Descripciones mejoradas de Syndetics:

Scale Development: Theory and Applications, by Robert F. DeVellis and new co-author Carolyn T. Thorpe, demystifies measurement by emphasizing a logical rather than strictly mathematical understanding of concepts. The Fifth Editionincludes a new chapter that lays out the key concepts that distinguish indices from scales, contrasts various types of indices, suggests approaches for developing them, reviews validity and reliability issues, and discusses in broad terms some analytic approaches. All chapters have been updated, and the book strikes a balance between including relevant topics and highlighting recent developments in measurement while retaining an accessible, user-friendly approach to the material covered.

Tabla de contenidos provista por Syndetics

• Preface(p. xiii)
• Acknowledgments(p. xv)
• About the Authors(p. xvii)
• Chapter 1 Overview(p. 1)
• General Perspectives on Measurement(p. 2)
• Historical Origins of Measurement in Social Science(p. 3)
• Early Examples(p. 3)
• Emergence of Statistical Methods and the Rote of Mental Testing(p. 5)
• The Role of Psychophysics(p. 5)
• Later Developments in Measurement(p. 6)
• Evolution of Basic Concepts(p. 6)
• Evolution of Mental Testing(p. 7)
• Assessment of Mental Illness(p. 7)
• Broadening the Domain of Psychometrics(p. 10)
• The Role of Measurement in the Social Sciences(p. 11)
• The Relationship of Theory to Measurement(p. 11)
• Theoretical and Atheoretical Measures(p. 12)
• Composite Measurement Tools(p. 13)
• All Scales Are Not Created Equal(p. 15)
• Costs of Poor Measurement(p. 16)
• Summary and Preview(p. 17)
• Exercises(p. 18)
• Chapter 2 Understanding the Latent Variable(p. 19)
• Constructs Versus Measures(p. 19)
• Latent Variable as the Presumed Cause of Scale Item Values(p. 21)
• Path Diagrams(p. 22)
• Diagrammatic Conventions(p. 22)
• Path Diagrams in Scale Development(p. 22)
• Further Elaboration of the Measurement Model(p. 25)
• Classical Measurement Assumptions(p. 25)
• Parallel Tests(p. 26)
• Alternative Models(p. 29)
• Choosing a Causal Model(p. 30)
• Exercises(p. 32)
• Note(p. 32)
• Chapter 3 Scale Reliability(p. 33)
• Methods Based on the Analysis of Variance(p. 34)
• Continuous Versus Dichotomous Items(p. 35)
• Internal Consistency(p. 36)
• Coefficient Alpha(p. 36)
• The Covariance Matrix(p. 37)
• Covariance Matrices for Multi-Item Scales(p. 38)
• Alpha and the Covariance Matrix(p. 39)
• Alternative Formula for Alpha(p. 43)
• Critique of Alpha(p. 45)
• Remedies to Alpha's Limitations(p. 49)
• Coefficient Omega (¿)(p. 50)
• Reliability Based on Correlations Between Scale Scores(p. 52)
• Alternate-Forms Reliability(p. 52)
• Split-Half Reliability(p. 53)
• Inter-Rater Agreement(p. 56)
• Temporal Stability(p. 58)
• Reliability of Change Scores(p. 60)
• Reliability and Statistical Power(p. 65)
• Generalizability Theory(p. 66)
• Summary(p. 68)
• Exercises(p. 69)
• Notes(p. 70)
• Chapter 4 Scale Validity(p. 71)
• Content Validity(p. 72)
• Scope of the Variable and Implications for Content Validity(p. 73)
• Criterion-Related Validity(p. 78)
• Criterion-Related Validity Versus Accuracy(p. 79)
• Construct Validity(p. 82)
• Differentiating Construct From Criterion-Related Validity(p. 83)
• Attenuation(p. 84)
• How Strong Should Correlations Be to Demonstrate Construct Validity?(p. 85)
• Multitrait-Multimethod Matrix(p. 85)
• What About Face Validity?(p. 87)
• Exercises(p. 89)
• Chapter 5 Guidelines in Scale Development(p. 91)
• Step 1 Determine Clearly What It Is You Want to Measure(p. 91)
• Theory as an Aid to Clarity(p. 91)
• Specificity as an Aid to Clarity(p. 92)
• Being Clear About What to Include in a Measure(p. 93)
• Step 2 Generate an Item Pool(p. 94)
• Choose items That Reflect the Scale's Purpose(p. 94)
• Redundancy(p. 95)
• Number of Items(p. 97)
• Beginning the Process of Writing Items(p. 98)
• Characteristics of Good and Bad Items(p. 98)
• Positively and Negatively Worded Items(p. 101)
• Conclusion(p. 102)
• Step 3 Determine the Format for Measurement(p. 102)
• Thurstone Scaling(p. 103)
• Guttman Scaling(p. 104)
• Scales With Equally Weighted items(p. 105)
• How Many Response Categories?(p. 106)
• Specific Types of Response Formats(p. 110)
• Likert Scale(p. 110)
• Semantic Differential(p. 112)
• Visual Analog(p. 113)
• Pictorial Response Options(p. 114)
• Numerical Response Formats and Basic Neural Processes(p. 116)
• Binary Options(p. 117)
• Item Time Frames(p. 117)
• Mode of Administration(p. 118)
• Step 4 Have Initial Item Pool Reviewed by Experts(p. 119)
• Step 5 Cognitive Interviewing(p. 120)
• Step 6 Consider Inclusion of Validation items(p. 122)
• Step 7 Administer Items to a Development Sample(p. 123)
• Step 8 Evaluate the Items(p. 125)
• Initial Examination of Items' Performance(p. 125)
• Reverse Scoring(p. 126)
• Item-Scale Correlations(p. 127)
• Item Variances(p. 128)
• Item Means(p. 128)
• Dimensionality(p. 129)
• Reliability(p. 129)
• Step 9 Optimize Scale Length(p. 131)
• Effect of Scale Length on Reliability(p. 131)
• Effects of Dropping "Bad" Items(p. 131)
• Tinkering With Scale Length(p. 132)
• Split Samples(p. 133)
• Exercises(p. 134)
• Note(p. 135)
• Chapter 6 Factor Analysis(p. 137)
• Overview of Factor Analysis(p. 138)
• Examples of Methods Analogous to Factor Analytic Concepts(p. 139)
• Example 1 (p. 139)
• Example 2 (p. 141)
• Shortcomings of These Methods(p. 142)
• Conceptual Description of Factor Analysis(p. 144)
• Extracting Factors(p. 144)
• The First Factor(p. 144)
• Subsequent Factors(p. 146)
• Deciding How Many Factors to Extract(p. 147)
• Rotating Factors(p. 152)
• Rotation Analogy 1(p. 153)
• Rotation Analogy 2(p. 154)
• Rotation Analogy 3(p. 155)
• Orthogonal Versus Oblique Rotation(p. 161)
• Choosing Type of Rotation(p. 164)
• Bifactor and Hierarchical Factor Models(p. 165)
• Interpreting Factors(p. 170)
• Principal Components Versus Common Factors(p. 172)
• Same or Different?(p. 172)
• Confirmatory Factor Analysis(p. 176)
• Using Factor Analysis in Scale Development(p. 178)
• Sample Size(p. 180)
• Conclusion(p. 182)
• Exercises(p. 182)
• Chapter 7 The Index(p. 183)
• How an Index Differs From a Scale(p. 183)
• The Two Distinct Types of Index(p. 185)
• Causal Formative Measures(p. 185)
• Composite Formative Measures(p. 186)
• Other Conceptual Differences Between Scales and Indices(p. 188)
• Empirical Differences Between Scales and Indices(p. 189)
• Relationships Among Indicators(p. 189)
• Impact of Adding Items(p. 190)
• Rules of Thumb for Differentiating an Index From a Scale(p. 192)
• Limitations of Conceptual Criteria(p. 193)
• Limitations of Empirical Criteria(p. 195)
• Is It a Scale or an Index? Formal Methods for Distinguishing Effect and Causal Indicators(p. 196)
• The Correlation Matrix(p. 196)
• Factor Analysis(p. 197)
• Vanishing Tetrads(p. 198)
• Steps in Developing and Evaluating an Index(p. 202)
• Index Hem Development(p. 202)
• The Role of Theory(p. 202)
• Index Item Creation(p. 202)
• Index Item Redundancy and Number(p. 203)
• Other Index Development Considerations(p. 203)
• Evaluating Items(p. 203)
• Regression as a Heuristic for Index Development(p. 204)
• Regression as an Alternative to Index Development(p. 205)
• Regression as a Method for Index Development and Validation(p. 207)
• Regression-Based Examples(p. 207)
• Index Validity(p. 212)
• Content Validity(p. 212)
• Construct Validity(p. 212)
• Criterion Validity(p. 216)
• Group Comparison Approach(p. 217)
• Index Reliability(p. 218)
• Hybrid Measures(p. 219)
• Hierarchical Hybrids(p. 219)
• Hierarchical Hybrid Indices and Multidimensional Scales(p. 220)
• Hybrids Involving Nonhierarchical Heterogeneous Indicators(p. 221)
• Methods Based on Structural Equation Modeling(p. 224)
• Heuristic Overview of Structural Equation Modeling(p. 224)
• MIMIC Models(p. 226)
• Criticisms of Index Composites(p. 228)
• Exercises(p. 229)
• Note(p. 229)
• Chapter 8 An Overview of Item Response Theory(p. 231)
• Item Difficulty(p. 234)
• Item Discrimination(p. 236)
• Guessing, or False Positives(p. 236)
• Item-Characteristic Curves(p. 238)
• IRT Applied to Multiresponse Items(p. 241)
• Theta and Computerized Adaptive Testing (CAT)(p. 248)
• Complexities of IRT(p. 250)
• Conclusions(p. 252)
• Exercises(p. 255)
• Chapter 9 Measurement in the Broader Research Context(p. 257)
• Before Scale Development(p. 258)
• Look for Existing Tools(p. 258)
• View the Construct in the Context of the Population of Interest(p. 259)
• Consider the Scale in the Context of Other Measures or Procedures(p. 260)
• After Scale Administration(p. 261)
• Analytic Issues(p. 261)
• Interpretation Issues(p. 262)
• Generalizability(p. 262)
• Final Thoughts(p. 263)
• Small Measurement and Big Measurement(p. 263)
• Canoes and Cruise Ships(p. 263)
• Measurement "Canoes" and Measurement "Cruise Ships"(p. 264)
• Practical Implications of Small Versus Big Measurement(p. 266)
• Remember, Measurement Matters(p. 267)
• Exercise(p. 268)
• References(p. 269)
• Index(p. 281)

Notas de autor provistas por Syndetics

Robert F. DeVellis is Professor Emeritus in the Department of Health Behavior (Gillings School of Global Public Health) at the University of North Carolina at Chapel Hill. Dr. DeVellis has more than 40 years of experience in the measurement of psychological and social variables. He served as the first domain chair for Social Outcomes of the Patient-Reported Outcomes Measurement Information System (PROMIS) consortium, a multisite National Institutes of Health (NIH) Roadmap initiative directed at identifying, modifying, testing, and disseminating outcome measures for use by NIH investigators. He has served on the Board of Directors for the American Psychological Association's Division of Health Psychology (38), on the Arthritis Foundation's Clinical/Outcomes/Therapeutics Research Study Section, and on the Advisory Board of the Veterans Affairs Measurement Excellence Initiative. He is the recipient of the 2005 Distinguished Scholar Award from the Association of Rheumatology Health Professionals and is an associate editor of Arthritis Care and Research. In addition, he has served as guest editor, guest associate editor, or reviewer for more than two dozen other journals. He has served as principal investigator or co-investigator since the early 1980s on a series of research projects funded by the federal government and private foundations.

Carolyn T. Thorpe , PhD, MPH, is a tenured Associate Professor in the Division of Pharmaceutical Outcomes and Policy at the University of North Carolina Eshelman School of Pharmacy. She holds a joint appointment as a Research Health Scientist and Core Investigator in the Veterans Affairs (VA) Pittsburgh Healthcare System's Center for Health Equity Research and Promotion (CHERP), and serves as Co-Director of the Department of Veteran's Affairs Postdoctoral Pharmacy Fellowship in Medication Safety and Pharmacy Outcomes. She has served on study sections for the National Institutes of Health and Department of Veterans Affairs, as an Associate Editor for the journal Research in Social and Administrative Pharmacy , and as a reviewer for a diverse range of peer-reviewed journals at the intersection of public health, medicine, and social science. Over the past two decades, Dr. Thorpe has led a federally-funded research program that aims to improve the health of older adults managing complex chronic conditions and has published over 100 peer-reviewed manuscripts. Her research interests lie in measuring and examining psychological, social, and behavioral factors related to the safe and appropriate use of medications in older adults, and she has specific expertise in developing and evaluating measures of patient illness self-management behavior and medication nonadherence. She enjoys teaching principles of study design and measurement to professional pharmacy and graduate students and serving as an active research mentor of trainees at all levels.