Principles of Psychological Assessment: With Applied Examples in R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

This book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments. It discusses psychometrics (reliability and validity), the assessment of various psychological domains (behavior, personality, intellectual functioning), various measurement methods (e.g., questionnaires, observations, interviews, biopsychological assessments, performance-based assessments), and emerging analytical frameworks to evaluate and improve assessment including: generalizability theory, structural equation modeling, item response theory, and signal detection theory. The text also discusses ethics, test bias, and cultural and individual diversity. Key Features Gives analysis examples using free software Helps readers apply principles to research and practice Provides text, analysis code/syntax, R output, figures, and interpretations integrated to guide readers Uses the freely available petersenlab package for R Principles of Psychological Assessment: With Applied Examples in R is intended for use by graduate students, faculty, researchers, and practicing psychologists. Cover Half Title Series Page Title Page Copyright Page Dedication Contents List of Figures List of Tables Acknowledgments Introduction 1. Scores and Scales 1.1. Getting Started 1.2. Data Types 1.3. Score Transformation 1.4. Conclusion 1.5. Suggested Readings 2. Constructs 2.1. Types of Constructs 2.2. Differences in Measurement Expectations 2.3. Practical Issues 2.4. How to Estimate 2.5. Latent Variable Modeling: IRT, SEM, and CFA 2.6. Conclusion 2.7. Suggested Readings 3. Reliability 3.1. Classical Test Theory 3.2. Measurement Error 3.3. Overview of Reliability 3.4. Getting Started 3.5. Types of Reliability 3.6. Applied Examples 3.7. Standard Error of Measurement 3.8. Influences of Measurement Error on Test–Retest Reliability 3.9. Effect of Measurement Error on Associations 3.10. Method Bias 3.11. Generalizability Theory 3.12. Item Response Theory 3.13. The Problem of Low Reliability 3.14. Ways to Increase Reliability 3.15. Conclusion 3.16. Suggested Readings 4. Validity 4.1. Overview 4.2. Getting Started 4.3. Types of Validity 4.4. Validity Is a Process, Not an Outcome 4.5. Reliability Versus Validity 4.6. Effect of Measurement Error on Associations 4.7. Generalizability Theory 4.8. Ways to Increase Validity 4.9. Conclusion 4.10. Suggested Readings 5. Generalizability Theory 5.1. Overview 5.2. Getting Started 5.3. Conclusion 5.4. Suggested Readings 6. Factor Analysis and Principal Component Analysis 6.1. Overview 6.2. Getting Started 6.3. Descriptive Statistics and Correlations 6.4. Factor Analysis 6.5. Principal Component Analysis 6.6. Conclusion 6.7. Suggested Readings 7. Structural Equation Modeling 7.1. Overview 7.2. Getting Started 7.3. Types of Models 7.4. Estimating Latent Factors 7.5. Additional Types of SEM 7.6. Model Fit Indices 7.7. Measurement Model (of a Given Construct) 7.8. Confirmatory Factor Analysis 7.9. Structural Equation Model 7.10. Benefits of SEM 7.11. Generalizability Theory 7.12. Conclusion 7.13. Suggested Readings 8. Item Response Theory 8.1. Overview 8.2. Getting Started 8.3. Comparison of Scoring Approaches 8.4. One-Parameter Logistic (Rasch) Model 8.5. Two-Parameter Logistic Model 8.6. Two-Parameter Multidimensional Logistic Model 8.7. Three-Parameter Logistic Model 8.8. Four-Parameter Logistic Model 8.9. Graded Response Model 8.10. Conclusion 8.11. Suggested Readings 9. Prediction 9.1. Overview 9.2. Getting Started 9.3. Receiver Operating Characteristic Curve 9.4. Prediction Accuracy Across Cutoffs 9.5. Prediction Accuracy at a Given Cutoff 9.6. Optimal Cutoff Specification 9.7. Accuracy at Every Possible Cutoff 9.8. Regression for Prediction of Continuous Outcomes 9.9. Pseudo-Prediction 9.10. Ways to Improve Prediction Accuracy 9.11. Conclusion 9.12. Suggested Readings 10. Clinical Judgment Versus Algorithmic Prediction 10.1. Approaches to Prediction 10.2. Errors in Clinical Judgment 10.3. Humans Versus Computers 10.4. Accuracy of Different Statistical Models 10.5. Getting Started 10.6. Fitting the Statistical Models 10.7. Why Clinical Judgment Is More Widely Used Than Statistical Formulas 10.8. Conclusion 10.9. Suggested Readings 11. General Issues in Clinical Assessment 11.1. Historical Perspectives on Clinical Assessment 11.2. Contemporary Trends 11.3. Terminology 11.4. Errors of Pseudo-Prediction 11.5. Conclusion 11.6. Suggested Readings 12. Evidence-Based Assessment 12.1. Considerations 12.2. Clinically Relevant 12.3. Culturally Sensitive 12.4. Scientifically Sound 12.5. Bayesian Updating 12.6. Dimensional Approaches to Psychopathology 12.7. Reporting Guidelines for Publications 12.8. Many Measures Are Available 12.9. Conclusion 12.10. Suggested Readings 13. Ethical Issues in Assessment 13.1. Belmont Report 13.2. Our Ethical Advice 13.3. APA Ethics Code 13.4. Clinical Report Writing 13.5. Open Science 13.6. Conclusion 13.7. Suggested Readings 14. Intellectual Assessment 14.1. Defining Intelligence 14.2. History of Intelligence Research 14.3. Alternative Conceptualizations of Intelligence 14.4. Purposes of Intelligence Tests 14.5. Intelligence Versus Achievement Versus Aptitude 14.6. Theory Influences Intepretation of Scores 14.7. Time-Related Influences 14.8. Concerns with Intelligence Tests 14.9. Aptitude Testing 14.10. Scales 14.11. Conclusion 14.12. Suggested Readings 15. Test Bias 15.1. Overview 15.2. Ways to Investigate/Detect Test Bias 15.3. Examples of Bias 15.4. Test Fairness 15.5. Correcting for Bias 15.6. Getting Started 15.7. Examples of Unbiased Tests (in Terms of Predictive Bias) 15.8. Predictive Bias: Different Regression Lines 15.9. Differential Item Functioning 15.10. Measurement/Factorial Invariance 15.11. Conclusion 15.12. Suggested Readings 16. The Interview and the DSM 16.1. Overview 16.2. Two Traditions: Unstructured and Structured Interviews 16.3. Other Findings Regarding Interviews 16.4. Best Practice for Diagnostic Assessment 16.5. DSM and ICD 16.6. Conclusion 16.7. Suggested Readings 17. Objective Personality Testing 17.1. Overview 17.2. Example of an Objective Personality Test: MMPI 17.3. Problems with Objective True/False Measures 17.4. Approaches to Developing Personality Measures 17.5. Measure Development and Item Selection 17.6. Emerging Techniques 17.7. Flawed Nature of Self-Assessments 17.8. Observational Assessments 17.9. Structure of Personality 17.10. Personality Across the Lifespan 17.11. Conclusion 17.12. Suggested Readings 18. Projective Personality Testing 18.1. Overview 18.2. Examples of Projective Measures 18.3. Most Widely Used Assessments for Children 18.4. Evaluating the Scientific Status of Projective Measures 18.5. Conclusion 18.6. Suggested Readings 19. Psychophysiological and Ambulatory Assessment 19.1. NIMH Research Domain Criteria 19.2. Psychophysiological Measures 19.3. Conclusion 19.4. Suggested Readings 20. Computers and Adaptive Testing 20.1. Computer-Administered/Online Assessment 20.2. Adaptive Testing 20.3. Getting Started 20.4. Example of Unidimensional CAT 20.5. Creating a Computerized Adaptive Test From an Item Response Theory Model 20.6. Conclusion 20.7. Suggested Readings 21. Behavioral Assessment 21.1. Overview 21.2. Contexts for Observing 21.3. Costs of Behavioral Observation 21.4. Dependent Variable 21.5. Functional Behavioral Assessment/Analysis 21.6. Mental Status Exam 21.7. Reliability 21.8. Validity 21.9. Forms of Measurement 21.10. Analogue (Structured) Observational Assessments 21.11. Self-Monitoring 21.12. Behavior Rating Scales 21.13. Assessment of Therapeutic Process 21.14. Conclusion 21.15. Suggested Readings 22. Repeated Assessments Across Time 22.1. Overview 22.2. Examples of Repeated Measurement 22.3. Test Revisions 22.4. Change and Stability 22.5. Assessing Change 22.6. Types of Research Designs 22.7. Using Sequential Designs to Make Developmental Inferences 22.8. Heterotypic Continuity 22.9. Conclusion 22.10. Suggested Readings 23. Assessment of Cognition 23.1. Overview 23.2. Aspects of Cognition Assessed 23.3. Approaches to Assessing Cognition 23.4. Conclusion 23.5. Suggested Readings 24. Cultural and Individual Diversity 24.1. Terminology 24.2. Assessing Cultural and Individual Diversity: Multicultural Assessment Frameworks 24.3. Assessments with Ethnic, Linguistic, and Culturally Diverse Populations 24.4. Conclusion 24.5. Suggested Readings References Index This book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments. It discusses psychometrics (reliability and validity), the assessment of various psychological domains (behavior, personality, intellectual functioning), various measurement methods (e.g., questionnaires, observations, interviews, biopsychological assessments, performance-based assessments), and emerging analytical frameworks to evaluate and improve assessment including: generalizability theory, structural equation modeling, item response theory, and signal detection theory. It also discusses ethics, test bias, and cultural and individual diversity. Key Features: • Analysis examples using free software • Helps readers apply principles to research and practice • Text, analysis code/syntax, R output, figures, and interpretations integrated to guide readers • Uses the freely available petersenlab package for R Principles of Psychological Assessment: With Applied Examples in R is intended for use by graduate students, faculty, researchers, and practicing psychologists. The book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments. It discusses psychometrics (reliability and validity), the assessment of various psychological domains (behavior, personality, intellectual functioning), various measurement methods (e.g., questionnaires, observations, interviews, biopsychological assessments, performance-based assessments), and emerging analytical frameworks to evaluate and improve assessment generalizability theory, structural equation modeling, item response theory, and signal detection theory. It also discusses ethics, test bias, and cultural and individual diversity. Key Principles of Psychological With Applied Examples in R is intended for use by graduate students, faculty, researchers, and practicing psychologists.