Germania 1980 : una scadenza per l'Europa / Comitato di iniziativa e di appoggio alla difesa dei diritti civili e delle libertà democratiche nella RFT ; contributi di Wolfgang Abendroth ... [et al.]

• Record Nr.: UNICAMPANIAVAN00289831
• Pubbl/distr/stampa: [Milano], : Feltrinelli, 1980
• Descrizione fisica: 165 p. ; 21 cm
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Structural equation modeling : a Bayesian approach / / Sik-Yum Lee

• Record Nr.: UNINA9911019175403321
• Autore: Lee Sik-Yum
• Pubbl/distr/stampa: Chichester, England ; ; Hoboken, NJ, : Wiley, c2007
• ISBN: 9786610838554; 9781280838552; 1280838558; 9780470024737; 0470024739; 9780470024249; 0470024240
• Descrizione fisica: 1 online resource (459 p.)
• Collana: Wiley series in probability and statistics
• Classificazione: 31.73
• Disciplina: 519.5/3
• Soggetti: Structural equation modeling; Bayesian statistical decision theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Structural Equation Modeling; Contents; About the Author; Preface; 1 Introduction; 1.1 Standard Structural Equation Models; 1.2 Covariance ; Structure Analysis; 1.3 Why a New Book?; 1.4 Objectives of the Book; 1.5 Data Sets and Notations; Appendix 1.1; References; 2 Some Basic Structural Equation Models; 2.1 Introduction; 2.2 Exploratory Factor Analysis; 2.3 Confirmatory and Higher-order Factor Analysis Models; 2.4 The LISREL Model; 2.5 The Bentler-Weeks Model; 2.6 Discussion; References; 3 Covariance Structure Analysis; 3.1 Introduction; 3.2 Definitions, Notations and Preliminary Results; 3.3 GLS Analysis of Covariance Structure3.4 ML Analysis of Covariance Structure; 3.5 Asymptotically Distribution-free Methods; 3.6 Some Iterative Procedures; Appendix 3.1: Matrix Calculus; Appendix 3.2: Some Basic Results in Probability Theory; Appendix 3.3: Proofs of Some Results; References; 4 Bayesian Estimation of Structural Equation Models; 4.1 Introduction; 4.2 Basic Principles and Concepts of Bayesian Analysis of SEMs; 4.3 Bayesian Estimation of the CFA Model; 4.4 Bayesian Estimation of Standard SEMs; 4.5 Bayesian Estimation via WinBUGS; Appendix 4.1: The Metropolis-Hastings Algorithm; 6.3 Bayesian Estimation and Goodness-of-fit6.4 Bayesian Model Comparison; 6.5 Application 1: Bayesian Selection of the Number of Factors in EFA; 6.6 Application 2: Bayesian Analysis of Quality of Life Data; References; 7 Structural Equation Models with Dichotomous Variables; 7.1 Introduction; 7.2 Bayesian Analysis; 7.3 Analysis of a Multivariate Probit Confirmatory Factor Analysis Model; 7.4 Discussion; Appendix 7.1: Questions Associated with the Manifest Variables; References; 8 Nonlinear Structural Equation Models; 8.1 Introduction; 8.2 Bayesian Analysis of a Nonlinear SEM; 8.3 Bayesian Estimation of Nonlinear SEMs with Mixed Continuous and Ordered Categorical Variables8.4 Bayesian Estimation of SEMs with Nonlinear Covariates and Latent Variables; 8.5 Bayesian Model Comparison; References; 9 Two-level Nonlinear Structural Equation Models; 9.1 Introduction; 9.2 A Two-level Nonlinear SEM with Mixed Type Variables; 9.3 Bayesian Estimation; 9.4 Goodness-of-fit and Model Comparison; 9.5 An Application: Filipina CSWs Study; 9.6 Two-level Nonlinear SEMs with Cross-level Effects; 9.7 Analysis of Two-level Nonlinear SEMs using WinBUGS; Appendix 9.1: Conditional Distributions: Two-level Nonlinear SEM
• Sommario/riassunto: ***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent varia