Psychometrics

Michael Furr is Professor of Psychology and Wright Faculty Fellow at Wake Forest University, where he teaches and conducts research in personality psychology, psychological measurement, and quantitative methods. He earned a BA from the College of William and Mary, an MS from Villanova University, and a PhD from the University of California at Riverside. He is an editor of the “Statistical Developments and Applications” section of the Journal of Personality Assessment, a former associate editor of the Journal of Research in Personality, a former executive editor of the Journal of Social Psychology, and a consulting editor for several other scholarly journals. He received Wake Forest University’s 2012 Award for Excellence in Research, and he won the Society for Personality Assessment’s 2017 Bruno Klopfer Award for Distinguished Contributions to the Literature in Personality Assessment,. He is a fellow of Divisions 5 (Quantitative and Qualitative Methods) and 8 (Social and Personality Psychology) of the American Psychological Association, a fellow of the Association for Psychological Science, and a fellow of the Society for Personality and Social Psychology.

• PrefaceThe Conceptual Orientation of This Book, Its Purpose, and the Intended AudienceOrganizational OverviewNew to This EditionAuthor’s AcknowledgmentsPublisher’s AcknowledgmentsAbout the AuthorChapter 1. Psychometrics and the Importance of Psychological MeasurementWhy Psychological Testing Matters to YouObservable Behavior and Unobservable Psychological AttributesPsychological Tests: Definition and TypesWhat Is Psychometrics?Challenges to Measurement in PsychologyThe Importance of Individual DifferencesBut Psychometrics Goes Well Beyond “Differential” PsychologySuggested ReadingsPART I. BASIC CONCEPTS IN MEASUREMENTChapter 2. ScalingFundamental Issues With NumbersUnits of MeasurementAdditivity and CountingFour Scales of MeasurementScales of Measurement: Practical ImplicationsAdditional Issues Regarding Scales of MeasurementTechnical Appendix: R SyntaxSummarySuggested ReadingsChapter 3. Differences, Consistency, and the Meaning of Test ScoresThe Nature of VariabilityImportance of Individual DifferencesVariability and Distributions of ScoresQuantifying the Association or Consistency Between DistributionsVariance and Covariance for “Composite Variables”Binary ItemsInterpreting Test ScoresTest NormsTechnical Appendix: R SyntaxSummarySuggested ReadingsChapter 4. Test Dimensionality and Factor AnalysisTest DimensionalityFactor Analysis: Examining the Dimensionality of a TestTechnical Appendix: R SyntaxSummarySuggested ReadingsPART II. RELIABILITYChapter 5. Reliability: Conceptual BasisOverview of Reliability and Classical Test TheoryObserved Scores, True Scores, and Measurement ErrorVariances in Observed Scores, True Scores, and Error ScoresFour Ways to Think of ReliabilityReliability and the Standard Error of MeasurementFrom Theory to Practice: Measurement Models and Their Implications for Estimating ReliabilityDomain Sampling TheorySummarySuggested ReadingsChapter 6. Empirical Estimates of ReliabilityAlternate Forms Method of Estimating ReliabilityTest–Retest Method of Estimating ReliabilityInternal Consistency Method of Estimating ReliabilitySample Heterogeneity and Reliability GeneralizationReliability of Difference ScoresTechnical Appendix: R SyntaxSummarySuggested ReadingsNoteChapter 7. The Importance of ReliabilityApplied Behavioral Practice: Evaluation of an Individual’s Test ScoreBehavioral ResearchTest Construction and RefinementTechnical Appendix: R SyntaxSummarySuggested ReadingsPART III. VALIDITYChapter 8. Validity: Conceptual BasisWhat Is Validity?The Importance of ValidityValidity Evidence: Test ContentValidity Evidence: Internal Structure of the TestValidity Evidence: Response ProcessesValidity Evidence: Associations With Other VariablesValidity Evidence: Consequences of TestingOther Perspectives on ValidityContrasting Reliability and ValiditySummarySuggested ReadingsChapter 9. Estimating and Evaluating Convergent and Discriminant Validity EvidenceA Construct’s Nomological NetworkMethods for Evaluating Convergent and Discriminant ValidityFactors Affecting a Validity CoefficientInterpreting a Validity CoefficientTechnical Appendix: R SyntaxSummarySuggested ReadingsNotesPART IV. THREATS TO PSYCHOMETRIC QUALITYChapter 10. Response BiasesTypes of Response BiasesMethods for Coping With Response BiasesResponse Biases, Response Sets, and Response StylesSummarySuggested ReadingsChapter 11. Test BiasWhy Worry About Test Score Bias?Detecting Construct Bias: Internal Evaluation of a TestDetecting Predictive Bias: External Evaluation of a TestOther Statistical ProceduresTest FairnessExample: Is the SAT Biased in Terms of Race or Socioeconomic Status?Technical Appendix: R SyntaxSummarySuggested ReadingsNotesPART V. ADVANCED PSYCHOMETRIC APPROACHESChapter 12. Confirmatory Factor AnalysisOn the Use of EFA and CFAThe Process of CFA for Analysis of a Scale’s Internal StructureCFA and ReliabilityCFA and ValidityCFA and Measurement InvarianceTechnical Appendix: R SyntaxSummarySuggested ReadingsChapter 13. Generalizability TheoryMultiple Facets of MeasurementGeneralizability, Universes, and Variance ComponentsG Studies and D StudiesConducting and Interpreting Generalizability Theory Analysis: A One-Facet DesignConducting and Interpreting Generalizability Theory Analysis: A Two-Facet DesignOther Measurement DesignsA Practical, Consistency-Oriented Interpretation of Variance ComponentsTechnical Appendix: R SyntaxSummarySuggested ReadingsNotesChapter 14. Item Response Theory and Rasch ModelsFactors Affecting Responses to Test ItemsIRT Measurement ModelsObtaining Parameter Estimates: A 1PL ExampleModel FitItem and Test InformationApplications of IRTTechnical Appendix: R SyntaxSummarySuggested ReadingsGlossaryReferencesIndex