Experimental Design Procedures for the Behavioral Sciences

ISBN-10: 1412974453
ISBN-13: 9781412974455
Edition: 4th 2013
Authors: Roger E. Kirk
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Book details

List price: $145.00
Edition: 4th
Copyright year: 2013
Publisher: SAGE Publications, Incorporated
Publication date: 6/13/2012
Binding: Hardcover
Pages: 1072
Size: 7.75" wide x 11.00" long x 2.00" tall
Weight: 4.356
Language: English

Roger E. Kirk received his Ph.D. in experimental psychology from the Ohio State University and did post doctoral study in mathematical psychology at the University of Michigan. He is a Distinguished Professor of Psychology and Statistics at Baylor University. He founded, and for 25 years, directed Baylor's Behavioral Statistics Ph.D. program and the Institute of Statistics, now the Department of Statistical Science. For the past 29 years he also has been the president of Research Consultants, a statistical consulting corporation. He has published extensively in the areas of statistics, psychoacoustics, and human engineering, and is the author of five statistics books. Experimental Design: Procedures for the Behavioral Sciences , his first book, was identified by the Institute for Scientific Information as one of the most frequently cited books in its field. Dr. Kirk is a fellow of the American Psychological Association (Divisions 1, 2, 5, 13, and 21), the Association for Psychological Science, and the American Educational Research Association. He is a past president of the Society for Applied Multivariate Research, Division 5 of the American Psychological Association, and the Southwestern Psychological Association. In recognition of his teaching effectiveness, he was named the Outstanding Tenured Teacher in the College of Arts and Sciences and designated a Master Teacher, Baylor University's highest teaching honor. He is the 2005 recipient of the Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring from the Division of Evaluation, Measurement, and Statistics of the American Psychological Association.

Preface
About the Author
Research Strategies and the Control of Nuisance Variables
Introduction
Formulation of Plans for the Collection and Analysis of Data
Research Strategies
Other Research Strategies
Threats to Valid Inference Making
Other Threats to Valid Inference Making
Controlling Nuisance Variables and Minimizing Threats to Valid Inference Making
Ethical Treatment of Subjects
Review Exercises
Experimental Designs: An Overview
Introduction
Overview of Some Basic Experimental Designs
Classification of Analysis of Variance Designs
Selecting an Appropriate Design
Review of Statistical Inference
Review Exercises
Fundamental Assumptions in Analysis of Variance
Sampling Distributions in Analysis of Variance
Partition of the Total Sum of Squares
Expectation of the Mean Squares
The F Statistic in Analysis of Variance
Effects of Failure to Meet Assumptions in Analysis of Variance
Transformations
Other Procedures for Dealing With Nonnormality, Unequal Variances, and Outliers
Supplement for Section 3.3
Review Exercises
Completely Randomized Design
Description of the Design
Exploratory Data Analysis
Computational Example for CR-4 Design
Measures of Strength of Association and Effect Size
Power and the Determination of Sample Size
Random-Effects Model
Advantages and Disadvantages of CR-p Design
Review Exercises
Multiple Comparison Tests
Introduction to Multiple Comparison Tests
Procedures for Testing p - 1 a Priori Orthogonal Contrasts
Procedures for Testing p - 1 Contrasts Involving a Control Group Mean
Procedures for Testing C a Priori Nonorthogonal Contrasts
Procedures for Testing All Pairwise Contrasts
Testing All Contrasts Suggested by an Inspection of the Data
Other Multiple Comparison Procedures
Comparison of Multiple Comparison Procedures
Review Exercises
Trend Analysis
Introduction to Tests for Trends
Test for the Linear Trend Contrast
Tests for Higher-Order Trend Contrasts
Linear and Curvilinear Correlation
Variance Accounted for by Mean Contrasts
Review Exercises
General Linear Model Approach to ANOVA
Comparison of Analysis of Variance and Multiple Regression
Operations With Vectors and Matrices
General Linear Model
Estimating the Parameters in a Regression Model
Regression Model Approach to ANOVA
Alternative Conception of the Test of �<sub>1</sub> = �<sub>2</sub> = &#8230; = �<sub>h-1</sub> = 0
Cell Means Model Approach to ANOVA
Summary
Review Exercises
Randomized Block Designs
Description of Randomized Block Design
Computational Example for RB-p Design
Alternative Models for RB-p Design
Some Assumptions Underlying RB-p Design
Procedures for Testing Differences Among Means
Tests for Trends
Relative Efficiency of Randomized Block Design
Cell Mean Model Approach to the RB-p Design
Generalized Randomized Block Design
Advantages and Disadvantages of RB-p and GRB-p Designs
Review Exercises
Completely Randomized Factorial Design With Two Treatments
Introduction to Factorial Designs
Description of Completely Randomized Factorial Design
Computational Example for CRF-pq Design
Experimental Design Model for CRF-pq Design
Procedures for Testing Differences Among Means
More on the Interpretation of Interactions
Tests for Trends
Estimating Strength of Association, Effect Size, Power, and Sample Size
Rules for Deriving Expected Values of Mean Squares
Quasi F Statistics
Preliminary Tests on the Model and Pooling Procedures
Analysis of Completely Randomized Factorial Designs With n = 1
Cell Means Model Approach to Completely Randomized Factorial Design
Analysis of Completely Randomized Factorial Designs With Missing Observations and Empty Cells
Advantages and Disadvantages of Factorial Designs
Review Exercises
Completely Randomized Factorial Design With Three or More Treatments and Randomized Block Factorial Design
Introduction to CRF-pqr Design
Computational Example for CRF-pqr Design
Patterns Underlying Sum-of-Squares Formulas
Formulating Coefficient Matrices for the Cell Means Model
Introduction to Randomized Block Factorial Design
Computational Example for RBF-pq Design
Expected Value of Mean Squares and the Sphericity Conditions
Cell Means Model Approach to Randomized Block Factorial Design
Minimizing Time and Location Effects by Using a Randomized Block Factorial Design
Review Exercises
Hierarchical Designs
Introduction to Hierarchical Designs
Computational Example for CKH-pq(A) Design
Experimental Design Model for CRH-pq(A) Design
Procedures for Testing Differences Among Means
Estimating Strength of Association, Effect Size, Power, and Sample Size
Description of Other Completely Randomized Hierarchical Designs
Cell Means Model for Completely Randomized Hierarchical Design
Cell Means Model for Randomized Block Hierarchical! Design
Advantages and Disadvantages of Hierarchical Designs
Review Exercises
Split-Plot Factorial Design: Design With Group-Treatment Confounding
Description of Split-Plot Factorial Design
Computational Example for SW-p�q Design
Experimental Design Model for SPF-p�q Design
Some Assumptions Underlying SFF-p�q Design
Procedures for Testing Differences Among Means
Procedures for Testing Hypotheses About Simple Main Effects and Treatment-Contrast Interactions
Relative Efficiency of Split-Plot Factorial Design
Computational Procedures for SPF-pr�q Design
Computational Procedures for SW-prt�q Design
Computational Procedures for SPF-p�qr Design
Computational Procedures for STF-p�qrt Design
Computational Procedures for SW-pr�qt Design
Evaluation of Sequence Effects
Cell Means Model Approach to SPF-p�g Design
Advantages and Disadvantages of Split-Plot Factorial Designs
Review Exercises
Analysis of Covariance
Introduction to Analysis of Covariance
Rationale Underlying Covariate Adjustment
Layout and Computational Procedures for CRAC-p Design
Some Assumptions Underlying CRAC-p Design
Procedures for Testing Differences Among Means in CRAC-p Design
Analysis With Two Covariates
Analysis of Covariance for Randomized Block Design
Analysis of Covariance for Factorial Designs
Covariance Versus Stratification
Regression Model Approach to Analysis of Covariance
Cell Means Model Approach to Analysis of Covariance
Advantages and Disadvantages of Analysis of Covariance
Review Exercises
Latin Square and Related Designs
Description of Latin Square Design
Construction and Randomization of Latin Squares
Computational Example for Latin Square Design
Computational Procedures for n = 1
Experimental Design Model for Latin Square Design
Procedures for Testing Differences Among Means
Relative Efficiency of Latin Square Design With n = 1
Analysis of Covariance for Latin Square Design
Cell Means Model Approach to Latin Square Design
Graeco-Latin Square Design
Hyper-Graeco-Latin Square Designs
Crossover Design
Advantages and Disadvantages of Designs Based on a Latin Square
Review Exercises
Confounded Factorial Designs: Designs With Group-Interaction Confounding
Group-Interaction Confounding
Use of Modular Arithmetic in Constructing Confounded Designs
Computational Procedures for RBCF-2<sup>2</sup> Design
Experimental Design Model for RBCF-2<sup>2</sup> Design
Layout and Analysis for RBCF-2<sup>3</sup> Design
Complete Versus Partial Confounding
Computational Procedures for RBPF-2<sup>3</sup> Design
Computational Procedures for RBCF-3<sup>2</sup> and RBPF-3<sup>2</sup> Designs
Analysis Procedures for Higher-Order Confounded Designs
Alternative Notation and Computational Systems
Computational Procedures for RBPF-32<sup>2</sup> Design
Cell Means Model Approach to RBCF-p<sup>k</sup> Design
Group-Interaction Confounding by Means of a Latin Square
Advantages and Disadvantages of Confounding in Factorial Designs
Review Exercises
Fractional Factorial Designs: Designs With Treatment-Interaction Confounding
Introduction to Fractional Factorial Designs
General Procedures for Constructing Completely Randomized Fractional Factorial Designs
Computational Procedures for CRFF-2<sup>4-1</sup> Design
Computational Procedures for CRFF-3<sup>4-1</sup> Design
Cell Means Model for CRFF-p<sup>k-i</sup> Design
General Procedures for Constructing RBFF-p<sup>k-i</sup> Designs
Other Types of CRFF and RBFF Designs
Introduction to Latin Square Fractional Factorial Designs
Computational Procedures for LSFF-p�p<sup>2</sup> Design
Computational Procedures for LSFF-p<sup>3</sup>t Design
Computational Procedures for LSFF-p<sup>4</sup>u Design
Computational Procedures for GLSFF-p<sup>3</sup> Design
Advantages and Disadvantages of Fractional Factorial Designs
Review Exercises
Rules of Summation
Rules of Expectation, Variance, and Covariance
Orthogonal Coefficients for Unequal Intervals and Unequal ns
Matrix Algebra
Tables
Answers to Starred Exercises
References
Index

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