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Primer on Effect Sizes, Simple Research Designs, and Confidence Intervals
Sapp, Marty
Charles C. Thomas, Publishers / Softcover / 2017-11-01 / 0398091978
Research / Psychological Testing
price: $52.95 (may be subject to change)
196 pages
Not in Stock, usually ships in 3-4 weeks

A Primer on Effect Sizes, Simple Research Designs, and Confidence Intervals was designed to help individuals learn to calculate effect sizes for their research designs. Effect sizes allow a clinician or researcher to determine the effect of a treatment. For example, an effect size of zero would indicate that the treatment had no effect, but generally effect sizes allow researchers to see the degree of effect of some treatment or intervention. Often, researchers and clinicians are not aware that effect sizes are connected to research designs. For years, statisticians have been aware of limits of null hypothesis significance testing (NHST). The Wilkinson Task Force (Wilkinson & Task Force on Statistical Inference, 1999) recommended that researchers report effect sizes and confidence intervals in addition to null hypothesis significance testing (NHST). The purpose of this book is to provide the connection among effect sizes, confidence intervals, and simple research designs. Also, some commonly used univariate and multivariate statistics are covered. Regression discontinuity designs, simple moderation and mediation designs, power analysis, and fit indices as effect sizes measure are presented. All calculations are demonstrated through a calculator and statistical packages such as Microsoft Excel, SPSS, SAS, Hayes' Process Analysis, and EQS. This book covers more than 25 effect sizes that are connected to simple research designs. It will be of interest to students taking a statistics class, research methods class, or research design class. Unlike many texts within this area, the current test will give students or researchers the understanding of how to calculate effect sizes with a simple calculator or with a few commands from statistical software programs. Hence, mathematical ability is not a prerequisite for this text. This text provides a nonmathematical treatment of effect sizes within the context of research designs. Finally, to aid understanding, critical material is repeated throughout this book.


1. Introduction: r and d effect sizes
History of Effect Sizes
Reliability for Unidimensional Scales
Reliability for Multidimensional Scales
Face Validity
Content Validity
Criterion Validity
Predictive Validity
Construct Validity
Effect Sizes
Definition of Multivariate Statistics
Confidence Intervals
Testing Calculated Validity Coefficients Against Hypothesized Values
Standard Error of Estimate
Confidence Intervals Around Validity
A Practical Example of a One Sample Case
Confidence Interval
The Effect Size r
Counternull Value of an Effect
Confidence Intervals Around the Effect Size r
Using SAS for Calculating d Effect Size Confidence Intervals
SAS Control Lines to Compute an Exact 95% Confidence Interval for Effect Size d For Two Groups of Participants
SAS Control Lines to Compute an Exact 95% Confidence Interval for Effect Size d For One Group of Participants
Chapter Summary
Practice Problems
Answer to Practice Problems

2. Confidence Intervals for A Single Mean

3. Effect Size and Confidence Interval for Differences Between Two Means
(Between Group Research Designs)
Regression Discontinuity Designs

4. One-Group Pre-test Post-test Design

5. Effect Size for One-Way Analysis of Variance or Three or More Group means
Test of Between-Subjects Effects
Test of Homogeneity of Variances
SAS Commands for 95% Confidence Interval for Eta Squared
Welch and Brown-Forsythe Test for Unequal Variances
Factorial Designs
Fixed Effects, Random Effects, and Mixed Model Analysis of Variance (ANOVA)
Disproportional Cell Size or Unbalanced Factorial Designs
Three-Way Analysis of Variance (ANOVA)
Multiple Comparisons
Post Hoc Procedures
Nested ANOVA
One-Way Analysis of Covariance (ANCOVA)

6. Correlations as Effect Sizes

7. Effect Sizes for Two or More Predictors and One Dependent Variable
Multiple Regression
Schematic Design for Two-Predictor Case
Analysis of Variance Table for Regression
Multiple Regression Broken Down into Sums of Squares
Assumptions of Multiple Regression
Suppressor Variables in Multiple Regression
Structure Coefficients within Multiple Regression
Interaction Effects within Multiple Regression
Cross-Validation Formulas with Multiple Regression
Logistic Regression

8. Effect Sizes for Two or More Predictors and Two or More Dependent Variables
Multivariate Regression

9. Effect Size for Two-Group Multivariate Analysis of Variance

10. Moderation and Meditation effects

11. Power Analysis
A Priori and Post Hoc Estimations of Power

12. Path Analysis and Effect Sizes

13. Fit Indices as Effect Size Measures
Book Summary

Name Index
Subject Index

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