  • Ietaupiet 30%
  • Formāts: 440 pages, 78 Tables, black and white; 160 Line drawings, black and white; 67 Halftones, black and white; 227 Illustrations, black and white
  • Izdošanas datums: 27-Nov-2009
  • Izdevniecība: Routledge
  • ISBN-13: 9781003343097
"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 own problems." "This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain anintuitive 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 recognise 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." --Book Jacket.

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 own 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 recognise 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.

Preface to the second edition xiii
Preface to the first edition xv
Multivariate techniques in context
1(12)
Using this book
1(1)
Statistics in research
2(1)
Terminology and conventions
3(1)
Testing hypotheses
4(3)
The model and prediction
7(1)
Power
7(1)
The General Linear Model
8(1)
Generalized Liner Models
9(1)
Exploratory methods
10(1)
Reference
11(2)
Analysis of variance (ANOVA)
13(42)
Introduction and terminology
13(5)
Assumptions and transformations
18(3)
Effect size and power
21(2)
A one-way ANOVA
23(11)
A factorial between-subjects design
34(9)
A factorial within-subjects design
43(6)
A mixed factorial design
49(4)
Reporting results
53(1)
References
54(1)
Multivariate analysis of variance (MANOVA)
55(28)
Introduction
55(1)
A between-subjects design with two DVs
56(14)
A within-subjects design with two DVs
70(6)
MANOVA and repeated measures ANOVA compared
76(3)
Missing data
79(2)
Outliers
81(1)
Reporting results
81(2)
Multiple regression
83(34)
Introduction and intuitive explication
83(2)
Data requirements
85(2)
A psychology example of a multiple regression problem
87(9)
Using a stepwise method
96(2)
Categorical variables
98(2)
Estimating the success of predicting new cases
100(5)
Hierarchical regression
105(5)
Non-linear relationships
110(4)
Reporting results
114(2)
Reference
116(1)
Analysis of covariance (Ancova)
117(22)
Introduction and intuitive explication
117(3)
ANCOVA: A psychology example of a pretest-posttest control group design
120(16)
ANCOVA with more than one treatment factor
136(1)
Reporting results
136(2)
Reference
138(1)
Partial correlation, mediation and moderation
139(20)
Introduction
139(1)
Partial correlation
139(2)
A psychology example suitable for partial correlation analysis
141(4)
Semipartial (or part) correlations
145(1)
Reporting results: Partial correlation analysis
146(1)
Mediation effects
147(4)
Reporting results: Mediation analysis
151(1)
Moderating effects
151(5)
Reporting results: Moderation analysis
156(1)
Complex path models
157(1)
References
158(1)
Path analysis
159(18)
Introduction
159(1)
Path diagrams and terminology
159(3)
Conducting a path analysis using regression
162(6)
Using a dedicated package (AMOS) to do path analysis
168(7)
Reporting results
175(1)
Reference
176(1)
Factor analysis
177(30)
Introduction
177(1)
Exploratory factor analysis (EFA)
178(13)
The reliability of factor scales: Internal consistency of scales
191(2)
Confirmatory factor analysis (CFA)
193(9)
Structural equation modelling
202(2)
Reporting results
204(1)
Reference
205(2)
Discriminant analysis and logistic regression
207(30)
Discriminant analysis
207(3)
A psychology example of a discriminant analysis problem
210(12)
Reporting results: Discriminant analysis
222(1)
Logistic regression: An alternative approach to classification into two groups
223(2)
An example with psychology data
225(9)
Reporting results: Logistic regression
234(3)
Chaster analysis
237(26)
Introduction
237(1)
Calculating distance between cases
238(4)
Using the distance matrix to form clusters
242(3)
Some examples and (fabricated) data
245(9)
Results for other datasets
254(2)
Deciding how many clusters there are
256(1)
Clustering variables and some (fabricated) binary data
257(4)
Reporting results
261(1)
References
262(1)
Multidimenaional scaling
263(24)
Introduction and intuitive explication
263(5)
Multidimensional scaling: A psychology example and (fabricated) data
268(13)
Multidimensional scaling and seriation
281(3)
Reporting results
284(1)
Reference
285(2)
Loglinear models
287(26)
Introduction and intuitive explication
287(3)
A psychology example of loglinear analysis
290(5)
Selecting a reduced model
295(4)
Automating model selection
299(7)
Measures of association and size of effects
306(2)
Variables with more than two categories
308(2)
Reporting results
310(3)
Poisson regression
313(24)
Introduction
313(1)
A psychology experiment and (fabricated) data with equal observation periods
314(11)
Poisson models with unequal observation periods
325(1)
A psychology experiment and (fabricated) data with unequal observation periods
325(9)
Reporting results
334(3)
Survival analysis
337(22)
Introduction
337(2)
A psychology example: an experiment with fabricated data
339(17)
Incomplete records
356(1)
Reporting results
357(2)
Longitudinal data
359(18)
Introduction
359(1)
Some benefits and some problems
359(1)
ANCOVA
360(1)
Within-subjects ANOVA
361(1)
MANOVA
362(2)
Regression
364(5)
Generalized estimating equations
369(6)
Poisson regression and survival analysis
375(1)
Time series
376(1)
Reference
376(1)
Appendix: SPSS and SAS syntax
377(20)
Introduction
377(1)
An example of SPSS and SAS syntax
377(2)
Uses of SPSS and SAS syntax
379(1)
How to create an SPSS syntax file
380(2)
How to edit an SPSS syntax file
382(2)
How to perform analyses using an SPSS syntax file
384(1)
SPSS and SAS syntax for selected analyses
384(13)
Further reading 397(4)
Glossary 401(10)
Abbreviations 411(2)
Author index 413(2)
Subject index 415
 









 



Pat Dugard worked until 1999 at the University of Abertay Dundee teaching statistics and providing statistical support and consultancy for researchers in psychology, engineering and other areas. Since then she has worked on two books with John Todman, with Open University and WEA students, and done statistical work for community groups and for engineers at Abertay.









John Todman lectured in the Psychology Department at the University of Dundee, first as a Lecturer, then as Senior Lecturer, then as a Professor. During that period he taught in addition to aspects of cognitive psychology, Design and Analysis at undergraduate and postgraduate levels. His research has been primarily in the areas of computer-aided communication for people without speech, and in computer anxiety. Sadly, John died just as this edition of the book had been completed.









Harry Staines has lectured statistics to a wide variety of students (including psychologists and nurses), led research groups and supervised postgraduate research students at the University of Abertay Dundee for over 20 years. He left his job as Professor of Applied Statistics in 2007 to work in the pharmaceutical industry designing and analyzing clinical trials. He also provides statistical support to medical researchers and QCMD.