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010 a| 2012023035;

020 a| 9781118356302 (ePub);

020 a| 9781118356296 (MobiPocket);

020 a| 9781118356319 ( Adobe PDF);

020 z| 9781119978299 (hardback);

035 a| (WaSeSS)ssj0000701932;

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050 00 a| QA278.3;

082 00 a| 519.5/32| 23;

084 a| SOC0270002| bisacsh;

100 1 a| Wang, Jichuan.=| ^A1126750;

245 10 a| Structural equation modeling with Mplush| [electronic resource] :b| methods and applications /c| Jichuan Wang, Xiaoqian Wang.;

260 a| Chichester, West Sussex :b| Wiley/Higher Education Press,;

300 a| 1 online resource.;

490 0 a| Wiley series in probability and statistics;

504 a| Includes bibliographical references and index.;

505 8 a| Machine generated contents note: Preface 1. Introduction 1.1 Model formulation 1.2 Model identification 1.3 Model estimation 1.4 Model evaluation 1.5 Model modification 2. Confirmatory factor analysis (CFA) Models 2.1 Basics of CFA 2.2 CFA with continuous indicators 2.3 CFA with non-normal and censored continuous indicators 2.4 CFA with categorical indicators 2.5 Higher-order CFA 3. Structural Equation Models (SEM) 3.1 Multiple indicators and multiple causes (MIMIC) Model 3.2 Structural equation model 3.3 Correcting for measurement errors in single indicator variables 3.4 Testing interactions involving latent variables 4. Latent growth modeling (LGM) for longitudinal data 4.1 Linear latent growth model (LGM) 4.2 Non-linear LGM 4.3 LGM with multiple growth processes 4.4 Two-part LGM 4.5 LGM with categorical outcomes 5. Multi-Group Modeling 5.1 Multi-group confirmatory factor analysis (CFA) model 5.2 Multi-group structural equation model (SEM) 5.3 Multi-group latent growth model (LGM) 6. Mixture models 6.1 Latent class analysis (LCA) model 6.2 Latent transition analysis (LTA) model 6.3 Growth mixture model (GMM) 6.4 Factor Mixture Model (FMM) 7. Sample Size for Structural Equation Modeling 7.1 Rule of thumbs for sample size needed for SEM 7.2 Satorra-Saris's method for sample size estimation 7.3 Monte Carlo simulation for sample size estimation 7.4 Estimate sample size for SEM based on model fit statistics/indexes References Index .;

506 a| Available only to authorized users.;

520 a| "Focuses on the methods and practical aspects of SEM models using Mplus"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

588 a| Description based on print version record and CIP data provided by publisher.;

630 00 a| Mplus.=| ^A1049940;

650 0 a| Structural equation modelingx| Data processing.=| ^A651161;

650 0 a| Multivariate analysisx| Data processing.=| ^A1133641;

650 0 a| Social sciencesx| Statistical methodsx| Data processing.=| ^A1114323;

650 7 a| SOCIAL SCIENCE / Statistics.2| bisacsh;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Wang, Xiaoqian.=| ^A1127258;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=989223;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5619547-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 4/19/2021x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5619547-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 4/19/2021x| EBOOKz| JERESOURCE;

Structural equation modeling with Mplus methods and applications / Jichuan Wang, Xiaoqian Wang.

Author/creator	Wang, Jichuan
Other author	Wang, Xiaoqian.
Format	Electronic
Publication Info	Chichester, West Sussex : Wiley/Higher Education Press,
Description	1 online resource.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Mplus. Structural equation modeling--Data processing. Multivariate analysis--Data processing. Social sciences--Statistical methods. --Data processing.

Series	Wiley series in probability and statistics
Contents	Machine generated contents note: Preface 1. Introduction 1.1 Model formulation 1.2 Model identification 1.3 Model estimation 1.4 Model evaluation 1.5 Model modification 2. Confirmatory factor analysis (CFA) Models 2.1 Basics of CFA 2.2 CFA with continuous indicators 2.3 CFA with non-normal and censored continuous indicators 2.4 CFA with categorical indicators 2.5 Higher-order CFA 3. Structural Equation Models (SEM) 3.1 Multiple indicators and multiple causes (MIMIC) Model 3.2 Structural equation model 3.3 Correcting for measurement errors in single indicator variables 3.4 Testing interactions involving latent variables 4. Latent growth modeling (LGM) for longitudinal data 4.1 Linear latent growth model (LGM) 4.2 Non-linear LGM 4.3 LGM with multiple growth processes 4.4 Two-part LGM 4.5 LGM with categorical outcomes 5. Multi-Group Modeling 5.1 Multi-group confirmatory factor analysis (CFA) model 5.2 Multi-group structural equation model (SEM) 5.3 Multi-group latent growth model (LGM) 6. Mixture models 6.1 Latent class analysis (LCA) model 6.2 Latent transition analysis (LTA) model 6.3 Growth mixture model (GMM) 6.4 Factor Mixture Model (FMM) 7. Sample Size for Structural Equation Modeling 7.1 Rule of thumbs for sample size needed for SEM 7.2 Satorra-Saris's method for sample size estimation 7.3 Monte Carlo simulation for sample size estimation 7.4 Estimate sample size for SEM based on model fit statistics/indexes References Index .
Abstract	"Focuses on the methods and practical aspects of SEM models using Mplus"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Source of description	Description based on print version record and CIP data provided by publisher.
Issued in other form	Print version: Wang, Jichuan. Structural equation modeling with Mplus Hoboken, N.J. : Wiley, 2012 9781119978299 (hardback)
Genre/form	Electronic books.
LCCN	2012023035
ISBN	9781118356302 (ePub)
ISBN	9781118356296 (MobiPocket)
ISBN	9781118356319 ( Adobe PDF)

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Structural equation modeling with Mplus methods and applications / Jichuan Wang, Xiaoqian Wang.

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