Psychometrics : an introduction / R. Michael Furr, Wake Forest University.

Enhanced descriptions from Syndetics:

In this fully revised Fourth Edition of Psychometrics: An Introduction, author R. Michael Furr centers his presentation around a conceptual understanding of psychometric core issues, such as scales, reliability, and validity. Focusing on purpose rather than procedure and the "why" rather than the "how to," this accessible book uses a wide variety of examples from behavioral science research so readers can see the importance of psychometric fundamentals in research. By emphasizing concepts, logic, and practical applications over mathematical proofs, this book gives students an appreciation of how measurement problems can be addressed and why it is important to address them. The book offers readers the most contemporary views of topics in psychometrics available in the nontechnical psychometric literature.

Table of contents provided by Syndetics

• Preface (p. xiii)
• The Conceptual Orientation of This Book, Its Purpose, and the Intended Audience (p. xiii)
• Organizational Overview (p. xiv)
• New to This Edition (p. xvi)
• General Changes (p. xvi)
• Chapter-Specific Changes (p. xviii)
• Author's Acknowledgments (p. xxi)
• Publisher's Acknowledgments (p. xxii)
• About the Author (p. xxiv)
• Chapter 1 Psychometrics and the Importance of Psychological Measurement (p. 1)
• Why Psychological Testing Matters to You (p. 2)
• Observable Behavior and Unobservable Psychological Attributes (p. 4)
• Psychological Tests: Definition and Types (p. 7)
• What Is a Psychological Test? (p. 7)
• Types of Tests (p. 8)
• What Is Psychometrics? (p. 11)
• Psychometrics (p. 11)
• A Brief History of Psychometrics (p. 11)
• Challenges to Measurement in Psychology (p. 13)
• The Importance of Individual Differences (p. 18)
• But Psychometrics Goes Well Beyond "Differential" Psychology (p. 19)
• Suggested Readings (p. 20)
• Part I Basic Concepts in Measurement (p. 21)
• Chapter 2 Scaling (p. 23)
• Fundamental Issues With Numbers (p. 24)
• The Property of Identity (p. 25)
• The Property of Order (p. 26)
• The Property of Quantity (p. 27)
• The Number 0 (p. 28)
• Units of Measurement (p. 30)
• Additivity and Counting (p. 32)
• Additivity (p. 32)
• Counts: When Do They Qualify as Measurement? (p. 34)
• Four Scales of Measurement (p. 35)
• Nominal Scales (p. 35)
• Ordinal Scales (p. 36)
• Interval Scales (p. 37)
• Ratio Scales (p. 38)
• Scales of Measurement: Practical Implications (p. 39)
• Additional Issues Regarding Scales of Measurement (p. 40)
• Technical Appendix: R Syntax (p. 41)
• Summary (p. 46)
• Suggested Readings (p. 47)
• Chapter 3 Differences, Consistency, and the Meaning of Test Scores (p. 49)
• The Nature of Variability (p. 49)
• Importance of Individual Differences (p. 50)
• Variability and Distributions of Scores (p. 52)
• Central Tendency (p. 54)
• Variability (p. 54)
• Distribution Shapes and Normal Distributions (p. 58)
• Quantifying the Association or Consistency Between Distributions (p. 61)
• Interpreting the Association Between Two Variables (p. 61)
• Scatterplots: Visually Representing the Association Between Two Variables (p. 62)
• Covariance (p. 64)
• Correlation (p. 68)
• Variance and Covariance for "Composite Variables" (p. 69)
• Binary Items (p. 71)
• Interpreting Test Scores (p. 74)
• Needed: An Interpretive Frame of Reference (p. 75)
• z Scores (Standard Scores) (p. 77)
• Converted Standard Scores (Standardized Scores) (p. 81)
• Percentile Ranks (p. 82)
• Normalized Scores (p. 85)
• Test Norms (p. 86)
• Representativeness of the Reference Sample (p. 87)
• Technical Appendix: R Syntax (p. 88)
• Summary (p. 94)
• Suggested Readings (p. 95)
• Chapter 4 Test Dimensionality and Factor Analysis (p. 97)
• Test Dimensionality (p. 99)
• Three Dimensionality Questions: What They Are and Why They Matter (p. 100)
• Unidimensional Tests (p. 101)
• Multidimensional Tests With Correlated Dimensions (Tests With Higher-Order Factors) (p. 103)
• Multidimensional Tests With Uncorrelated Dimensions (p. 105)
• The Psychological Meaning of Test Dimensions (p. 106)
• Factor Analysis: Examining the Dimensionality of a Test (p. 107)
• The Logic and Purpose of Exploratory Factor Analysis: A Conceptual Overview (p. 107)
• Conducting and Interpreting an Exploratory Factor Analysis (p. 110)
• A Deeper Perspective on Factors, Factor Loadings, and Rotation (p. 126)
• Factor Analysis of Binary Items (p. 132)
• A Quick Look at Confirmatory Factor Analysis (p. 133)
• Technical Appendix: R Syntax (p. 134)
• Summary (p. 139)
• Suggested Readings (p. 140)
• Part II Reliability (p. 141)
• Chapter 5 Reliability: Conceptual Basis (p. 143)
• Overview of Reliability and Classical Test Theory (p. 145)
• Observed Scores, True Scores, and Measurement Error (p. 147)
• Variances in Observed Scores, True Scores, and Error Scores (p. 150)
• Four Ways to Think of Reliability (p. 154)
• Reliability as the Ratio of True Score Variance to Observed Score Variance (p. 155)
• Reliability as Lack of Error Variance (p. 157)
• Reliability as the (Squared) Correlation Between Observed Scores and True Scores (p. 159)
• Reliability as the Lack of (Squared) Correlation Between Observed Scores and Error Scores (p. 161)
• Reliability and the Standard Error of Measurement (p. 163)
• From Theory to Practice: Measurement Models and Their Implications for Estimating Reliability (p. 165)
• Overview of Key Assumptions (p. 166)
• Parallel Tests (p. 170)
• Tau-Equivalent and Essentially Tau-Equivalent Tests (p. 173)
• Congeneric Tests (p. 176)
• Tests With Correlated Errors (p. 177)
• Summary (p. 178)
• Domain Sampling Theory (p. 178)
• Summary (p. 179)
• Suggested Readings (p. 180)
• Chapter 6 Empirical Estimates of Reliability (p. 181)
• Alternate Forms Method of Estimating Reliability (p. 183)
• Test-Retest Method of Estimating Reliability (p. 186)
• Internal Consistency Method of Estimating Reliability (p. 190)
• Split-Half Estimates of Reliability (p. 191)
• "Raw" Coefficient Alpha (p. 195)
• "Standardized" Coefficient Alpha (p. 201)
• Raw Alpha for Binary items: KR 20 (p. 203)
• Omega (p. 205)
• On the Assumptions Underlying Alpha and Omega, the Relative Applicability of Those Indices, and Their Limitations (p. 205)
• Internal Consistency Versus Dimensionality (p. 209)
• Factors Affecting the Reliability of Test Scores (p. 209)
• Sample Heterogeneity and Reliability Generalization (p. 216)
• Reliability of Difference Scores (p. 217)
• Defining Difference Scores (p. 218)
• Estimating the Reliability of Difference Scores (p. 220)
• Factors Affecting the Reliability of Difference Scores (p. 221)
• The Problem of Unequal Variability (p. 222)
• Difference Scores: Summary and Caution (p. 226)
• Technical Appendix: R Syntax (p. 228)
• Summary (p. 233)
• Suggested Readings (p. 234)
• Note (p. 235)
• Chapter 7 The Importance of Reliability (p. 237)
• Applied Behavioral Practice: Evaluation of an Individual's Test Score (p. 237)
• Point Estimates of True Scores (p. 238)
• Confidence Intervals (p. 242)
• Debate and Alternatives (p. 244)
• Summary (p. 245)
• Behavioral Research (p. 246)
• Reliability, True Associations, and Observed Associations (p. 246)
• Measurement Error (Low Reliability) Attenuates the Observed Associations Between Measures (p. 249)
• Reliability, Effect Sizes, and Statistical Significance (p. 254)
• Implications for Conducting and Interpreting Behavioral Research (p. 259)
• Summary (p. 263)
• Test Construction and Refinement (p. 263)
• Item Discrimination and Other Information Regarding Internal Consistency (p. 265)
• Item Difficulty (Mean) and Item Variance (p. 270)
• Technical Appendix: R Syntax (p. 271)
• Summary (p. 277)
• Suggested Readings (p. 278)
• Part III Validity (p. 279)
• Chapter 8 Validity: Conceptual Basis (p. 281)
• What is Validity? (p. 282)
• The Importance of Validity (p. 287)
• Validity Evidence: Test Content (p. 289)
• Expert Rating Evidence (p. 290)
• Threats to Content Validity (p. 292)
• Content Validity Versus Face Validity (p. 293)
• Validity Evidence: internal Structure of the Test (p. 294)
• Factor-Analytic Evidence (p. 296)
• Validity Evidence: Response Processes (p. 299)
• Direct Evidence (p. 302)
• Indirect Evidence (p. 303)
• Validity Evidence: Associations With Other Variables (p. 304)
• Convergent Evidence (p. 306)
• Discriminant Evidence (p. 307)
• Criterion, Concurrent, and Predictive Evidence (p. 309)
• Validity Evidence: Consequences of Testing (p. 310)
• Evidence of Intended Effects (p. 312)
• Evidence Regarding Unintended Differential Impact on Groups (p. 313)
• Evidence Regarding Unintended Systemic Effects (p. 314)
• Other Perspectives on Validity (p. 316)
• Contrasting Reliability and Validity (p. 319)
• Summary (p. 320)
• Suggested Readings (p. 321)
• Chapter 9 Estimating and Evaluating Convergent and Discriminant Validity Evidence (p. 323)
• A Construct's Nomological Network (p. 324)
• Methods for Evaluating Convergent and Discriminant Validity (p. 326)
• Focused Associations (p. 327)
• Sets of Correlations (p. 330)
• Multitrait-Multimethod Matrices (p. 333)
• Quantifying Construct Validity (p. 341)
• Factors Affecting a Validity Coefficient (p. 347)
• Associations Between Constructs (p. 348)
• Random Measurement Error and Reliability (p. 348)
• Restricted Range (p. 350)
• Skew and Relative Proportions (p. 356)
• Method Variance (p. 361)
• Time (p. 362)
• Predictions of Single Events (p. 362)
• Interpreting a Validity Coefficient (p. 364)
• Squared Correlations and "Variance Explained" (p. 364)
• Estimating Practical Effects; Binomial Effect Size Display, Taylor-Russell Tables, Utility Analysis, and Sensitivity/Specificity (p. 367)
• Guidelines or Norms for a Field (p. 376)
• Statistical Significance (p. 378)
• Technical Appendix: R Syntax (p. 385)
• Summary (p. 390)
• Suggested Readings (p. 391)
• Notes (p. 392)
• Part IV Threats to Psychometric Quality (p. 393)
• Chapter 10 Response Biases (p. 395)
• Types of Response Biases (p. 396)
• Acquiescence Bias ("Yea-Saying and Nay-Saying") (p. 396)
• Extreme and Moderate Responding (p. 404)
• Social Desirability ("Faking Good") (p. 408)
• Malingering ("Faking Bad") (p. 415)
• Careless or Random Responding (p. 416)
• Guessing (p. 420)
• Methods for Coping With Response Biases (p. 421)
• Minimizing the Existence of Bias by Managing the Testing Context (p. 421)
• Minimizing the Existence of Bias by Managing Test Content (p. 423)
• Minimizing the Effects of Bias by Managing Test Content or Scoring (p. 426)
• Managing Test Content to Detect Bias and Intervene (p. 432)
• Using Specialized Tests to Detect Bias and Intervene (p. 435)
• Response Biases, Response Sets, and Response Styles (p. 437)
• Summary (p. 437)
• Suggested Readings (p. 438)
• Chapter 11 Test Bias (p. 441)
• Why Worry About Test Score Bias? (p. 443)
• Detecting Construct Bias: Internal Evaluation of a Test (p. 444)
• Reliability (p. 446)
• Rank Order (p. 447)
• Item Discrimination Index (p. 448)
• Factor Analysis (p. 450)
• Differential item Functioning Analyses (p. 452)
• Summary (p. 456)
• Detecting Predictive Bias: External Evaluation of a Test (p. 456)
• Basics of Regression Analysis (p. 458)
• One Size Fits All: The Common Regression Equation (p. 461)
• Intercept Bias (p. 462)
• Slope Bias (p. 466)
• Intercept and Slope Bias (p. 470)
• Criterion Score Bias (p. 470)
• The Effect of Reliability (p. 471)
• Other Statistical Procedures (p. 471)
• Test Fairness (p. 472)
• Example: Is the SAT Biased in Terms of Race or Socioeconomic Status? (p. 473)
• Race/Ethnicity (p. 473)
• Socioeconomic Status (p. 475)
• Technical Appendix: R Syntax (p. 479)
• Summary (p. 487)
• Suggested Readings (p. 487)
• Notes (p. 488)
• Part V Advanced Psychometric Approaches (p. 489)
• Chapter 12 Confirmatory Factor Analysis (p. 491)
• On the Use of EFA and CFA (p. 493)
• The Frequency and Roles of EFA and CFA (p. 493)
• Using CFA to Evaluate Measurement Models (p. 493)
• The Process of CFA for Analysis of a Scale's Internal Structure (p. 494)
• Overview of CFA and an Example (p. 494)
• Preliminary Steps (p. 496)
• Step 1 : Specification of the Measurement Model (p. 497)
• Step 2: Computations (p. 500)
• Step 3: Interpreting and Reporting Output (p. 503)
• Step 4: Model Modification and Reanalysis (If Necessary) (p. 508)
• Comparing Models (p. 510)
• Summary (p. 511)
• CFA and Reliability (p. 511)
• Evaluating Types of Classical Test Theory Measurement Models (p. 511)
• Estimating Reliability (Omega Index) (p. 515)
• CFA and Validity (p. 518)
• CFA and Measurement Invariance (p. 520)
• The Meaning of Measurement Invariance (p. 521)
• Levels of Invariance: Meaning and Detection (p. 523)
• Technical Appendix: R Syntax (p. 531)
• Summary (p. 542)
• Suggested Readings (p. 543)
• Chapter 13 Generalizability Theory (p. 545)
• Multiple Facets of Measurement (p. 547)
• Generalizability, Universes, and Variance Components (p. 550)
• G Studies and D Studies (p. 553)
• Conducting and Interpreting Generalizability Theory Analysis: A One-Facet Design (p. 554)
• Phase 1: G Study (p. 556)
• Phase 2: D Study (p. 559)
• Conducting and Interpreting Generalizability Theory Analysis: A Two-Facet Design (p. 563)
• Phase 1: G Study (p. 565)
• Phase 2: D Study (p. 571)
• Other Measurement Designs (p. 573)
• Number of Facets (p. 573)
• Random Versus Fixed Facets (p. 574)
• Crossed Versus Nested Designs (p. 577)
• Relative Versus Absolute Decisions (p. 577)
• A Practical, Consistency-Oriented Interpretation of Variance Components (p. 580)
• Systematic Variance Components Reflect "Consistent Variance" (p. 580)
• Residual/Error Variance Component Reflects inconsistent Variance (p. 581)
• Generatizability Coefficients as the Proportion of Variance That Is Consistent (p. 582)
• More Complex Designs (p. 583)
• Technical Appendix: R Syntax (p. 583)
• Summary (p. 591)
• Suggested Readings (p. 592)
• Notes (p. 592)
• Chapter 14 Item Response Theory and Rasch Models (p. 593)
• Factors Affecting Responses to Test items (p. 593)
• Respondent Trait Level as a Determinant of Item Responses (p. 594)
• Item Difficulty as a Determinant of Item Responses (p. 595)
• Item Discrimination as a Determinant of item Responses (p. 597)
• Guessing (p. 598)
• IRT Measurement Models (p. 599)
• One-Parameter Logistic Model (or Rasch Model) (p. 600)
• Two-Parameter Logistic Model (p. 602)
• Three-Parameter Logistic Model (p. 604)
• Graded Response Model (p. 605)
• Obtaining Parameter Estimates: A 1PL Example (p. 609)
• Model Fit (p. 614)
• Item and Test Information (p. 616)
• Item Characteristic Curves (p. 616)
• Item Information and Test Information (p. 619)
• Applications of IRT (p. 626)
• Test Development and Improvement (p. 626)
• Differential Item Functioning (p. 626)
• Person Fit (p. 627)
• Computerized Adaptive Testing (p. 628)
• Technical Appendix: R Syntax (p. 629)
• Summary (p. 638)
• Suggested Readings (p. 638)
• Glossary (p. 641)
• References (p. 649)
• Index (p. 669)