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Discover The Power And Ease Of Mplus

Mplus is a comprehensive modeling program that integrates random effect, factor, and latent class analysis in both cross-sectional and longitudinal settings and for both single-level and multi-level data.

Cross-Sectional Analyses

Factor analysis and structural equation modeling - unique features for categorical and multilevel data Multilevel regression analysis - unique features for combining random effects and factors Latent class analysis - unique features for covariates and complex sample data Finite mixture analysis - unique features for model testing and complex sample data Complier-average causal effect estimation in randomized trials - unique features for complex samples and missing data with latent ignorability

Longitudinal Analyses

Mixed-effects repeated measures modeling - unique features for latent trajectory class analysis Multilevel analysis - unique features for 3-level growth modeling Discrete-time survival analysis - unique features for mixtures of latent classes

Monte Carlo Simulations

Estimation and power investigations for - Random effect models - Multilevel models - Mixture models

Analysis Advice

Mplus Support - extensive and rapid response Mplus Discussion - authors' advice on analyses Mplus short courses - 1- to 5-day intensive training on latest methodology

[此贴子已经被作者于2005-12-13 8:45:24编辑过]

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关键词：Official Mplus Offic PLUS CIA Mplus Official

Version 3 User's Guide Excerpts Following are excerpts from the Version 3 Mplus User's Guide. Chapters 3 - 11 include listings of a total of 125 examples. These examples are also included on the Mplus CD along with the corresponding Monte Carlo simulation setups that generated the data. Click on the image next to each chapter to download that chapter's examples and corresponding Monte Carlo simulation setups. Chapter 1: Introduction Chapter 2: Getting started with Mplus Chapter 3: Regression and path analysis Chapter 4: Exploratory factor analysis Chapter 5: Confirmatory factor analysis and structural equation modeling Chapter 6: Growth modeling Chapter 7: Mixture modeling with cross-sectional data Chapter 8: Mixture modeling with longitudinal data Chapter 9: Multilevel modeling with complex sample data Chapter 10: Multilevel mixture modeling Chapter 11: Monte Carlo studies Chapter 12: Special features Chapter 13: Special modeling issues

MPLUS Discussion Board

Exploratory Factor Analysis May 23, 2005 - 05:52 pm

Confirmatory Factor Analysis May 27, 2005 - 12:39 pm

Structural Equation Modeling May 29, 2005 - 09:25 am

Multilevel Data/Complex Sample May 28, 2005 - 06:23 am

Latent Variable Mixture Modeling May 26, 2005 - 04:18 pm

Growth Modeling of Longitudinal Data May 26, 2005 - 05:34 am

Missing Data Modeling May 27, 2005 - 12:51 pm

Categorical Data Modeling May 27, 2005 - 02:56 pm

Hi Drs. Muthen, I am not sure where to post this, but I just finished reading "Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models" (similar to the Psychometrica piece with Pickles) by Skrondal & Rabe-Hesketh. I was wondering where you stood on their approach (i.e., GLLAMM). They critize the multigroup approach to M-level anlayses, but only LISREL seems to do that now. I am ingorant as to much of the math underlying Mplus, but it seems Mplus's approach is quite similar (and I've heard as being much faster). Do you know where the two approaches/programs differ markedly and when one might be more advantageous than the other. thanks!

Michael J. Zyphur

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The spirit of general latent variable modeling introduced with the emergence of Mplus in 1998 is also present in the nice book of 2004 by Skrondal-Rabe-Hesketh and in their related computer program GLLAMM, but there are some key differences with respect to interface, models, and algorithms. GLLAMM has a technical-statistical interface where the user needs to specify models in terms of matrices, whereas Mplus has a simple, non-technical interface. The modeling framework of Mplus is more general than that of GLLAMM, for example modeling with a very flexible combination of continuous and categorical latent variables and random slopes with continuous latent variables. The computations of Mplus are considerably faster than those of GLLAMM both because Mplus has a more efficient executable platform and because with full ML estimation Mplus avoids numerical integration wherever possible and Mplus also offers other, quicker estimators. If you point me to the pages where the critique of the multigroup approach is given, I can respond to that aspect.

我买了，但是这个是dome的啊，不是正式的！

不是吧，要100金？

Yes, It is Demo + Activation, which is the way we use official software!

好贵，降价吧

这个帖子骗人。根本就不能activate

到底是什么版本的呢？