1. Introduction 1.1 Psychological Measurement and Tests 1.2 Tests and Samples of Behavior 1.3 Types of Tests 1.4 Origin of Psychometrics 1.5 Definition of Measurement 1.6 Measuring Behavior 1.7 Psychometrics and Its Importance to Research and Practice 1.8 Organization of This Book Key Terms and Definitions 2. Measurement and Statistical Concepts 2.1 Introduction 2.2 Numbers and Measurement 2.3 Properties of Measurement in Relation to Numbers 2.4 Levels of Measurement 2.5 Contemporary View on the Levels of Measurement and Scaling 2.6 Statistical Foundations for Psychometrics 2.7 Variables, Frequency Distributions, and Scores 2.8 Summation or Sigma Notation 2.9 Shape, Central Tendency, and Variability of Score Distributions 2.10 Correlation, Covariance, and Regression 2.11 Summary Key Terms and Definitions 3. Criterion, Content, and Construct Validity 3.1 Introduction 3.2 Criterion Validity 3.3 Essential Elements of a High-Quality Criterion 3.4 Statistical Estimation of Criterion Validity 3.5 Correction for Attenuation 3.6 Limitations to Using the Correction for Attenuation 3.7 Estimating Criterion Validity with Multiple Predictors: Partial Correlation 3.8 Estimating Criterion Validity with Multiple Predictors: Higher-Order Partial Correlation 3.9 Coefficient of Multiple Determination and Multiple Correlation 3.10 Estimating Criterion Validity with More Than One Predictor: Multiple Linear Regression 3.11 Regression Analysis for Estimating Criterion Validity: Development of the Regression Equation 3.12 Unstandardized Regression Equation for Multiple Regression 3.13 Testing the Regression Equation for Significance 3.14 Partial Regression Slopes 3.15 Standardized Regression Equation 3.16 Predictive Accuracy of a Regression Analysis 3.17 Predictor Subset Selection in Regression 3.18 Summary Key Terms and Definitions 4. Statistical Aspects of the Validation Process 4.1 Techniques for Classification and Selection 4.2 Discriminant Analysis 4.3 Multiple-Group Discriminant Analysis 4.4 Logistic Regression 4.5 Logistic Multiple Discriminant Analysis: Multinomial Logistic Regression 4.6 Model Fit in Logistic Regression 4.7 Content Validity 4.8 Limitations of the Content Validity Model 4.9 Construct Validity 4.10 Establishing Evidence of Construct Validity 4.11 Correlational Evidence of Construct Validity 4.12 Group Differentiation Studies of Construct Validity 4.13 Factor Analysis and Construct Validity 4.14 Multitrait–Multimethod Studies 4.15 Generalizability Theory and Construct Validity 4.16 Summary and Conclusions Key Terms and Definitions 5. Scaling 5.1 Introduction 5.2 A Brief History of Scaling 5.3 Psychophysical versus Psychological Scaling 5.4 Why Scaling Models Are Important 5.5 Types of Scaling Models 5.6 Stimulus-Centered Scaling 5.7 Thurstone’s Law of Comparative Judgment 5.8 Response-Centered Scaling 5.9 Scaling Models Involving Order 5.10 Guttman Scaling 5.11 The Unfolding Technique 5.12 Subject-Centered Scaling 5.13 Data Organization and Missing Data 5.14 Incomplete and Missing Data 5.15 Summary and Conclusions Key Terms and Definitions 6. Test Development 6.1 Introduction 6.2 Guidelines for Test and Instrument Development 6.3 Item Analysis 6.4 Item Difficulty 6.5 Item Discrimination 6.6 Point–Biserial Correlation 6.7 Biserial Correlation 6.8 Phi Coefficient 6.9 Tetrachoric Correlation 6.10 Item Reliability and Validity 6.11 Standard Setting 6.12 Standard-Setting Approaches 6.13 The Nedelsky Method 6.14 The Ebel Method 6.15 The Angoff Method and Modifications 6.16 The Bookmark Method 6.17 Summary and Conclusions Key Terms and Definitions 7. Reliability 7.1 Introduction 7.2 Conceptual Overview 7.3 The True Score Model 7.4 Probability Theory, True Score Model, and Random Variables 7.5 Properties and Assumptions of the True Score Model 7.6 True Score Equivalence, Essential True Score Equivalence, and Congeneric Tests 7.7 Relationship between Observed and True Scores 7.8 The Reliability Index and Its Relationship to the Reliability Coefficient 7.9 Summarizing the Ways to Conceptualize Reliability 7.10 Reliability of a Composite 7.11 Coefficient of Reliability: Methods of Estimation Based on Two Occasions 7.12 Methods Based on a Single Testing Occasion 7.13 Estimating Coefficient Alpha: Computer Programs and Example Data 7.14 Reliability of Composite Scores Based on Coefficient Alpha 7.15 Reliability Estimation Using the Analysis of Variance Method 7.16 Reliability of Difference Scores 7.17 Application of the Reliability of Difference Scores 7.18 Errors of Measurement and Confidence Intervals 7.19 Standard Error of Measurement 7.20 Standard Error of Prediction 7.21 Summarizing and Reporting Reliability Information 7.22 Summary and Conclusions Key Terms and Definitions 8. Generalizability Theory 8.1 Introduction 8.2 Purpose of Generalizability Theory 8.3 Facets of Measurement and Universe Scores 8.4 How Generalizability Theory Extends Classical Test Theory 8.5 Generalizability Theory and Analysis of Variance 8.6 General Steps in Conducting a Generalizability Theory Analysis 8.7 Statistical Model for Generalizability Theory 8.8 Design 1: Single-Facet Person by Item Analysis 8.9 Proportion of Variance for the p x i Design 8.10 Generalizability Coefficient and CTT Reliability 8.11 Design 2: Single-Facet Crossed Design with Multiple Raters 8.12 Design 3: Single-Facet Design with the Same Raters on Multiple Occasions 8.13 Design 4: Single-Facet Nested Design with Multiple Raters 8.14 Design 5: Single-Facet Design Multiple Raters Rating on Two Occasions 8.15 Standard Errors of Measurement: Designs 1–5 8.16 Two-Facet Designs 8.17 Summary and Conclusions Key Terms and Definitions 9. Factor Analysis 9.1 Introduction 9.2 Brief History 9.3 Applied Example with GfGc Data 9.4 Estimating Factors and Factor Loadings 9.5 Factor Rotation 9.6 Correlated Factors and Simple Structure 9.7 The Factor Analysis Model, Communality, and Uniqueness 9.8 Components, Eigenvalues, and Eigenvectors 9.9 Distinction between Principal Components Analysis and Factor Analysis 9.10 Confirmatory Factor Analysis 9.11 Confirmatory Factor Analysis and Structural Equation Modeling 9.12 Conducting Factor Analysis: Common Errors to Avoid 9.13 Summary and Conclusions Key Terms and Definitions 10. Item Response Theory 10.1 Introduction 10.2 How IRT Differs from CTT 10.3 Introduction to IRT 10.4 Strong True Sco

“I like the book and it meshes well with what I plan to do in my course. I particularly like the generalizability theory, norms and test equating, scaling, and validation process chapters. It is very easy reading--I am planning to use the book next spring."--R. J. de Ayala, PhD, Chair of Educational Psychology, University of Nebraska–Lincoln "I encourage all psychologists and educators to read this marvelous book. I learned a lot from reading it. The key terms are very useful, as are the chapter summaries. Readers of all levels will find material relevant to them, including SPSS code and GfGc datasets on intelligence that will be quite useful in trying out the ideas. I give this book my highest recommendation and think it will be a great classroom text."--John J. McArdle, PhD, Department of Psychology, University of Southern California "This book is both comprehensive and accessible, laying the foundation for all the requisite skills needed to be both a successful consumer and producer of psychometrics. Scholars who are unfamiliar with measurement could easily teach themselves from this text, becoming quite proficient at psychometrics. There is excellent integration of quantitative statistics throughout, so that readers will be able not only to understand the psychometric concepts, but also to apply their knowledge. This is a useful text for a graduate-level Psychometric Methods or Measurement class."--Debbie L. Hahs-Vaughn, PhD, Department of Educational and Human Sciences, University of Central Florida "An encyclopedia of psychometric issues--a real 'must have' for anyone teaching Tests and Measurement or Research Methods, or directing student research projects. The book's high level of detail makes it invaluable for any professional who works with, creates, or analyzes psychometric material. The use of intelligence testing data throughout the chapters helps bring cohesiveness."--John Wallace, PhD, Department of Psychological Science, Ball State University "With vast expertise in psychometric instrument development, statistical applications, and research, Price has produced a theoretically informed, practical volume. Professionals in health-related fields will find this book extremely valuable for guidance in the development of rigorous instruments, such as patient-reported outcome measures. Featuring examples using a range of software, this text is ideal for graduate courses on measurement in schools of medicine, public health, nursing, or health professions."--Byron J. Gajewski, PhD, Department of Biostatistics, University of Kansas Medical Center-This book is suitable for emerging assessment professionals and practitioners who are interested in learning psychometrics but with little knowledge in statistics. It provides not only a theoretical foundation to the topics but also worked examples to highlight their practical applications. The syntax, output, and interpretations based on software programs like SPSS and BILOG-MG will help readers to bridge theory and methods with hands-on examples. This book would be a convenient toolbox for applied researchers who would like to conduct psychometric analyses, and it would also serve as a handbook for graduate students who study measurement and psychometrics.--Psychometrika, 3/29/2019ƒƒThis excellent book explores the basic concepts of psychometric knowledge and practice….This would be a great addition to the libraries of graduate students and researchers.--Doody's Review Service, 3/17/2017