Descripciones mejoradas de Syndetics:

From a perspective that focuses on the meaning, purpose, and implications of key psychometric concepts, principles, and procedures, this text introduces the subject and study of psychometrics. It addresses these topics at a level that is deeper and more focused than what is found in typical introductory undergraduate testing and measurement texts, but is much more intuitive than what is traditionally found in the more technical publications intended for advanced graduate courses. By emphasizing concepts over mathematical proofs and by focusing on practical significance, this book assists students in appreciating not just how measurement problems can be addressed, but why it is crucial to address them.

Tabla de contenidos provista por Syndetics

• Preface(p. xiii)
• The Conceptual Orientation of This Book, Its Purpose, and the Intended Audience(p. xiii)
• Organizational Overview(p. xiv)
• Acknowledgments(p. xxi)
• About the Authors(p. xxiii)
• 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. 6)
• What Is a Psychological Test?(p. 6)
• Types of Tests(p. 7)
• Psychometrics(p. 9)
• What Is Psychometrics?(p. 9)
• Francis Galton and the Scope of Psychometrics(p. 9)
• Challenges to Measurement in Psychology(p. 10)
• Theme: The Importance of Individual Differences(p. 14)
• Suggested Readings(p. 15)
• Part I Basic Concepts in Measurement
• Chapter 2 Scaling(p. 19)
• Fundamental Issues With Numbers(p. 20)
• The Property of Identity(p. 20)
• The Property of Order(p. 21)
• The Property of Quantity(p. 22)
• The Number 0(p. 23)
• Units of Measurement(p. 25)
• Additivity and Counting(p. 27)
• Additivity(p. 27)
• Counts: When Do They Qualify as Measurement?(p. 29)
• Four Scales of Measurement(p. 29)
• Nominal Scales(p. 30)
• Ordinal Scales(p. 31)
• Interval Scales(p. 31)
• Ratio Scales(p. 32)
• Scales of Measurement: Practical Implications(p. 33)
• Additional Issues Regarding Scales of Measurement(p. 33)
• Summary(p. 34)
• Suggested Readings(p. 35)
• Chapter 3 Individual Differences and Correlations(p. 37)
• The Nature of Variability(p. 37)
• Importance of Individual Differences(p. 38)
• Variability and Distributions of Scores(p. 40)
• Central Tendency(p. 41)
• Variability(p. 41)
• Distribution Shapes and Normal Distributions(p. 45)
• Quantifying the Association Between Distributions(p. 47)
• Interpreting the Association Between Two Variables(p. 47)
• Covariance(p. 48)
• Correlation(p. 51)
• Variance and Covariance for "Composite Variables"(p. 52)
• Binary Items(p. 53)
• Interpreting Test Scores(p. 56)
• z Scores (Standard Scores)(p. 58)
• Converted Standard Scores (Standardized Scores)(p. 61)
• Percentile Ranks(p. 62)
• Normalized Scores(p. 65)
• Test Norms(p. 66)
• Representativeness of the Reference Sample(p. 67)
• Summary(p. 68)
• Suggested Readings(p. 69)
• Chapter 4 Test Dimensionality and Factor Analysis(p. 71)
• Test Dimensionality(p. 73)
• Three Dimensionality Questions(p. 74)
• Unidimensional Tests(p. 74)
• Multidimensional Tests With Correlated Dimensions (Tests With Higher-Order Factors)(p. 76)
• Multidimensional Tests With Uncorrelated Dimensions(p. 78)
• The Psychological Meaning of Test Dimensions(p. 78)
• Factor Analysis: Examining the Dimensionality of a Test(p. 79)
• The Logic and Purpose of Exploratory Factor Analysis: A Conceptual Overview(p. 79)
• Conducting and Interpreting an Exploratory Factor Analysis(p. 82)
• A Quick Look at Confirmatory Factor Analysis(p. 96)
• Summary(p. 97)
• Suggested Readings(p. 98)
• Part II Reliability
• Chapter 5 Reliability: Conceptual Basis(p. 101)
• Overview of Reliability and Classical Test Theory(p. 102)
• Observed Scores, True Scores, and Measurement Error(p. 104)
• Variances in Observed Scores, True Scores, and Error Scores(p. 107)
• Four Ways to Think of Reliability(p. 109)
• Reliability as the Ratio of True Score Variance to Observed Score Variance(p. 110)
• Lack of Error Variance(p. 112)
• The (Squared) Correlation Between Observed Scores and True Scores(p. 114)
• Lack of (Squared) Correlation Between Observed Scores and Error Scores(p. 116)
• Reliability and the Standard Error of Measurement(p. 118)
• Parallel Tests(p. 119)
• Domain Sampling Theory(p. 122)
• Summary(p. 122)
• Suggested Readings(p. 123)
• Chapter 6 Empirical Estimates of Reliability(p. 125)
• Alternate Forms Reliability(p. 126)
• Test-Retest Reliability(p. 129)
• Internal Consistency Reliability(p. 132)
• Split-Half Estimates of Reliability(p. 132)
• "Raw" Coefficient Alpha(p. 136)
• "Standardized" Coefficient Alpha(p. 139)
• Raw Alpha for Binary Items: KR 20(p. 141)
• Accuracy and Use of Internal Consistency Estimates of Reliability: Theory and Reality(p. 142)
• Internal Consistency Versus Dimensionality(p. 144)
• Factors Affecting the Reliability of Test Scores(p. 145)
• Sample Homogeneity and Reliability Generalization(p. 151)
• Reliability of Difference Scores(p. 152)
• Estimating the Reliability of Difference Scores(p. 154)
• Factors Affecting the Reliability of Difference Scores(p. 155)
• The Problem of Unequal Variability(p. 157)
• Difference Scores: Summary and Caution(p. 160)
• Summary(p. 161)
• Note(p. 162)
• Suggested Readings(p. 163)
• Chapter 7 The Importance of Reliability(p. 165)
• Applied Behavioral Practice: Evaluation of an Individual's Test Score(p. 165)
• Point Estimates of True Scores(p. 166)
• True Score Confidence Intervals(p. 169)
• Behavioral Research(p. 172)
• Reliability, True Associations, and Observed Associations(p. 172)
• Measurement Error (Low Reliability) Attenuates the Observed Associations Between Measures(p. 175)
• Reliability, Effect Sizes, and Statistical Significance(p. 178)
• Implications for Conducting and Interpreting Behavioral Research(p. 182)
• Test Construction and Refinement(p. 186)
• Item Discrimination and Other Information Regarding Internal Consistency(p. 187)
• Item Difficulty (Mean) and Item Variance(p. 191)
• Summary(p. 193)
• Suggested Readings(p. 193)
• Part III VALIDITY
• Chapter 8 Validity: Conceptual Basis(p. 197)
• What Is Validity?(p. 198)
• The Importance of Validity(p. 202)
• Validity Evidence: Test Content(p. 204)
• Threats to Content Validity(p. 204)
• Content Validity Versus Face Validity(p. 205)
• Validity Evidence: Internal Structure of the Test(p. 206)
• Validity Evidence: Response Processes(p. 209)
• Validity Evidence: Associations With Other Variables(p. 211)
• Validity Evidence: Consequences of Testing(p. 214)
• Other Perspectives on Validity(p. 216)
• Contrasting Reliability and Validity(p. 219)
• Summary(p. 220)
• Suggested Readings(p. 220)
• Chapter 9 Estimating and Evaluating Convergent and Discriminant Validity Evidence(p. 221)
• Methods for Evaluating Convergent and Discriminant Validity(p. 222)
• Focused Associations(p. 222)
• Sets of Correlations(p. 225)
• Multitrait-Multimethod Matrices(p. 228)
• Quantifying Construct Validity(p. 235)
• Factors Affecting a Validity Coefficient(p. 239)
• Associations Between Constructs(p. 240)
• Measurement Error and Reliability(p. 240)
• Restricted Range(p. 242)
• Skew and Relative Proportions(p. 246)
• Method Variance(p. 250)
• Time(p. 251)
• Predictions of Single Events(p. 251)
• Interpreting a Validity Coefficient(p. 253)
• Squared Correlations and "Variance Explained"(p. 253)
• Estimating Practical Effects: Binomial Effect Size Display, Taylor-Russell Tables, Utility Analysis, and Sensitivity/Specificity(p. 256)
• Guidelines or Norms for a Field(p. 261)
• Statistical Significance(p. 263)
• Summary(p. 268)
• Notes(p. 268)
• Suggested Readings(p. 268)
• Part IV Threats to Psychometric Quality
• Chapter 10 Response Biases(p. 273)
• Types of Response Biases(p. 274)
• Acquiescence Bias ("Yea-Saying and Nay-Saying")(p. 274)
• Extreme and Moderate Responding(p. 278)
• Social Desirability(p. 281)
• Malingering(p. 285)
• Careless or Random Responding(p. 286)
• Guessing(p. 287)
• Methods for Coping With Response Biases(p. 287)
• Minimizing the Existence of Bias by Managing the Testing Context(p. 288)
• Minimizing the Existence of Bias by Managing Test Content(p. 289)
• Minimizing the Effects of Bias by Managing Test Content or Scoring(p. 291)
• Managing Test Content to Detect Bias and Intervene(p. 294)
• Using Specialized Tests to Detect Bias and Intervene(p. 297)
• Response Biases, Response Sets, and Response Styles(p. 298)
• Summary(p. 299)
• Suggested Readings(p. 299)
• Chapter 11 Test Bias(p. 301)
• Why Worry About Test Score Bias?(p. 303)
• Detecting Construct Bias: Internal Evaluation of a Test(p. 303)
• Item Discrimination Index(p. 305)
• Factor Analysis(p. 307)
• Differential Item Functioning Analyses(p. 308)
• Rank Order(p. 311)
• Summary(p. 311)
• Detecting Predictive Bias: External Evaluation of a Test(p. 312)
• Basics of Regression Analysis(p. 313)
• One Size Fits All: The Common Regression Equation(p. 315)
• Intercept Bias(p. 316)
• Slope Bias(p. 318)
• Intercept and Slope Bias(p. 319)
• Outcome Score Bias(p. 320)
• The Effect of Reliability(p. 320)
• Other Statistical Procedures(p. 320)
• Test Fairness(p. 321)
• Example: Is the SAT Biased in Terms of Race or Socioeconomic Status?(p. 322)
• Race/Ethnicity(p. 322)
• Socioeconomic Status(p. 324)
• Summary(p. 327)
• Suggested Readings(p. 328)
• Part V Advanced Psychometric Approaches
• Chapter 12 Confirmatory Factor Analysis(p. 331)
• On the Use of EFA and CFA(p. 332)
• The Frequency and Roles of EFA and CFA(p. 332)
• Using CFA to Evaluate Measurement Models(p. 333)
• The Process of CFA for Analysis of a Scale's Internal Structure(p. 334)
• Overview of CFA and Example(p. 334)
• Preliminary Steps(p. 336)
• Step 1 Specification of Measurement Model(p. 336)
• Step 2 Computations(p. 339)
• Step 3 Interpreting and Reporting Output(p. 342)
• Step 4 Model Modification and Reanalysis (If Necessary)(p. 346)
• Comparing Models(p. 347)
• Summary(p. 348)
• CFA and Reliability(p. 348)
• CFA and Validity(p. 351)
• Summary(p. 353)
• Chapter 13 Generalizability Theory(p. 355)
• Multiple Facets of Measurement(p. 356)
• Generalizability, Universes, and Variance Components(p. 358)
• G Studies and D Studies(p. 360)
• Conducting and Interpreting Generalizability Theory Analysis: A One-Facet Design(p. 361)
• Phase 1 G Study(p. 362)
• Phase 2 D Study(p. 365)
• Conducting and Interpreting Generalizability Theory Analysis: A Two-Facet Design(p. 368)
• Phase 1 G Study(p. 370)
• Phase 2 D Study(p. 375)
• Other Measurement Designs(p. 377)
• Number of Facets(p. 377)
• Random Versus Fixed Facets(p. 378)
• Crossed Versus Nested Designs(p. 380)
• Relative Versus Absolute Decisions(p. 381)
• Summary(p. 383)
• Suggested Readings(p. 383)
• Chapter 14 Item Response Theory and Rasch Models(p. 385)
• Factors Affecting Responses to Test Items(p. 386)
• Respondent Trait Level as a Determinant of Item Responses(p. 386)
• Item Difficulty as a Determinant of Item Responses(p. 386)
• Item Discrimination as a Determinant of Item Responses(p. 388)
• Guessing(p. 389)
• IRT Measurement Models(p. 389)
• One-Parameter Logistic Model (or Rasch Model)(p. 390)
• Two-Parameter Logistic Model(p. 391)
• Graded Response Model(p. 393)
• Obtaining Parameter Estimates: A IPL Example(p. 396)
• Item and Test Information(p. 398)
• Item Characteristic Curves(p. 398)
• Item Information and Test Information(p. 401)
• Applications of IRT(p. 407)
• Test Development and Improvement(p. 407)
• Differential Item Functioning(p. 408)
• Person Fit(p. 408)

Notas de autor provistas por Syndetics

R. Michael Furr is Professor of Psychology and McCulloch Faculty Fellow at Wake Forest University, where he received the 2012 Award for Excellence in Research. His methodological work focuses on the measurement of similarity between psychological profiles, on the measurement of behavior, and on contrast analysis. His substantive research focuses on personality judgment and on the links between personality, situational forces, and behavior. He is 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 has taught graduate and undergraduate courses in psychometrics, psychological testing, personality psychology, statistics, and research methods at Appalachian State University and Wake Forest University. Verne R. Bacharach is professor of psychology at Appalachian State University. He has held faculty appointments at the University of Alabama, Peabody College of Vanderbilt University, and Acadia University in Nova Scotia and has chaired the departments at Appalachian State and Acadia. He has taught undergraduate and graduate courses in statistics, tests and measurements, and research methods for nearly 40 years. He has a long journal publication history of research and review articles. Dr. Bacharach obtained a Ph.D. in experimental psychology from the University of Kansas in 1971.