Efnisyfirlit

• Preface
  • The Conceptual Orientation of This Book, Its Purpose, and the Intended Audience
  • Organizational Overview
  • New to This Edition
    • General Changes
    • Chapter-Specific Changes
  • Author’s Acknowledgments
  • Publisher’s Acknowledgments
• About the Author
• Chapter 1 • Psychometrics and the Importance of Psychological Measurement
  • Why Psychological Testing Matters to You
  • Observable Behavior and Unobservable Psychological Attributes
  • Psychological Tests: Definition and Types
    • What Is a Psychological Test?
    • Types of Tests
  • What Is Psychometrics?
    • Psychometrics
    • A Brief History of Psychometrics
  • Challenges to Measurement in Psychology
  • The Importance of Individual Differences
  • But Psychometrics Goes Well Beyond “Differential” Psychology
  • Suggested Readings
• PART I • BASIC CONCEPTS IN MEASUREMENT
  • Chapter 2 • Scaling
    • Fundamental Issues With Numbers
      • The Property of Identity
      • The Property of Order
      • The Property of Quantity
      • The Number 0
    • Units of Measurement
    • Additivity and Counting
      • Additivity
      • Counts: When Do They Qualify as Measurement?
    • Four Scales of Measurement
      • Nominal Scales
      • Ordinal Scales
      • Interval Scales
      • Ratio Scales
    • Scales of Measurement: Practical Implications
    • Additional Issues Regarding Scales of Measurement
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
  • Chapter 3 • Differences, Consistency, and the Meaning of Test Scores
    • The Nature of Variability
    • Importance of Individual Differences
    • Variability and Distributions of Scores
      • Central Tendency
      • Variability
      • Distribution Shapes and Normal Distributions
    • Quantifying the Association or Consistency Between Distributions
      • Interpreting the Association Between Two Variables
      • Scatterplots: Visually Representing the Association Between Two Variables
      • Covariance
      • Correlation
    • Variance and Covariance for “Composite Variables”
    • Binary Items
    • Interpreting Test Scores
      • Needed: An Interpretive Frame of Reference
      • z Scores (Standard Scores)
      • Converted Standard Scores (Standardized Scores)
      • Percentile Ranks
      • Normalized Scores
    • Test Norms
      • Representativeness of the Reference Sample
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
  • Chapter 4 • Test Dimensionality and Factor Analysis
    • Test Dimensionality
      • Three Dimensionality Questions: What They Are and Why They Matter
      • Unidimensional Tests
      • Multidimensional Tests With Correlated Dimensions (Tests With Higher-Order Factors)
      • Multidimensional Tests With Uncorrelated Dimensions
      • The Psychological Meaning of Test Dimensions
    • Factor Analysis: Examining the Dimensionality of a Test
      • The Logic and Purpose of Exploratory Factor Analysis: A Conceptual Overview
      • Conducting and Interpreting an Exploratory Factor Analysis
      • A Deeper Perspective on Factors, Factor Loadings, and Rotation
      • Factor Analysis of Binary Items
      • A Quick Look at Confirmatory Factor Analysis
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
• PART II • RELIABILITY
  • Chapter 5 • Reliability: Conceptual Basis
    • Overview of Reliability and Classical Test Theory
    • Observed Scores, True Scores, and Measurement Error
    • Variances in Observed Scores, True Scores, and Error Scores
    • Four Ways to Think of Reliability
      • Reliability as the Ratio of True Score Variance to Observed Score Variance
      • Reliability as Lack of Error Variance
      • Reliability as the (Squared) Correlation Between Observed Scores and True Scores
      • Reliability as the Lack of (Squared) Correlation Between Observed Scores and Error Scores
    • Reliability and the Standard Error of Measurement
    • From Theory to Practice: Measurement Models and Their Implications for Estimating Reliability
      • Overview of Key Assumptions
      • Parallel Tests
      • Tau-Equivalent and Essentially Tau-Equivalent Tests
      • Congeneric Tests
      • Tests With Correlated Errors
      • Summary
    • Domain Sampling Theory
    • Summary
    • Suggested Readings
  • Chapter 6 • Empirical Estimates of Reliability
    • Alternate Forms Method of Estimating Reliability
    • Test–Retest Method of Estimating Reliability
    • Internal Consistency Method of Estimating Reliability
      • Split-Half Estimates of Reliability
      • “Raw” Coefficient Alpha
      • “Standardized” Coefficient Alpha
      • Raw Alpha for Binary Items: KR20
      • Omega
      • On the Assumptions Underlying Alpha and Omega, the Relative Applicability of Those Indices, and Their Limitations
      • Internal Consistency Versus Dimensionality
      • Factors Affecting the Reliability of Test Scores
    • Sample Heterogeneity and Reliability Generalization
    • Reliability of Difference Scores
      • Defining Difference Scores
      • Estimating the Reliability of Difference Scores
      • Factors Affecting the Reliability of Difference Scores
      • The Problem of Unequal Variability
      • Difference Scores: Summary and Caution
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
    • Note
  • Chapter 7 • The Importance of Reliability
    • Applied Behavioral Practice: Evaluation of an Individual’s Test Score
      • Point Estimates of True Scores
      • Confidence Intervals
      • Debate and Alternatives
      • Summary
    • Behavioral Research
      • Reliability, True Associations, and Observed Associations
      • Measurement Error (Low Reliability) Attenuates the Observed Associations Between Measures
      • Reliability, Effect Sizes, and Statistical Significance
      • Implications for Conducting and Interpreting Behavioral Research
      • Summary
    • Test Construction and Refinement
      • Item Discrimination and Other Information Regarding Internal Consistency
      • Item Difficulty (Mean) and Item Variance
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
• PART III • VALIDITY
  • Chapter 8 • Validity: Conceptual Basis
    • What Is Validity?
    • The Importance of Validity
    • Validity Evidence: Test Content
      • Expert Rating Evidence
      • Threats to Content Validity
      • Content Validity Versus Face Validity
    • Validity Evidence: Internal Structure of the Test
      • Factor-Analytic Evidence
    • Validity Evidence: Response Processes
      • Direct Evidence
      • Indirect Evidence
    • Validity Evidence: Associations With Other Variables
      • Convergent Evidence
      • Discriminant Evidence
      • Criterion, Concurrent, and Predictive Evidence
    • Validity Evidence: Consequences of Testing
      • Evidence of Intended Effects
      • Evidence Regarding Unintended Differential Impact on Groups
      • Evidence Regarding Unintended Systemic Effects
    • Other Perspectives on Validity
    • Contrasting Reliability and Validity
    • Summary
    • Suggested Readings
  • Chapter 9 • Estimating and Evaluating Convergent and Discriminant Validity Evidence
    • A Construct’s Nomological Network
    • Methods for Evaluating Convergent and Discriminant Validity
      • Focused Associations
      • Sets of Correlations
      • Multitrait–Multimethod Matrices
      • Quantifying Construct Validity
    • Factors Affecting a Validity Coefficient
      • Associations Between Constructs
      • Random Measurement Error and Reliability
      • Restricted Range
      • Skew and Relative Proportions
      • Method Variance
      • Time
      • Predictions of Single Events
    • Interpreting a Validity Coefficient
      • Squared Correlations and “Variance Explained”
      • Estimating Practical Effects: Binomial Effect Size Display, Taylor-Russell Tables, Utility Analysis, and Sensitivity/Specificity
      • Guidelines or Norms for a Field
      • Statistical Significance
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
    • Notes
• PART IV • THREATS TO PSYCHOMETRIC QUALITY
  • Chapter 10 • Response Biases
    • Types of Response Biases
      • Acquiescence Bias (“Yea-Saying and Nay-Saying”)
      • Extreme and Moderate Responding
      • Social Desirability (“Faking Good”)
      • Malingering (“Faking Bad”)
      • Careless or Random Responding
      • Guessing
    • Methods for Coping With Response Biases
      • Minimizing the Existence of Bias by Managing the Testing Context
      • Minimizing the Existence of Bias by Managing Test Content
      • Minimizing the Effects of Bias by Managing Test Content or Scoring
      • Managing Test Content to Detect Bias and Intervene
      • Using Specialized Tests to Detect Bias and Intervene
    • Response Biases, Response Sets, and Response Styles
    • Summary
    • Suggested Readings
  • Chapter 11 • Test Bias
    • Why Worry About Test Score Bias?
    • Detecting Construct Bias: Internal Evaluation of a Test
      • Reliability
      • Rank Order
      • Item Discrimination Index
      • Factor Analysis
      • Differential Item Functioning Analyses
      • Summary
    • Detecting Predictive Bias: External Evaluation of a Test
      • Basics of Regression Analysis
      • One Size Fits All: The Common Regression Equation
      • Intercept Bias
      • Slope Bias
      • Intercept and Slope Bias
      • Criterion Score Bias
      • The Effect of Reliability
    • Other Statistical Procedures
    • Test Fairness
    • Example: Is the SAT Biased in Terms of Race or Socioeconomic Status?
      • Race/Ethnicity
      • Socioeconomic Status
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
    • Notes
• PART V • ADVANCED PSYCHOMETRIC APPROACHES
  • Chapter 12 • Confirmatory Factor Analysis
    • On the Use of EFA and CFA
      • The Frequency and Roles of EFA and CFA
      • Using CFA to Evaluate Measurement Models
    • The Process of CFA for Analysis of a Scale’s Internal Structure
      • Overview of CFA and an Example
      • Preliminary Steps
      • Step 1: Specification of the Measurement Model
      • Step 2: Computations
      • Step 3: Interpreting and Reporting Output
      • Step 4: Model Modification and Reanalysis (If Necessary)
      • Comparing Models
      • Summary
    • CFA and Reliability
      • Evaluating Types of Classical Test Theory Measurement Models
      • Estimating Reliability (Omega Index)
    • CFA and Validity
    • CFA and Measurement Invariance
      • The Meaning of Measurement Invariance
      • Levels of Invariance: Meaning and Detection
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
  • Chapter 13 • Generalizability Theory
    • Multiple Facets of Measurement
    • Generalizability, Universes, and Variance Components
    • G Studies and D Studies
    • Conducting and Interpreting Generalizability Theory Analysis: A One-Facet Design
      • Phase 1: G Study
      • Phase 2: D Study
    • Conducting and Interpreting Generalizability Theory Analysis: A Two-Facet Design
      • Phase 1: G Study
      • Phase 2: D Study
    • Other Measurement Designs
      • Number of Facets
      • Random Versus Fixed Facets
      • Crossed Versus Nested Designs
      • Relative Versus Absolute Decisions
    • A Practical, Consistency-Oriented Interpretation of Variance Components
      • Systematic Variance Components Reflect “Consistent Variance”
      • Residual/Error Variance Component Reflects Inconsistent Variance
      • Generalizability Coefficients as the Proportion of Variance That Is Consistent
      • More Complex Designs
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
    • Notes
  • Chapter 14 • Item Response Theory and Rasch Models
    • Factors Affecting Responses to Test Items
      • Respondent Trait Level as a Determinant of Item Responses
      • Item Difficulty as a Determinant of Item Responses
      • Item Discrimination as a Determinant of Item Responses
      • Guessing
    • IRT Measurement Models
      • One-Parameter Logistic Model (or Rasch Model)
      • Two-Parameter Logistic Model
      • Three-Parameter Logistic Model
      • Graded Response Model
    • Obtaining Parameter Estimates: A 1PL Example
    • Model Fit
    • Item and Test Information
      • Item Characteristic Curves
      • Item Information and Test Information
    • Applications of IRT
      • Test Development and Improvement
      • Differential Item Functioning
      • Person Fit
      • Computerized Adaptive Testing
    • Technical Appendix: R Syntax
    • Summary
    • Suggested Readings
• Glossary
• References
• Index

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