# Experimental Design : Procedures for the Behavioral Sciences

, by Roger E. Kirk**Note:**Supplemental materials are not guaranteed with Rental or Used book purchases.

- ISBN: 9781412974455 | 1412974453
- Cover: Hardcover
- Copyright: 6/13/2012

This book serves as an ideal textbook and reference book for students and researchers in the behavioral sciences and education. It includes a diagram of each design that illustrates how subjects are assigned to treatments, the use of boldface type to emphasize new terms, summaries of the advantages and disadvantages of the designs, and a plethora of review exercises. The most obvious change in the fourth edition is the sequence of the chapters. Other changes include an expanded coverage of exploratory data analysis, measures of practical significance, determination of sample size and power, and one degree-of-freedom measures. The latest advances in the rapidly expanding area of multiple comparisons are described in Chapter 5 along with recommendations for their use. Improved procedures for testing the tenability of assumptions in analysis of variance are described in Chapters 3, 4, and 8. This edition demonstrates the flexibility of the cell means model for analyzing data with missing observations and missing cells.

Preface | p. xi |

About the Author | p. xiii |

Research Strategies and the Control of Nuisance Variables | p. 1 |

Introduction | p. 1 |

Formulation of Plans for the Collection and Analysis of Data | p. 2 |

Research Strategies | p. 6 |

Other Research Strategies | p. 9 |

Threats to Valid Inference Making | p. 16 |

Other Threats to Valid Inference Making | p. 19 |

Controlling Nuisance Variables and Minimizing Threats to Valid Inference Making | p. 21 |

Ethical Treatment of Subjects | p. 24 |

Review Exercises | p. 26 |

Experimental Designs: An Overview | p. 30 |

Introduction | p. 30 |

Overview of Some Basic Experimental Designs | p. 30 |

Classification of Analysis of Variance Designs | p. 45 |

Selecting an Appropriate Design | p. 48 |

Review of Statistical Inference | p. 49 |

Review Exercises | p. 70 |

Fundamental Assumptions in Analysis of Variance | p. 77 |

Sampling Distributions in Analysis of Variance | p. 77 |

Partition of the Total Sum of Squares | p. 86 |

Expectation of the Mean Squares | p. 92 |

The F Statistic in Analysis of Variance | p. 95 |

Effects of Failure to Meet Assumptions in Analysis of Variance | p. 96 |

Transformations | p. 103 |

Other Procedures for Dealing With Nonnormality, Unequal Variances, and Outliers | p. 108 |

Supplement for Section 3.3 | p. 111 |

Review Exercises | p. 117 |

Completely Randomized Design | p. 125 |

Description of the Design | p. 125 |

Exploratory Data Analysis | p. 127 |

Computational Example for CR-4 Design | p. 131 |

Measures of Strength of Association and Effect Size | p. 134 |

Power and the Determination of Sample Size | p. 138 |

Random-Effects Model | p. 145 |

Advantages and Disadvantages of CR-p Design | p. 146 |

Review Exercises | p. 146 |

Multiple Comparison Tests | p. 154 |

Introduction to Multiple Comparison Tests | p. 154 |

Procedures for Testing p - 1 a Priori Orthogonal Contrasts | p. 170 |

Procedures for Testing p - 1 Contrasts Involving a Control Group Mean | p. 176 |

Procedures for Testing C a Priori Nonorthogonal Contrasts | p. 179 |

Procedures for Testing All Pairwise Contrasts | p. 187 |

Testing All Contrasts Suggested by an Inspection of the Data | p. 198 |

Other Multiple Comparison Procedures | p. 200 |

Comparison of Multiple Comparison Procedures | p. 201 |

Review Exercises | p. 201 |

Trend Analysis | p. 209 |

Introduction to Tests for Trends | p. 209 |

Test for the Linear Trend Contrast | p. 211 |

Tests for Higher-Order Trend Contrasts | p. 218 |

Linear and Curvilinear Correlation | p. 225 |

Variance Accounted for by Mean Contrasts | p. 225 |

Review Exercises | p. 227 |

General Linear Model Approach to ANOVA | p. 233 |

Comparison of Analysis of Variance and Multiple Regression | p. 233 |

Operations With Vectors and Matrices | p. 234 |

General Linear Model | p. 244 |

Estimating the Parameters in a Regression Model | p. 247 |

Regression Model Approach to ANOVA | p. 253 |

Alternative Conception of the Test of ß_{1} = ß_{2} = … = ß_{h-1} = 0 | p. 262 |

Cell Means Model Approach to ANOVA | p. 266 |

Summary | p. 272 |

Review Exercises | p. 272 |

Randomized Block Designs | p. 280 |

Description of Randomized Block Design | p. 280 |

Computational Example for RB-p Design | p. 288 |

Alternative Models for RB-p Design | p. 296 |

Some Assumptions Underlying RB-p Design | p. 303 |

Procedures for Testing Differences Among Means | p. 314 |

Tests for Trends | p. 319 |

Relative Efficiency of Randomized Block Design | p. 321 |

Cell Mean Model Approach to the RB-p Design | p. 322 |

Generalized Randomized Block Design | p. 336 |

Advantages and Disadvantages of RB-p and GRB-p Designs | p. 343 |

Review Exercises | p. 344 |

Completely Randomized Factorial Design With Two Treatments | p. 357 |

Introduction to Factorial Designs | p. 357 |

Description of Completely Randomized Factorial Design | p. 357 |

Computational Example for CRF-pq Design | p. 360 |

Experimental Design Model for CRF-pq Design | p. 368 |

Procedures for Testing Differences Among Means | p. 372 |

More on the Interpretation of Interactions | p. 373 |

Tests for Trends | p. 386 |

Estimating Strength of Association, Effect Size, Power, and Sample Size | p. 395 |

Rules for Deriving Expected Values of Mean Squares | p. 400 |

Quasi F Statistics | p. 404 |

Preliminary Tests on the Model and Pooling Procedures | p. 406 |

Analysis of Completely Randomized Factorial Designs With n = 1 | p. 409 |

Cell Means Model Approach to Completely Randomized Factorial Design | p. 411 |

Analysis of Completely Randomized Factorial Designs With Missing Observations and Empty Cells | p. 422 |

Advantages and Disadvantages of Factorial Designs | p. 431 |

Review Exercises | p. 432 |

Completely Randomized Factorial Design With Three or More Treatments and Randomized Block Factorial Design | p. 439 |

Introduction to CRF-pqr Design | p. 439 |

Computational Example for CRF-pqr Design | p. 441 |

Patterns Underlying Sum-of-Squares Formulas | p. 448 |

Formulating Coefficient Matrices for the Cell Means Model | p. 451 |

Introduction to Randomized Block Factorial Design | p. 458 |

Computational Example for RBF-pq Design | p. 460 |

Expected Value of Mean Squares and the Sphericity Conditions | p. 465 |

Cell Means Model Approach to Randomized Block Factorial Design | p. 469 |

Minimizing Time and Location Effects by Using a Randomized Block Factorial Design | p. 484 |

Review Exercises | p. 485 |

Hierarchical Designs | p. 489 |

Introduction to Hierarchical Designs | p. 489 |

Computational Example for CKH-pq(A) Design | p. 492 |

Experimental Design Model for CRH-pq(A) Design | p. 496 |

Procedures for Testing Differences Among Means | p. 498 |

Estimating Strength of Association, Effect Size, Power, and Sample Size | p. 500 |

Description of Other Completely Randomized Hierarchical Designs | p. 502 |

Cell Means Model for Completely Randomized Hierarchical Design | p. 515 |

Cell Means Model for Randomized Block Hierarchical! Design | p. 521 |

Advantages and Disadvantages of Hierarchical Designs | p. 530 |

Review Exercises | p. 531 |

Split-Plot Factorial Design: Design With Group-Treatment Confounding | p. 541 |

Description of Split-Plot Factorial Design | p. 541 |

Computational Example for SW-p·q Design | p. 544 |

Experimental Design Model for SPF-p·q Design | p. 550 |

Some Assumptions Underlying SFF-p·q Design | p. 555 |

Procedures for Testing Differences Among Means | p. 560 |

Procedures for Testing Hypotheses About Simple Main Effects and Treatment-Contrast Interactions | p. 566 |

Relative Efficiency of Split-Plot Factorial Design | p. 569 |

Computational Procedures for SPF-pr·q Design | p. 570 |

Computational Procedures for SW-prt·q Design | p. 579 |

Computational Procedures for SPF-p·qr Design | p. 583 |

Computational Procedures for STF-p·qrt Design | p. 590 |

Computational Procedures for SW-pr·qt Design | p. 595 |

Evaluation of Sequence Effects | p. 595 |

Cell Means Model Approach to SPF-p·g Design | p. 597 |

Advantages and Disadvantages of Split-Plot Factorial Designs | p. 613 |

Review Exercises | p. 613 |

Analysis of Covariance | p. 621 |

Introduction to Analysis of Covariance | p. 621 |

Rationale Underlying Covariate Adjustment | p. 625 |

Layout and Computational Procedures for CRAC-p Design | p. 633 |

Some Assumptions Underlying CRAC-p Design | p. 637 |

Procedures for Testing Differences Among Means in CRAC-p Design | p. 640 |

Analysis With Two Covariates | p. 642 |

Analysis of Covariance for Randomized Block Design | p. 646 |

Analysis of Covariance for Factorial Designs | p. 648 |

Covariance Versus Stratification | p. 654 |

Regression Model Approach to Analysis of Covariance | p. 656 |

Cell Means Model Approach to Analysis of Covariance | p. 660 |

Advantages and Disadvantages of Analysis of Covariance | p. 663 |

Review Exercises | p. 664 |

Latin Square and Related Designs | p. 671 |

Description of Latin Square Design | p. 671 |

Construction and Randomization of Latin Squares | p. 672 |

Computational Example for Latin Square Design | p. 675 |

Computational Procedures for n = 1 | p. 681 |

Experimental Design Model for Latin Square Design | p. 684 |

Procedures for Testing Differences Among Means | p. 687 |

Relative Efficiency of Latin Square Design With n = 1 | p. 687 |

Analysis of Covariance for Latin Square Design | p. 690 |

Cell Means Model Approach to Latin Square Design | p. 692 |

Graeco-Latin Square Design | p. 700 |

Hyper-Graeco-Latin Square Designs | p. 702 |

Crossover Design | p. 703 |

Advantages and Disadvantages of Designs Based on a Latin Square | p. 710 |

Review Exercises | p. 711 |

Confounded Factorial Designs: Designs With Group-Interaction Confounding | p. 719 |

Group-Interaction Confounding | p. 719 |

Use of Modular Arithmetic in Constructing Confounded Designs | p. 722 |

Computational Procedures for RBCF-2^{2} Design | p. 726 |

Experimental Design Model for RBCF-2^{2} Design | p. 729 |

Layout and Analysis for RBCF-2^{3} Design | p. 732 |

Complete Versus Partial Confounding | p. 739 |

Computational Procedures for RBPF-2^{3} Design | p. 740 |

Computational Procedures for RBCF-3^{2} and RBPF-3^{2} Designs | p. 749 |

Analysis Procedures for Higher-Order Confounded Designs | p. 760 |

Alternative Notation and Computational Systems | p. 772 |

Computational Procedures for RBPF-32^{2} Design | p. 775 |

Cell Means Model Approach to RBCF-p^{k} Design | p. 785 |

Group-Interaction Confounding by Means of a Latin Square | p. 787 |

Advantages and Disadvantages of Confounding in Factorial Designs | p. 793 |

Review Exercises | p. 796 |

Fractional Factorial Designs: Designs With Treatment-Interaction Confounding | p. 803 |

Introduction to Fractional Factorial Designs | p. 803 |

General Procedures for Constructing Completely Randomized Fractional Factorial Designs | p. 805 |

Computational Procedures for CRFF-2^{4-1} Design | p. 810 |

Computational Procedures for CRFF-3^{4-1} Design | p. 814 |

Cell Means Model for CRFF-p^{k-i} Design | p. 820 |

General Procedures for Constructing RBFF-p^{k-i} Designs | p. 823 |

Other Types of CRFF and RBFF Designs | p. 824 |

Introduction to Latin Square Fractional Factorial Designs | p. 825 |

Computational Procedures for LSFF-p·p^{2} Design | p. 828 |

Computational Procedures for LSFF-p^{3}t Design | p. 832 |

Computational Procedures for LSFF-p^{4}u Design | p. 838 |

Computational Procedures for GLSFF-p^{3} Design | p. 840 |

Advantages and Disadvantages of Fractional Factorial Designs | p. 841 |

Review Exercises | p. 842 |

Rules of Summation | p. 847 |

Rules of Expectation, Variance, and Covariance | p. 852 |

Orthogonal Coefficients for Unequal Intervals and Unequal ns | p. 858 |

Matrix Algebra | p. 863 |

Tables | p. 891 |

Answers to Starred Exercises | p. 952 |

References | p. 1035 |

Index | p. 1048 |

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