Efnisyfirlit

• Preface
• Acknowledgments
• About the Authors
• Chapter 1 • Overview
  • General Perspectives on Measurement
  • Historical Origins of Measurement in Social Science
    • Early Examples
    • Emergence of Statistical Methods and the Role of Mental Testing
    • The Role of Psychophysics
  • Later Developments in Measurement
    • Evolution of Basic Concepts
    • Evolution of Mental Testing
    • Assessment of Mental Illness
    • Broadening the Domain of Psychometrics
  • The Role of Measurement in the Social Sciences
    • The Relationship of Theory to Measurement
    • Theoretical and Atheoretical Measures
    • Composite Measurement Tools
    • All Scales Are Not Created Equal
    • Costs of Poor Measurement
  • Summary and Preview
  • Exercises
• Chapter 2 • Understanding the Latent Variable
  • Constructs Versus Measures
  • Latent Variable as the Presumed Cause of Scale Item Values
  • Path Diagrams
    • Diagrammatic Conventions
    • Path Diagrams in Scale Development
  • Further Elaboration of the Measurement Model
    • Classical Measurement Assumptions
  • Parallel Tests
  • Alternative Models
  • Choosing a Causal Model
  • Exercises
  • Note
• Chapter 3 • Scale Reliability
  • Methods Based on the Analysis of Variance
  • Continuous Versus Dichotomous Items
  • Internal Consistency
  • Coefficient Alpha
    • The Covariance Matrix
    • Covariance Matrices for Multi-Item Scales
    • Alpha and the Covariance Matrix
    • Alternative Formula for Alpha
    • Critique of Alpha
  • Remedies to Alpha’s Limitations
  • Coefficient Omega (ω)
  • Reliability Based on Correlations Between Scale Scores
    • Alternate-Forms Reliability
    • Split-Half Reliability
    • Inter-Rater Agreement
    • Temporal Stability
    • Reliability of Change Scores
  • Reliability and Statistical Power
  • Generalizability Theory
  • Summary
  • Exercises
  • Notes
• Chapter 4 • Scale Validity
  • Content Validity
    • Scope of the Variable and Implications for Content Validity
  • Criterion-Related Validity
    • Criterion-Related Validity Versus Accuracy
  • Construct Validity
    • Differentiating Construct From Criterion-Related Validity
    • Attenuation
    • How Strong Should Correlations Be to Demonstrate Construct Validity?
    • Multitrait-Multimethod Matrix
  • What About Face Validity?
  • Exercises
• Chapter 5 • Guidelines in Scale Development
  • Step 1: Determine Clearly What It Is You Want to Measure
    • Theory as an Aid to Clarity
    • Specificity as an Aid to Clarity
    • Being Clear About What to Include in a Measure
  • Step 2: Generate an Item Pool
    • Choose Items That Reflect the Scale’s Purpose
    • Redundancy
    • Number of Items
    • Beginning the Process of Writing Items
    • Characteristics of Good and Bad Items
    • Positively and Negatively Worded Items
    • Conclusion
  • Step 3: Determine the Format for Measurement
    • Thurstone Scaling
    • Guttman Scaling
    • Scales With Equally Weighted Items
    • How Many Response Categories?
    • Specific Types of Response Formats
      • Likert Scale
      • Semantic Differential
      • Visual Analog
      • Pictorial Response Options
      • Numerical Response Formats and Basic Neural Processes
      • Binary Options
      • Item Time Frames
    • Mode of Administration
  • Step 4: Have Initial Item Pool Reviewed by Experts
  • Step 5: Cognitive Interviewing
  • Step 6: Consider Inclusion of Validation Items
  • Step 7: Administer Items to a Development Sample
  • Step 8: Evaluate the Items
    • Initial Examination of Items’ Performance
      • Reverse Scoring
      • Item-Scale Correlations
      • Item Variances
      • Item Means
    • Dimensionality
    • Reliability
  • Step 9: Optimize Scale Length
    • Effect of Scale Length on Reliability
    • Effects of Dropping “Bad” Items
    • Tinkering With Scale Length
    • Split Samples
  • Exercises
  • Note
• Chapter 6 • Factor Analysis
  • Overview of Factor Analysis
    • Examples of Methods Analogous to Factor Analytic Concepts
      • Example 1
      • Example 2
      • Shortcomings of These Methods
  • Conceptual Description of Factor Analysis
    • Extracting Factors
      • The First Factor
      • Subsequent Factors
      • Deciding How Many Factors to Extract
    • Rotating Factors
      • Rotation Analogy 1
      • Rotation Analogy 2
      • Rotation Analogy 3
      • Orthogonal Versus Oblique Rotation
      • Choosing Type of Rotation
  • Bifactor and Hierarchical Factor Models
  • Interpreting Factors
  • Principal Components Versus Common Factors
    • Same or Different?
  • Confirmatory Factor Analysis
  • Using Factor Analysis in Scale Development
  • Sample Size
  • Conclusion
  • Exercises
• Chapter 7 • The Index
  • How an Index Differs From a Scale
    • The Two Distinct Types of Index
      • Causal Formative Measures
      • Composite Formative Measures
    • Other Conceptual Differences Between Scales and Indices
    • Empirical Differences Between Scales and Indices
      • Relationships Among Indicators
      • Impact of Adding Items
  • Rules of Thumb for Differentiating an Index From a Scale
    • Limitations of Conceptual Criteria
    • Limitations of Empirical Criteria
  • Is It a Scale or an Index? Formal Methods for Distinguishing Effect and Causal Indicators
    • The Correlation Matrix
    • Factor Analysis
    • Vanishing Tetrads
  • Steps in Developing and Evaluating an Index
    • Index Item Development
      • The Role of Theory
      • Index Item Creation
      • Index Item Redundancy and Number
      • Other Index Development Considerations
    • Evaluating Items
      • Regression as a Heuristic for Index Development
      • Regression as an Alternative to Index Development
      • Regression as a Method for Index Development and Validation
      • Regression-Based Examples
    • Index Validity
      • Content Validity
      • Construct Validity
      • Criterion Validity
      • Group Comparison Approach
    • Index Reliability
  • Hybrid Measures
    • Hierarchical Hybrids
    • Hierarchical Hybrid Indices and Multidimensional Scales
    • Hybrids Involving Nonhierarchical Heterogeneous Indicators
  • Methods Based on Structural Equation Modeling
    • Heuristic Overview of Structural Equation Modeling
    • MIMIC Models
  • Criticisms of Index Composites
  • Exercises
  • Note
• Chapter 8 • An Overview of Item Response Theory
  • Item Difficulty
  • Item Discrimination
  • Guessing, or False Positives
  • Item-Characteristic Curves
  • IRT Applied to Multiresponse Items
    • Theta and Computerized Adaptive Testing (CAT)
    • Complexities of IRT
    • Conclusions
  • Exercises
• Chapter 9 • Measurement in the Broader Research Context
  • Before Scale Development
    • Look for Existing Tools
    • View the Construct in the Context of the Population of Interest
    • Consider the Scale in the Context of Other Measures or Procedures
  • After Scale Administration
    • Analytic Issues
    • Interpretation Issues
    • Generalizability
  • Final Thoughts
    • Small Measurement and Big Measurement
    • Canoes and Cruise Ships
    • Measurement “Canoes” and Measurement “Cruise Ships”
    • Practical Implications of Small Versus Big Measurement
    • Remember, Measurement Matters
  • Exercise
• References
• Index

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