Measurement Theory and Applications for the Social Sciences

Námskeið PRÓ0176110 Próffræði - Höfundur: Deborah L. Bandalos
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Measurement Theory and Applications for the Social Sciences

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Efnisyfirlit

  • Half Title Page
  • Series Page
  • Title Page
  • Copyright
  • Dedication
  • Series Editor’s Note
  • Preface
  • Contents
  • Part I. Instrument Development and Analysis
    • 1. Introduction
      • Problems in Social Science Measurement
      • What is Measurement Theory?
      • Measurement Defined
        • The Nominal Level of Measurement
        • The Ordinal Level of Measurement
        • The Interval Level of Measurement
        • The Ratio Level of Measurement
        • Criticisms of Stevens’s Levels of Measurement
      • A Brief History of Testing
        • The Chinese Civil Service Examinations
        • Testing in Ancient Greece
        • Early European Testing
        • Testing in the United States
        • Testing in Business and Industry
        • Personality Assessment
      • Summary
      • Exercises
    • 2. Norms and Standardized Scores
      • Which to Use?
      • Norm Groups
        • Important Characteristics of the Norm Group: The “Three R’s”
      • Types of Norm-referenced Scores
        • Percentile Ranks
        • Standardized and Normalized Scores
        • Stanines
        • Normal Curve Equivalents
        • Developmental-Level Scores
      • Criterion-Referenced Testing
      • Summary
      • Exercises
    • 3. The Test Development Process
      • Steps in Scale Development
        • State the Purpose of the Scale
        • Identify and Define the Domain
        • Determine Whether a Measure Already Exists
        • Determine the Item Format
        • Write Out the Testing Objectives
        • Create the Initial Item Pool
        • Conduct the Initial Item Review
        • Conduct Preliminary Item Tryouts
        • Conduct a Large-Scale Field Test of Items
        • Prepare Guidelines for Administration
      • Summary
      • Exercises
    • 4. Writing Cognitive Items
      • Objective Item Types
        • Multiple-Choice Items
        • True–False Items
        • Matching Items
        • Short-Answer or Completion Items
      • Performance Assessments
        • Essay Questions
        • Performance Tasks
      • Summary
      • Exercises
    • 5. Writing Noncognitive Items
      • Noncognitive Item Types
        • Thurstone Scaling
        • Likert Scaling
        • Guttman Scaling
      • Theories of Item Responding
        • The Cognitive Process Model of Responding
        • Item Responses as Social Encounters
      • Problems in Measuring Noncognitive Outcomes
        • Response Distortion
        • Managing Response Distortion
      • Practical Issues in Noncognitive Scale Construction
        • Number of Scale Points
        • Labeling of Response Options
        • Inclusion of Negatively Oriented Items
        • Including a Neutral Option
      • Summary
      • Exercises
    • 6. Item Analysis for Cognitive and Noncognitive Items
      • Item Analysis for Cognitive Items
        • Item Difficulty
        • Item Discrimination
        • Evaluating the Distractors for Multiple-Choice Items
        • Corrections for Guessing
        • Summary of Analyses for Cognitive Items
      • Item Analysis for Noncognitive Items
        • Frequency Distributions and Descriptive Statistics
        • Interitem Correlations
        • Item–Total Correlations and Information from Reliability Analyses
        • Group Comparisons
        • Factor Analytic Methods
        • Summary of Analyses for Noncognitive Items
      • Use of Item Analysis Information
      • Exercises
  • Part II. Reliability and Validity
    • 7. Introduction to Reliability and the Classical Test Theory Model
      • What Is Reliability?
      • Measurement Error and CTT
      • More on CTT
        • Properties of True and Error Scores in CTT
      • The CTT Definition of Reliability
      • Correlation between True and Observed Scores: The Reliability Index
      • Parallel, Tau-Equivalent, and Congeneric Measures
      • Reliability as the Correlation between Scores on Parallel Tests
      • Summary
      • Exercises
    • 8. Methods of Assessing Reliability
      • Internal Consistency
        • Reliability of a Composite
        • The Spearman–Brown Prophecy Formula and Split-Half Reliability
        • Coefficient Alpha
        • Other Internal Consistency Coefficients
        • Recommended Values for Internal Consistency Indices
        • Factors Affecting Internal Consistency Coefficient Values
        • Computational Examples for Coefficient Alpha
      • Test–Retest Reliability
        • Factors Affecting Coefficients of Stability
        • Recommended Values for Coefficients of Stability
      • Alternate Forms Reliability
        • Factors Affecting Coefficients of Equivalence
        • Recommended Values for Coefficients of Equivalence
      • Combining Alternate Forms and Test–Retest Reliability
        • Factors Affecting Coefficients of Equivalence and Stability
        • Recommended Values for Coefficients of Equivalence and Stability
      • The Standard Error of Measurement
        • Factors Affecting the SEM
        • Using the SEM to Place Confidence Intervals around Scores
        • Sample Dependence of Reliability Coefficients and the SEM
      • Reliability of Difference Scores
      • Summary
      • Exercises
    • 9. Interrater Agreement and Reliability
      • Measures of Interrater Agreement
        • Nominal Agreement
        • Cohen’s Kappa
      • Measures of Interrater Reliability
        • Coefficient Alpha
        • Intraclass Correlation
      • Summary
      • Exercises
    • 10. Generalizability Theory
      • Basic Concepts and Terminology
      • Facets, Objects of Measurement, and Universe Scores
        • Crossed and Nested Facets
        • Random and Fixed Facets
      • G Studies and D Studies
      • The G Theory Model
      • Computation of Variance Components
        • Computation of Variance Components for a One-Facet Design
        • Computation of Variance Components for a Two-Facet Design
        • Variance Components for Nested Designs
        • Variance Components for Designs with Fixed Facets
      • Decision Studies
        • Relative and Absolute Interpretations
        • Calculating the G and Phi Coefficients
        • Use of the D Study to Determine the Optimal Test Design
        • Decision Studies with Nested or Fixed Facets
      • Summary
      • Exercises
    • 11. Validity
      • Validity Defined
      • Traditional Forms of Validity Evidence: A Historical Perspective
        • Original Validity Types
        • Arguments against the “Tripartite” View of Validity
      • Current Conceptualizations of Validity
        • The Unified View of Validity
        • Focus on Interpretation and Use of Test Scores
        • Focus on Explanation and Cognitive Models
        • Inclusion of Values and Test Consequences in the Validity Framework
      • Obtaining Evidence of Validity
        • Introduction to the Argument-Based Approach to Validity
        • Types of Validity Evidence
      • Summary
      • Exercises
  • Part III. Advanced Topics in Measurement Theory
    • 12. Exploratory Factor Analysis
      • The EFA–CFA Distinction
      • The EFA Model
        • The EFA Model: Diagrammatic Form
        • The EFA Model: Equation Form
      • Steps in Conducting EFA
        • Extracting the Factors
        • Determining the Number of Factors to Retain
        • Rotating the Factors
        • Interpreting the Factors
        • Data Requirements for EFA
        • Sample-Size Requirements
      • Summary
      • Exercises
    • 13. Confirmatory Factor Analysis
      • Differences between Exploratory and Confirmatory Factor Analyses
      • Advantages of CFA
      • CFA Model and Equations
      • Steps in Conducting a CFA
        • Model Specification
        • Model Identification
        • Estimation of Model Parameters
        • Model Testing
        • Respecification of the Model
      • Data Preparation and CFA Assumptions
        • Normality of Variable Distributions
        • Variable Scales
        • Outliers
        • Missing Data
        • Sampling Method
        • Sample Size
      • CFA-Based Reliability Estimation
        • Tests of Parameter Estimate Equivalence
        • Calculation of Coefficient Omega
      • Summary
      • Exercises
    • 14. Item Response Theory
      • Item Response Functions for IRT
      • IRT Models
        • The One-Parameter Logistic Model
        • The Two-Parameter Logistic Model
        • The Three-Parameter Logistic Model
        • IRT Models for Polytomous Items
      • Indeterminacy and Scaling
        • Scaling for the Rasch Model
        • Scaling for the 2PL and 3PL Models
      • Invariance of Parameter Estimates
      • Estimation
        • Maximum Likelihood Estimation
        • Bayesian Estimation Methods
      • Sample Size Requirements
      • Information, Standard Error of Measurement, and Reliability
        • Maximum Likelihood Estimation
        • EAP Estimation
      • IRT Assumptions
        • Correct Dimensionality
        • Local Independence
        • Functional Form
      • IRT Applications
        • Test Form Assembly
        • Equating
        • Computer Adaptive Testing Applications
        • Differential Item Functioning Applications
      • Summary
      • Exercises
    • 15. Diagnostic Classification Models
      • Categorical Latent Variables for DCMs
      • When to Use DCMs
      • Attribute Profiles
      • Diagnostic Classification Model: A Confirmatory Latent Class Model
      • The Latent Class Model
      • IRFs for DCMs
      • The Log-Linear Cognitive Diagnosis Model: A General DCM
        • Link Functions for DCMs
        • The Q-Matrix
        • IRF for Complex Structure Items
        • Fully Extending the IRF for the LCDM
        • Other General DCMs
      • Submodels of the LCDM
        • The Deterministic Inputs Noisy And Gate Model
        • The Compensatory Reparameterized Unified Model
        • The Deterministic Inputs Noisy Or Gate Model
        • Other Models
        • Which Model Should I Use?
      • Examinee Classifications
      • Summary
      • Exercises
    • 16. Bias, Fairness, and Legal Issues in Testing
      • Impact, Item and Test Bias, Differential Item Functioning, and Fairness Defined
      • Detecting Test and Item Bias
        • Test Bias
        • Item Bias
      • Choosing a DIF Detection Method
        • Purification of the Matching Variable
      • Interpretation of DIF and Test Bias
        • DIF as Construct-Irrelevant Variance
        • Sources of Test Bias
      • Test Fairness
        • Universal Design
        • Accommodations and Modifications
        • Need for More Research on DIF and Test Bias
        • Sensitivity Reviews
      • Legal Issues in Testing
        • Legislation under Which Tests Can Be Challenged
        • Court Cases Relevant to Testing
      • Summary
      • Exercises
    • 17. Standard Setting
      • Common Elements of Standard-Setting Procedures
        • Step 1: Select a Standard-Setting Procedure
        • Step 2: Choose the Panelists
        • Step 3: Prepare Descriptions of Each Performance Category
        • Step 4: Train the Panelists to Use the Chosen Procedure
        • Step 5: Collect Panelists’ Judgments
        • Step 6: Provide Panelists with Feedback and Discuss
        • Step 7: Collect a Second Set of Judgments and Create Recommended Cut Scores
        • Step 8: Conduct an Evaluation of the Standard-Setting Process
        • Step 9: Compile a Technical Report, Including Validity Evidence
      • Standard-Setting Procedures
        • The Angoff Method
        • The Bookmark Procedure
        • The Contrasting Groups Method
        • The Borderline Group Method
        • The Body of Work Method
      • Validity Evidence for Standard Setting
        • Procedural Evidence
        • Internal Evidence
        • External Evidence
      • Summary
      • Exercises
    • 18. Test Equating
      • Equating Defined
        • Alternatives to Equating
      • Equating Designs
        • Single-Group Design
        • Random-Groups Design
        • Common Item Nonequivalent Groups Design
      • Methods of Equating
        • Mean Equating
        • Linear Equating
        • Equipercentile Equating
        • IRT Equating Methods
      • Practical Considerations in Equating
        • Guidelines for Choosing Common Items
        • Error in Equating
        • Sample-Size Requirements
        • Systematic Equating Error
        • Choice of Equating Method
      • Summary
      • Exercises
  • Answers to Exercises
  • References
  • Author Index
  • Subject Index
  • About the Author

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