Experimental Design Procedures for the Behavioral Sciences 4th Edition by Roger Kirk – Ebook PDF Instant Download/Delivery: 1483305516, 9781483305516
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ISBN 10: 1483305516
ISBN 13: 9781483305516
Author: Roger Kirk
This classic text, with a reputuation for accessibility and readability, has been revised and updated to make learning design concepts even easier. Roger E. Kirk shows how three simple experimental designs can be combined to form a variety of complex designs. He provides diagrams illustrating how subjects are assigned to treatments and treatment combinations. New terms are emphasized in boldface type, there are summaries of the advantages and disadvantages of each design, and real-life examples show how the designs are used.
Experimental Design Procedures for the Behavioral Sciences 4th Table of contents:
CHAPTER 1 Research Strategies and the Control of Nuisance Variables
1.1 Introduction
1.2 Formulation of Plans for the Collection and Analysis of Data
1.3 Research Strategies
1.4 Other Research Strategies
1.5 Threats to Valid Inference Making
1.6 Other Threats to Valid Inference Making
1.7 Controlling Nuisance Variables and Minimizing Threats to Valid Inference Making
1.8 Ethical Treatment of Subjects
1.9 Review Exercises
CHAPTER 2 Experimental Designs: An Overview
2.1 Introduction
2.2 Overview of Some Basic Experimental Designs
2.3 Classification of Analysis of Variance Designs
2.4 Selecting an Appropriate Design
2.5 Review of Statistical Inference
2.6 Review Exercises
CHAPTER 3 Fundamental Assumptions in Analysis of Variance
3.1 Sampling Distributions in Analysis of Variance
3.2 Partition of the Total Sum of Squares
3.3 Expectation of the Mean Squares
3.4 The F Statistic in Analysis of Variance
3.5 Effects of Failure to Meet Assumptions in Analysis of Variance
3.6 Transformations
3.7 Other Procedures for Dealing With Nonnormality, Unequal Variances, and Outliers
3.8 Supplement for Section 3.3
3.9 Review Exercises
CHAPTER 4 Completely Randomized Design
4.1 Description of the Design
4.2 Exploratory Data Analysis
4.3 Computational Example for CR-4 Design
4.4 Measures of Strength of Association and Effect Size
4.5 Power and the Determination of Sample Size
4.6 Random-Effects Model
4.7 Advantages and Disadvantages of CR-p Design
4.8 Review Exercises
CHAPTER 5 Multiple Comparison Tests
5.1 Introduction to Multiple Comparison Tests
5.2 Procedures for Testing p – 1 a Priori Orthogonal Contrasts
5.3 Procedures for Testing p – 1 Contrasts Involving a Control Group Mean
5.4 Procedures for Testing C a Priori Nonorthogonal Contrasts
5.5 Procedures for Testing All Pairwise Contrasts
5.6 Testing All Contrasts Suggested by an Inspection of the Data
5.7 Other Multiple Comparison Procedures
5.8 Comparison of Multiple Comparison Procedures
5.9 Review Exercises
CHAPTER 6 Trend Analysis
6.1 Introduction to Tests for Trends
6.2 Test for the Linear Trend Contrast
6.3 Tests for Higher-Order Trend Contrasts
6.4 Linear and Curvilinear Correlation
6.5 Variance Accounted for by Mean Contrasts
6.6 Review Exercises
CHAPTER 7 General Linear Model Approach to ANOVA
7.1 Comparison of Analysis of Variance and Multiple Regression
7.2 Operations With Vectors and Matrices
7.3 General Linear Model
7.4 Estimating the Parameters in a Regression Model
7.5 Regression Model Approach to ANOVA
7.6 Alternative Conception of the Test of β1 = β2 = … = βh – 1= 0
7.7 Cell Means Model Approach to ANOVA
7.8 Summary
7.9 Review Exercises
CHAPTER 8 Randomized Block Designs
8.1 Description of Randomized Block Design
8.2 Computational Example for RB-p Design
8.3 Alternative Models for RB-p Design
8.4 Some Assumptions Underlying RB-p Design
8.5 Procedures for Testing Differences Among Means
8.6 Tests for Trends
8.7 Relative Efficiency of Randomized Block Design
8.8 Cell Mean Model Approach to the RB-p Design
8.9 Generalized Randomized Block Design
8.10 Advantages and Disadvantages of RB-p and GRB-p Designs
8.11 Review Exercises
CHAPTER 9 Completely Randomized Factorial Design With Two Treatments
9.1 Introduction to Factorial Designs
9.2 Description of Completely Randomized Factorial Design
9.3 Computational Example for CRF-pq Design
9.4 Experimental Design Model for CRF-pq Design
9.5 Procedures for Testing Differences Among Means
9.6 More on the Interpretation of Interactions
9.7 Tests for Trends
9.8 Estimating Strength of Association, Effect Size, Power, and Sample Size
9.9 Rules for Deriving Expected Values of Mean Squares
9.10 Quasi F Statistics
9.11 Preliminary Tests on the Model and Pooling Procedures
9.12 Analysis of Completely Randomized Factorial Designs With n = 1
9.13 Cell Means Model Approach to Completely Randomized Factorial Design
9.14 Analysis of Completely Randomized Factorial Designs With Missing Observations and Empty Cells
9.15 Advantages and Disadvantages of Factorial Designs
9.16 Review Exercises
CHAPTER 10 Completely Randomized Factorial Design With Three or More Treatments and Randomized Block Factorial Design
10.1 Introduction to CRF-pqr Design
10.2 Computational Example for CRF-pqr Design
10.3 Patterns Underlying Sum-of-Squares Formulas
10.4 Formulating Coefficient Matrices for the Cell Means Model
10.5 Introduction to Randomized Block Factorial Design
10.6 Computational Example for RBF-pq Design
10.7 Expected Value of Mean Squares and the Sphericity Conditions
10.8 Cell Means Model Approach to Randomized Block Factorial Design
10.9 Minimizing Time and Location Effects by Using a Randomized Block Factorial Design
10.10 Review Exercises
CHAPTER 11 Hierarchical Designs
11.1 Introduction to Hierarchical Designs
11.2 Computational Example for CRH-pq(A) Design
11.3 Experimental Design Model for CRH-pq(A) Design
11.4 Procedures for Testing Differences Among Means
11.5 Estimating Strength of Association, Effect Size, Power, and Sample Size
11.6 Description of Other Completely Randomized Hierarchical Designs
11.7 Cell Means Model for Completely Randomized Hierarchical Design
11.8 Cell Means Model for Randomized Block Hierarchical Design
11.9 Advantages and Disadvantages of Hierarchical Designs
11.10 Review Exercises
CHAPTER 12 Split-Plot Factorial Design: Design With Group-Treatment Confounding
12.1 Description of Split-Plot Factorial Design
12.2 Computational Example for SPF-p.q Design
12.3 Experimental Design Model for SPF-p.q Design
12.4 Some Assumptions Underlying SPF-p.q Design
12.5 Procedures for Testing Differences Among Means
12.6 Procedures for Testing Hypotheses About Simple Main Effects and Treatment-Contrast Interactions
12.7 Relative Efficiency of Split-Plot Factorial Design
12.8 Computational Procedures for SPF-pr.q Design
12.9 Computational Procedures for SPF-prt.q Design
12.10 Computational Procedures for SPF-p.qr Design
12.11 Computational Procedures for SPF-p.qrt Design
12.12 Computational Procedures for SPF-pr.qt Design
12.13 Evaluation of Sequence Effects
12.14 Cell Means Model Approach to SPF-p.q Design
12.15 Advantages and Disadvantages of Split-Plot Factorial Designs
12.16 Review Exercises
CHAPTER 13 Analysis of Covariance
13.1 Introduction to Analysis of Covariance
13.2 Rationale Underlying Covariate Adjustment
13.3 Layout and Computational Procedures for CRAC-p Design
13.4 Some Assumptions Underlying CRAC-p Design
13.5 Procedures for Testing Differences Among Means in CRAC-p Design
13.6 Analysis With Two Covariates
13.7 Analysis of Covariance for Randomized Block Design
13.8 Analysis of Covariance for Factorial Designs
13.9 Covariance Versus Stratification
13.10 Regression Model Approach to Analysis of Covariance
13.11 Cell Means Model Approach to Analysis of Covariance
13.12 Advantages and Disadvantages of Analysis of Covariance
13.13 Review Exercises
CHAPTER 14 Latin Square and Related Designs
14.1 Description of Latin Square Design
14.2 Construction and Randomization of Latin Squares
14.3 Computational Example for Latin Square Design
14.4 Computational Procedures for n = 1
14.5 Experimental Design Model for Latin Square Design
14.6 Procedures for Testing Differences Among Means
14.7 Relative Efficiency of Latin Square Design With n = 1
14.8 Analysis of Covariance for Latin Square Design
14.9 Cell Means Model Approach to Latin Square Design
14.10 Graeco-Latin Square Design
14.11 Hyper-Graeco-Latin Square Designs
14.12 Crossover Design
14.13 Advantages and Disadvantages of Designs Based on a Latin Square
14.14 Review Exercises
CHAPTER 15 Confounded Factorial Designs: Designs With Group-Interaction Confounding
15.1 Group-Interaction Confounding
15.2 Use of Modular Arithmetic in Constructing Confounded Designs
15.3 Computational Procedures for RBCF-2 Design
15.4 Experimental Design Model for RBCF-2 Design
15.5 Layout and Analysis for RBCF-2 Design
15.6 Complete Versus Partial Confounding
15.7 Computational Procedures for RBPF-2 Design
15.8 Computational Procedures for RBCF-3 and RBPF-3 Designs
15.9 Analysis Procedures for Higher-Order Confounded Designs
15.10 Alternative Notation and Computational Systems
15.11 Computational Procedures for RBPF-32 Design
15.12 Cell Means Model Approach to RBCF-p Design
15.13 Group-Interaction Confounding by Means of a Latin Square
15.14 Advantages and Disadvantages of Confounding in Factorial Designs
15.15 Review Exercises
CHAPTER 16 Fractional Factorial Designs: Designs With Treatment-Interaction Confounding
16.1 Introduction to Fractional Factorial Designs
16.2 General Procedures for Constructing Completely Randomized Fractional Factorial Designs
16.3 Computational Procedures for CRFF-24–1 Design
16.4 Computational Procedures for CRFF-34–1 Design
16.5 Cell Means Model for CRFF-p–i Design
16.6 General Procedures for Constructing RBFF-p–i Designs
16.7 Other Types of CRFF and RBFF Designs
16.8 Introduction to Latin Square Fractional Factorial Designs
16.9 Computational Procedures for LSFF-p.p Design
16.10 Computational Procedures for LSFF-pt Design
16.11 Computational Procedures for LSFF-pu Design
16.12 Computational Procedures for GLSFF-p Design
16.13 Advantages and Disadvantages of Fractional Factorial Designs
16.14 Review Exercises
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