Multilevel Modeling Using MplusCRC Press, 3 févr. 2017 - 336 pages This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data. |
Table des matières
An Introduction to Multilevel Data Structure | |
Fitting TwoLevel Models in Mplus | |
Additional Issues in Fitting TwoLevel Models | |
Fitting ThreeLevel Models in Mplus | |
Longitudinal Data Analysis Using Multilevel Models | |
Brief Introduction to Generalized Linear Models | |
Multilevel Generalized Linear Models MGLMs and Multilevel Survival Models | |
Brief Review of Latent Variable Modeling in Mplus | |
Mixture Models | |
Multilevel Latent Variable Models in Mplus | |
Bayesian Multilevel Modeling | |
A Brief Introduction to Mplus | |
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Expressions et termes fréquents
aBIC analysis assumption Bayesian BIC Category Chapter Chi-Square cluster context correlation covariance Criteria Akaike AIC Degrees of Freedom Equation Est./S.E. Two-Tailed P-Value Estimate S.E. Est./S.E. ESTIMATION TERMINATED NORMALLY example Factor for MLR factor loadings FILE FIT INFORMATION Number Free Parameters Loglikelihood GEREAD ON GEVOCAB gevocab GEVOCAB_SL HO Scaling independent variable individual Information Criteria Akaike interaction latent class latent variable latent variable modeling likelihood linear regression logistic regression Loglikelihood HO Value mean measured MODEL ESTIMATION TERMINATED MODEL FIT INFORMATION model parameters Mplus multilevel models normally distributed number of factors Number of Free numsense observed variables outcome variable output parameter estimates plot Poisson regression posterior distribution predictor random effects random intercept random slope Rasch model reading achievement relationship researcher Residual Variances Residual Variances GEREAD RESULTS Within Level Root Mean Square S.E. Est./S.E. Two-Tailed sample Sample-Size Adjusted BIC Scaling Correction Factor standard error statistically significant TWOLEVEL