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05182nam a2200913 4500 |
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ocn794922819 |
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OCoLC |
| 005 |
20170124071441.2 |
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m o d |
| 007 |
cr ||||||||||| |
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120605s2012 enk ob 001 0 eng |
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|a 2012023035
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| 040 |
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|a DLC
|b eng
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|b Safari Books Online
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|a 519.5/3
|2 23
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| 084 |
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|a SOC027000
|2 bisacsh
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| 049 |
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|a MAIN
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| 100 |
1 |
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|a Wang, Jichuan.
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| 245 |
1 |
0 |
|a Structural equation modeling with Mplus :
|b methods and applications /
|c Jichuan Wang, Xiaoqian Wang.
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| 264 |
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1 |
|a Chichester, West Sussex :
|b Wiley/Higher Education Press,
|c 2012.
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| 300 |
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|a 1 online resource.
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Wiley series in probability and statistics
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| 520 |
|
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|a "Focuses on the methods and practical aspects of SEM models using Mplus"--
|c Provided by publisher.
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| 504 |
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|a Includes bibliographical references and index.
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| 500 |
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|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.
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| 588 |
0 |
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|a Print version record and CIP data provided by publisher.
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| 505 |
0 |
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|a Introduction -- Confirmatory factor analysis -- Structural equations with latent variables -- Latent growth models for longitudinal data analysis -- Multi-group modeling -- Mixture modeling -- Sample size for structural equation modeling.
|
| 630 |
0 |
0 |
|a Mplus.
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| 630 |
0 |
4 |
|a Mplus.
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| 630 |
0 |
7 |
|a Mplus.
|2 blmlsh
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| 630 |
0 |
7 |
|a Mplus.
|2 blmsh
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| 650 |
|
0 |
|a Structural equation modeling
|x Data processing.
|
| 650 |
|
0 |
|a Multivariate analysis
|x Data processing.
|
| 650 |
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0 |
|a Social sciences
|x Statistical methods
|x Data processing.
|
| 650 |
|
4 |
|a Structural equation modeling
|x Data processing.
|
| 650 |
|
4 |
|a Multivariate analysis
|x Data processing.
|
| 650 |
|
4 |
|a Social sciences
|x Statistical methods
|x Data processing.
|
| 650 |
|
7 |
|a SOCIAL SCIENCE
|x Statistics.
|2 bisacsh
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| 650 |
|
7 |
|a Structural equation modeling / Data processing.
|2 local
|
| 650 |
|
7 |
|a Multivariate analysis / Data processing.
|2 local
|
| 650 |
|
7 |
|a Social sciences / Statistical methods / Data processing.
|2 local
|
| 655 |
|
4 |
|a Electronic books.
|
| 655 |
|
0 |
|a Electronic books.
|
| 700 |
1 |
|
|a Wang, Xiaoqian.
|
| 776 |
0 |
8 |
|i Print version:
|a Wang, Jichuan.
|t Structural equation modeling with Mplus.
|d Hoboken, N.J. : Wiley, 2012
|z 9781119978299
|w (DLC) 2012016113
|
| 830 |
|
0 |
|a Wiley series in probability and statistics.
|
| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118356258
|z Full Text via HEAL-Link
|
| 994 |
|
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|a 92
|b DG1
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