Psychometrics

Höfundur R. Michael Furr

Útgefandi SAGE Publications, Inc. (US)

Snið ePub

Print ISBN 9781071824078

Útgáfa 4

Útgáfuár 2022

8.390 kr.

Description

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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