Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer
| Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer |
| Autore | Wang Jichuan |
| Pubbl/distr/stampa | Berlin, : De Gruyter |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina | 005.5/5 |
| Altri autori (Persone) |
XieHaiyi
FischerJames H |
| Soggetto topico |
Social sciences - Research - Mathematical models
Multilevel models (Statistics) |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-11-026770-5 |
| Classificazione | SK 850 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface / Wang, Jichuan / Xie, Haiyi / Fisher, James H. -- Contents -- Chapter 1. Introduction -- Chapter 2. Basics of linear multilevel models -- Chapter 3. Application of two-level linear multilevel models -- Chapter 4. Application of multilevel modeling to longitudinal data -- Chapter 5. Multilevel models for discrete outcome measures -- Chapter 6. Other applications of multilevel modeling and related issues -- References -- Index |
| Record Nr. | UNINA-9910465523203321 |
Wang Jichuan
|
||
| Berlin, : De Gruyter | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer
| Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer |
| Autore | Wang Jichuan |
| Pubbl/distr/stampa | Berlin, : De Gruyter |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina | 005.5/5 |
| Altri autori (Persone) |
XieHaiyi
FischerJames H |
| Soggetto topico |
Social sciences - Research - Mathematical models
Multilevel models (Statistics) |
| Soggetto non controllato |
Multilevel Model
SAS® Statistics |
| ISBN | 3-11-026770-5 |
| Classificazione | SK 850 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface / Wang, Jichuan / Xie, Haiyi / Fisher, James H. -- Contents -- Chapter 1. Introduction -- Chapter 2. Basics of linear multilevel models -- Chapter 3. Application of two-level linear multilevel models -- Chapter 4. Application of multilevel modeling to longitudinal data -- Chapter 5. Multilevel models for discrete outcome measures -- Chapter 6. Other applications of multilevel modeling and related issues -- References -- Index |
| Record Nr. | UNINA-9910791967403321 |
|
Wang Jichuan
|
||
| Berlin, : De Gruyter | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer
| Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer |
| Autore | Wang Jichuan |
| Pubbl/distr/stampa | Berlin, : De Gruyter |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina | 005.5/5 |
| Altri autori (Persone) |
XieHaiyi
FischerJames H |
| Soggetto topico |
Social sciences - Research - Mathematical models
Multilevel models (Statistics) |
| Soggetto non controllato |
Multilevel Model
SAS® Statistics |
| ISBN | 3-11-026770-5 |
| Classificazione | SK 850 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface / Wang, Jichuan / Xie, Haiyi / Fisher, James H. -- Contents -- Chapter 1. Introduction -- Chapter 2. Basics of linear multilevel models -- Chapter 3. Application of two-level linear multilevel models -- Chapter 4. Application of multilevel modeling to longitudinal data -- Chapter 5. Multilevel models for discrete outcome measures -- Chapter 6. Other applications of multilevel modeling and related issues -- References -- Index |
| Record Nr. | UNINA-9910828881503321 |
|
Wang Jichuan
|
||
| Berlin, : De Gruyter | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structural equation modeling : applications using Mplus / / Jichuan Wang, Xiaoqian Wang
| Structural equation modeling : applications using Mplus / / Jichuan Wang, Xiaoqian Wang |
| Autore | Wang Jichuan |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2020] |
| Descrizione fisica | 1 online resource (537 pages) |
| Disciplina | 300.285 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Structural equation modeling - Data processing
Multivariate analysis - Data processing Social sciences - Statistical methods - Data processing |
| ISBN |
1-119-42272-8
1-119-42273-6 1-119-42271-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Confirmatory factor analysis -- Structural equation model -- Latent growth models (LGM) for longitudinal data analysis -- Multi-group modeling -- Mixture modeling -- Sample size for structural equation modeling. |
| Record Nr. | UNINA-9910555108103321 |
|
Wang Jichuan
|
||
| Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structural equation modeling [[electronic resource] ] : applications using Mplus / / Jichuan Wang, Xiaoqian Wang
| Structural equation modeling [[electronic resource] ] : applications using Mplus / / Jichuan Wang, Xiaoqian Wang |
| Autore | Wang Jichuan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (479 p.) |
| Disciplina | 519.5/3 |
| Altri autori (Persone) | WangXiaoqian |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Multivariate analysis - Data processing
Social sciences - Statistical methods - Data processing Structural equation modeling - Data processing |
| ISBN |
1-283-55059-8
9786613863041 1-118-35629-2 1-118-35625-X 1-118-35631-4 |
| Classificazione | SOC027000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Structural Equation Modeling: Applications Using Mplus; Contents; Preface; 1 Introduction; 1.1 Model formulation; 1.1.1 Measurement model; 1.1.2 Structural model; 1.1.3 Model formulation in equations; 1.2 Model identification; 1.3 Model estimation; 1.4 Model evaluation; 1.5 Model modification; 1.6 Computer programs for SEM; Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters; Appendix 1.B Maximum likelihood function for SEM; 2 Confirmatory factor analysis; 2.1 Basics of CFA model; 2.2 CFA model with continuous indicators
2.3 CFA model with non-normal and censored continuous indicators2.3.1 Testing non-normality; 2.3.2 CFA model with non-normal indicators; 2.3.3 CFA model with censored data; 2.4 CFA model with categorical indicators; 2.4.1 CFA model with binary indicators; 2.4.2 CFA model with ordered categorical indicators; 2.5 Higher order CFA model; Appendix 2.A BSI-18 instrument; Appendix 2.B Item reliability; Appendix 2.C Cronbach's alpha coefficient; Appendix 2.D Calculating probabilities using PROBIT regression coefficients; 3 Structural equations with latent variables; 3.1 MIMIC model 3.2 Structural equation model3.3 Correcting for measurement errors in single indicator variables; 3.4 Testing interactions involving latent variables; Appendix 3.A Influence of measurement errors; 4 Latent growth models for longitudinal data analysis; 4.1 Linear LGM; 4.2 Nonlinear LGM; 4.3 Multi-process LGM; 4.4 Two-part LGM; 4.5 LGM with categorical outcomes; 5 Multi-group modeling; 5.1 Multi-group CFA model; 5.1.1 Multi-group first-order CFA; 5.1.2 Multi-group second-order CFA; 5.2 Multi-group SEM model; 5.3 Multi-group LGM; 6 Mixture modeling; 6.1 LCA model; 6.1.1 Example of LCA 6.1.2 Example of LCA model with covariates6.2 LTA model; 6.2.1 Example of LTA; 6.3 Growth mixture model; 6.3.1 Example of GMM; 6.4 Factor mixture model; Appendix 6.A Including covariate in the LTA model; 7 Sample size for structural equation modeling; 7.1 The rules of thumb for sample size needed for SEM; 7.2 Satorra and Saris's method for sample size estimation; 7.2.1 Application of Satorra and Saris's method to CFA model; 7.2.2 Application of Satorra and Saris's method to LGM; 7.3 Monte Carlo simulation for sample size estimation; 7.3.1 Application of Monte Carlo simulation to CFA model 7.3.2 Application of Monte Carlo simulation to LGM7.3.3 Application of Monte Carlo simulation to LGM with covariate; 7.3.4 Application of Monte Carlo simulation to LGM with missing values; 7.4 Estimate sample size for SEM based on model fit indices; 7.4.1 Application of MacCallum, Browne and Sugawara's method; 7.4.2 Application of Kim's method; References; Index; Series |
| Record Nr. | UNINA-9910791707803321 |
|
Wang Jichuan
|
||
| Chichester, West Sussex, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structural equation modeling [[electronic resource] ] : applications using Mplus / / Jichuan Wang, Xiaoqian Wang
| Structural equation modeling [[electronic resource] ] : applications using Mplus / / Jichuan Wang, Xiaoqian Wang |
| Autore | Wang Jichuan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (479 p.) |
| Disciplina | 519.5/3 |
| Altri autori (Persone) | WangXiaoqian |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Multivariate analysis - Data processing
Social sciences - Statistical methods - Data processing Structural equation modeling - Data processing |
| ISBN |
1-283-55059-8
9786613863041 1-118-35629-2 1-118-35625-X 1-118-35631-4 |
| Classificazione | SOC027000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Structural Equation Modeling: Applications Using Mplus; Contents; Preface; 1 Introduction; 1.1 Model formulation; 1.1.1 Measurement model; 1.1.2 Structural model; 1.1.3 Model formulation in equations; 1.2 Model identification; 1.3 Model estimation; 1.4 Model evaluation; 1.5 Model modification; 1.6 Computer programs for SEM; Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters; Appendix 1.B Maximum likelihood function for SEM; 2 Confirmatory factor analysis; 2.1 Basics of CFA model; 2.2 CFA model with continuous indicators
2.3 CFA model with non-normal and censored continuous indicators2.3.1 Testing non-normality; 2.3.2 CFA model with non-normal indicators; 2.3.3 CFA model with censored data; 2.4 CFA model with categorical indicators; 2.4.1 CFA model with binary indicators; 2.4.2 CFA model with ordered categorical indicators; 2.5 Higher order CFA model; Appendix 2.A BSI-18 instrument; Appendix 2.B Item reliability; Appendix 2.C Cronbach's alpha coefficient; Appendix 2.D Calculating probabilities using PROBIT regression coefficients; 3 Structural equations with latent variables; 3.1 MIMIC model 3.2 Structural equation model3.3 Correcting for measurement errors in single indicator variables; 3.4 Testing interactions involving latent variables; Appendix 3.A Influence of measurement errors; 4 Latent growth models for longitudinal data analysis; 4.1 Linear LGM; 4.2 Nonlinear LGM; 4.3 Multi-process LGM; 4.4 Two-part LGM; 4.5 LGM with categorical outcomes; 5 Multi-group modeling; 5.1 Multi-group CFA model; 5.1.1 Multi-group first-order CFA; 5.1.2 Multi-group second-order CFA; 5.2 Multi-group SEM model; 5.3 Multi-group LGM; 6 Mixture modeling; 6.1 LCA model; 6.1.1 Example of LCA 6.1.2 Example of LCA model with covariates6.2 LTA model; 6.2.1 Example of LTA; 6.3 Growth mixture model; 6.3.1 Example of GMM; 6.4 Factor mixture model; Appendix 6.A Including covariate in the LTA model; 7 Sample size for structural equation modeling; 7.1 The rules of thumb for sample size needed for SEM; 7.2 Satorra and Saris's method for sample size estimation; 7.2.1 Application of Satorra and Saris's method to CFA model; 7.2.2 Application of Satorra and Saris's method to LGM; 7.3 Monte Carlo simulation for sample size estimation; 7.3.1 Application of Monte Carlo simulation to CFA model 7.3.2 Application of Monte Carlo simulation to LGM7.3.3 Application of Monte Carlo simulation to LGM with covariate; 7.3.4 Application of Monte Carlo simulation to LGM with missing values; 7.4 Estimate sample size for SEM based on model fit indices; 7.4.1 Application of MacCallum, Browne and Sugawara's method; 7.4.2 Application of Kim's method; References; Index; Series |
| Record Nr. | UNINA-9910820045703321 |
|
Wang Jichuan
|
||
| Chichester, West Sussex, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||