libri scuola books Fumetti ebook dvd top ten sconti 0 Carrello


Torna Indietro

kline rex b - principles and practice of structural equation modeling, fifth edition

Principles and Practice of Structural Equation Modeling, Fifth Edition




Disponibilità: Normalmente disponibile in 20 giorni
A causa di problematiche nell'approvvigionamento legate alla Brexit sono possibili ritardi nelle consegne.


PREZZO
87,98 €
NICEPRICE
83,58 €
SCONTO
5%



Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.


Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente


Facebook Twitter Aggiungi commento


Spese Gratis

Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 06/2023
Edizione: Edizione nuova, 5° edizione





Note Editore

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearls structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and a new self-test on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor. New to This Edition *Chapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. *Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. *Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical Features *New self-test of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers. *End-of-chapter suggestions for further reading and exercises with answers. *Troublesome examples from real data, with guidance for handling typical problems in analyses. *Topic boxes on special issues and boxed rules to remember. *Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the books detailed examples.




Sommario

Introduction - Whats New - Book Website - Pedagogical Approach - Principles > Software - Symbols and Notation - Enjoy the Ride - Plan of the Book I. Concepts, Standards, and Tools 1. Promise and Problems - Preparing to Learn SEM - Definition of SEM - Basic Data Analyzed in SEM - Family Matters - Pedagogy and SEM Families - Sample Size Requirements - Big Numbers, Low Quality - Limits of This Book - Summary - Learn More 2. Background Concepts and Self-Test - Uneven Background Preparation - Potential Obstacles to Learning about SEM - Significance Testing - Measurement and Psychometrics - Regression Analysis - Summary - Self-Test - Scoring Criteria 3. Steps and Reporting - Basic Steps - Optional Steps - Reporting Standards - Reporting Example - Summary - Learn More 4. Data Preparation - Forms of Input Data - Positive Definiteness - Missing Data - Classical (Obsolete) Methods for Incomplete Data - Modern Methods for Incomplete Data - Other Data Screening Issues - Summary - Learn More - Exercises - Appendix 4.a. Steps of Multiple Imputation 5. Computer Tools - Ease of Use, Not Suspension of Judgment - HumanComputer Interaction - Tips for SEM Programming - Ease of Use, Not Suspension of Judgment - Commercial versus Free Computer Tools - R Packages for SEM - Free SEM Software with Graphical User Interfaces - Commercial SEM Computer Tools - SEM Resources for Other Computing Environments - Summary II. Specification, Estimation, and Testing 6. Nonparametric Causal Models - Graph Vocabulary and Symbolism - Contracted Chains and Confounding - Covariate Selection - Instrumental Variables - Conditional Independencies and Other Types of Bias - Principles for Covariate Selection - d-Separation and Basis Sets - Graphical Identification Criteria - Detailed Example - Summary - Learn More - Exercises 7. Parametric Causal Models - Model Diagram Symbolism - Diagrams for Contracted Chains and Assumptions - Confounding in Parametric Models - Models with Correlated Causes or Indirect Effects - Recursive, Nonrecursive, and Partially Recursive Models - Detailed Example - Summary - Learn More - Exercises - Appendix 7.a. Advanced Topics in Parametric Models 8. Local Estimation and Piecewise SEM - Rationale of Local Estimation - Piecewise SEM - Detailed Example - Summary - Learn More - Exercises 9. Global Estimation and Mean Structures - Simultaneous Methods and Error Propagation - Maximum Likelihood Estimation - Default ML - Analyzing Nonnormal Data - Robust ML - FIML for Incomplete Data versus Multiple Imputation - Alternative Estimators for Continuous Outcomes - Fitting Models to Correlation Matrices - Healthy Perspective on Estimators and Global Estimation - Detailed Example - Introduction to Mean Structures - Prcis of Global Estimation - Summary - Learn More - Exercises - Appendix 9.a. Types of Information Matrices and Computer Options - Appendix 9.b. Casewise ML Methods for Data Missing Not at Random 10. Model Testing and Indexing - Model Testing - Model Chi-Square - Scaled Chi-Squares and Robust Standard Errors for Nonnormal Distributions - Model Fit Indexing - RMSEA - CFI - SRMR - Thresholds for Approximate Fit Indexes - Recommended Approach to Fit Evaluation - Global Fit Statistics for the Detailed Example - Power and Precision - Summary - Learn More - Exercises - Appendix 10.a. Significance Testing Based on the RMSEA 11. Comparing Models - Nested Models - Building and Trimming - Empirical versus Theoretical Respecification - Chi-Square Difference Test - Modification Indexes and Related Statistics - Intelligent Automated Search Strategies - Model Building for the Detailed Example - Comparing Nonnested Models - Equivalent Models - Coping with Equivalent or Nearly Equivalent Models - Summary - Learn More - Exercises - Appendix 11.a. Other Types of Model Relations and Tests 12. Comparing Groups - Issues in Multiple-Group SEM - Detailed Example for a Path Model of Achievement and Delinquency - Tests for Conditional Indirect Effects over Groups - Summary - Learn More - Exercises III. Multiple-Indicator Approximation of Concepts 13. Multiple-Indicator Measurement - Concepts, Indicators, and Proxies - Reflective Measurement and Effect Indicators - CausalFormative Measurement and Causal Indicators - Composite Measurement and Composite Indicators - Mixed-Model Measurement - Considerations in Selecting a Measurement Model - Cautions on Formative Measurement - Summary 14. Confirmatory Factor Analysis - EFA versus CFA - Suggestions for Selecting Indicators - Basic CFA Models - Other Methods for Scaling Factors - Detailed Example for a Basic CFA Model of Cognitive Abilities - Respecification of CFA Models - Estimation Problems - Equivalent CFA Models - Special Tests with Equality Constraints - Models for MultitraitMultimethod Data - Second-Order and Bifactor Models with General Factors - Summary - Learn More - Exercises - Appendix 14.a. Identification Rules for Correlated Errors or Multiple Loadings 15. Structural Regression Models - Full SR Models - Two-Step Modeling - Other Modeling Strategies - Detailed Example of Two-Step Modeling in a High-Risk Sample - Partial SR Models with Single Indicators - Example for a Partial SR Model - Summary - Learn More - Exercises 16. Composite Models - Modern Composite Analysis in SEM - Disambiguation of Terms - Special Computer Tools - Motivating Example - Alternative Composite Model - Partial Least Squares Path Modeling Algorithm - PLS PM Analysis of the Composite Model - HenselerOgasawara Specification and ML Analysis - Summary - Learn More - Exercises IV. Advanced Techniques 17. Analyses in Small Samples - Suggestions for Analyzing Common Factor Models - Analysis of a Common Factor Model in a Small Sample - Controlling Measurement Error in Manifest Variable Path Models - Adjusted Test Statistics for Small Samples - Bayesian Methods and Regularized SEM - Summary - Learn More - Exercises 18. Categorical Confirmatory Factor Analysis - Basic Estimation Options for Categorical Data - Overview of Continuous/Categorical Variable Methodology - Latent Response Variables and Thresholds - Polychoric Correlations - Measurement Model and Diagram - Methods to Scale Latent Response Variables - Estimators, Adjusted Test Statistics, and Robust Standard Errors - Models with Continuous and Ordinal Indicators - Detailed Example for Items about Self-Rated Depression - Other Estimation Options for Categorical CFA - Item Response Theory and CFA - Summary - Learn More - Exercises 19. Nonrecursive Models with Causal Loops - Causal Loops - Assumptions of Causal Loops - Identification Requirements - Respecificati




Autore

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montral, Qubec, Canada. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. Dr. Kline has published a number of chapters, journal articles, and books in these areas.










Altre Informazioni

ISBN:

9781462551910

Condizione: Nuovo
Dimensioni: Ø 1.85 lb
Formato: Brossura
Pagine Arabe: 494


Dicono di noi