Experimental Design & Analysis for Psychology

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Overview

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.

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Product Details

  • ISBN-13: 9780199299881
  • Publisher: Oxford University Press, USA
  • Publication date: 5/3/2009
  • Edition description: New Edition
  • Pages: 576
  • Product dimensions: 7.60 (w) x 10.40 (h) x 1.30 (d)

Meet the Author

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.

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Table of Contents

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

References

Index

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