Careful data collection and analysis lies at the heart of good research, through which our understanding of psychology is enhanced. Yet the students who will become the next generation of researchers need more exposure to statistics and experimental design than a typical introductory course presents.
Experimental Design and Analysis for Psychology provides a complete course in data collection and analysis for students who need to go beyond the basics.
Acting as a true course companion, the text's engaging writing style leads readers through a range of often challenging topics, blending examples and exercises with careful explanations and custom-drawn figures to ensure even the most daunting concepts can be fully understood.
Opening with a review of key concepts, including probability, correlation, and regression, the book goes on to explore the analysis of variance and factorial designs, before moving on to consider a range of more specialised, but yet powerful, statistical tools, including the General Linear Model, and the concept of unbalanced designs.
Not just a printed book, Experimental Design and Analysis for Psychology is enhanced by a range of online materials, all of which add to its value as an ideal teaching and learning resource.
The Online Resource Centre features:
For registered adopters:
Figures from the book, available to download.
Answers to exercises featured in the book.
Online-only Part III: bonus chapters featuring more advanced material, to extend the coverage of the printed book.
For students:
A downloadable workbook, featuring exercises for self-study.
SAS, SPSS and R companions, featuring program code and output for all major examples in the book tailored to these three software packages.
Rezensionen / Stimmen
The structure of the book makes a lot of sense, and the chapters I have seen are well-written. * David Lane, Rice University * Overall, I think the text has the potential to be an effective one...The writing style is excellent, and the amount and quality of in-text supporting material is excellent as well. * James Bovaird, University of Nebraska-Lincoln *
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Illustrationen
Maße
Höhe: 265 mm
Breite: 195 mm
Dicke: 29 mm
Gewicht
ISBN-13
978-0-19-929988-1 (9780199299881)
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 Klassifikation
Herve Abdi He is currently a full professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas and an adjunct professor of radiology at the University of Texas Southwestern Medical Center
at Dallas. His research interests include face processing and computational models of face processing, neural networks, computational and statistical models of cognitive processes (especially memory and learning), experimental design, and multivariate statistical analysis. He has published several books and papers in these domains.
Betty Edelman teaches Statistics for Psychology and Research Design
and Analysis as a senior lecturer at the University of Texas at Dallas. Her interests include modeling of cognitive processes using neural networks. She is a co-author of several research articles and a book about neural networks.
Dominique Valentin is currently associate professor at the University of Bourgogne at Dijon, France. She has published a book and several papers dealing with neural networks and modeling.
W. Jay Dowling is a professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas. His research interests have centred on the psychological reality and relevance to perception and memory of patterns of musical organization.
Autor*in
University of Texas, Dallas, USA
University of Texas, Dallas, USA
University of Bourgogne, Dijon, France
University of Texas, Dallas, USA
1 INTRODUCTION TO EXPERIMENTAL DESIGN; 2 CORRELATION; 3 STATISTICAL TEST: THE F TEST; 4 SIMPLE LINEAR REGRESSION; 5 ORTHOGONAL MULTIPLE REGRESSION; 6 NON-ORTHOGONAL MULTIPLE REGRESSION; 7 ANOVA ONE FACTOR: INTUITIVE APPROACH AND COMPUTATION OF F; 8 ANOVA, ONE FACTOR: TEST, COMPUTATION, AND EFFECT SIZE; 9 ANOVA, ONE FACTOR: REGRESSION POINT OF VIEW; 10 ANOVE, ONE FACTOR: SCORE MODEL; 11 ASSUMPTIONS OF ANALYSIS OF VARIANCE; 12 ANALYSIS OF VARIANCE, ONE FACTOR: PLANNED ORTHOGONAL COMPARISONS; 13 ANOVA, ONE FACTOR: PLANNED NON-ORTHOGONAL COMPARISONS; 14 ANOVA, ONE FACTOR: POST HOC OR A POSTERIORI ANALYSES; 15 MORE ON EXPERIMENTAL DESIGN: MULTI-FACTORIAL DESIGNS; 16 ANOVA, TWO FACTORS: AXB OR S(AXB); 17 FACTORIAL DESIGNS AND CONTRASTS; 18 ANOVA, ONE FACTOR REPEATED MEASURES DESIGN: SXA; 19 ANOVA, TTWO FACTORS COMPLETELY REPEATED MEASURES: SXAXB; 20 ANOVA TWO FACTOR PARTIALLY REPEATED MEASURES: S(A)XB; 21 ANOVA, NESTED FACTORIAL DESIGNS: SXA(B); 22 HOW TO DERIVE EXPECTED VALUES FOR ANY DESIGN; A DESCRIPTIVE STATISTICS; B THE SUM SIGN: E; C ELEMENTARY PROBABILITY: A REFRESHER; D PROBABILITY DISTRIBUTIONS; E THE BINOMIAL TEST; F EXPECTED VALUES; STATISTICAL TABLES
