# Mplus 6.12 Base Program And Combination Add-on !NEW!

Mplus Version 5 contains several new general features as well as features specific to exploratory factor analysis, mixture modeling, and multilevel modeling. Mplus Version 5 also has new features that improve computational speed and memory capacity.The Version 5 Mplus User's Guide contains 13 new examples and 50 examples revised from their earlier versions either to make the input simpler or because of default changes. The new examples are listed at the end of this description. The Version 5 Mplus User's Guide will be available online.Following is a list of the new features in Mplus Version 5.Operating System and Number of ProcessorsMplus Version 5 is available not only on 32-bit but also on 64-bit operating systems allowing the following memory capacity32-bit Mplus on 32-bit machine: 2GB, or 3GB if system booted with /3GB32-bit Mplus on 64-bit machine: 4GB64-bit Mplus on 64-bit machine: 8 terabytes (8 x 1024GB)Mplus Version 5 has no limit on the number of processors used for parallel computing. The number of processors is limited only by what is available on the system.General FeaturesStandard errors for standardized solutions and R-squareP-valuesStandardized and normalized residualsNew option: MODEL = NOCOVARIANCES which fixes all covariance parameters at zeroDefault changes: MISSING, MEANSTRUCTURE, H1 as the defaultNew options: LISTWISE = ON, NOMEANSTRUCTURE, NOCHISQUARESaving standardized resultsExploratory Factor AnalysisAdditional factor loading matrix rotations: Quartimin, Geomin, and many othersStandard errors for rotated loadings and factor correlationsNon-normality robust standard errors and chi-square tests of model fitModification indices for residual correlationsMaximum likelihood estimation with censored, categorical, and count variablesExploratory factor analysis for complex survey data (stratification, clustering, and weights)TYPE = COMPLEX EFA # #;Exploratory factor mixture analysis with class-specific rotationsTYPE = MIXTURE EFA # #;Two-level exploratory factor analysis for continuous and categorical variables with new rotations and standard errors, including unrestricted model for either levelTYPE = TWOLEVEL EFA # # UW # # UB;Mixture ModelingFaster computations using random starts distributed over several processorsPROCESSORS = 4 (STARTS);Equality tests of means across classes for variables not in the model using posterior probability-based multiple imputationsAUXILIARY = x1-x10(e);Modified TECH14 LRTSTARTS and new K-1STARTS optionNew language for c ON c and u ON xTECH10 and chi-square for countsP-values saved for TECH11 and TECH14 with Monte Carlo simulationMultilevel ModelingSimple two-level limited-information weighted least squares estimator for categorical variablescomputational demand virtually independent of number of factors/random effectshigh-dimensional integration replaced by multiple instances of one- and two-dimensional integrationgeneralization of the Muthen (1984) single-level WLSpossible to explore many different models in a time-efficient mannervariables can be categorical, continuous, combinationsresiduals can be correlated (no conditional independence assumption)model fit chi-square testingcan produce unrestricted level 1 and level 2 correlation matrices for EFAsaving sample statistics and weight matrix for subsequent analysesTYPE = TWOLEVEL;ESTIMATOR = WLSM;Improved integration algorithms for two-level mediation modelsNew Technical AppendicesStandardized Coefficients and Their Standard ErrorsStandardized and Normalized ResidualsMixture Exploratory Factor AnalysisEquality Test of Means Across Latent Classes Using Wald Chi-Square Based on Draws From Posterior ProbabilitiesTwo-Level Weighted Least Squares Estimation. Proceedings of the Joint Statistical Meeting, August 2007, Biometrics SectionNew Examples in the Version 5 Mplus User's Guide4.1: Exploratory factor analysis with continuous factor indicators4.2: Exploratory factor analysis with categorical factor indicators4.3: Exploratory factor analysis with continuous, censored, categorical, and count factor indicators4.4: Exploratory factor mixture analysis with continuous latent class indicators4.5: Two-level exploratory factor analysis with continuous factor indicators4.6: Two-level exploratory factor analysis with both individual- and cluster-level factor indicators6.18: Multiple group multiple cohort growth model9.1: Two-level regression analysis for a continuous dependent variable with a random intercept9.2: Two-level regression analysis for a continuous dependent variable with a random slope9.4: Two-level path analysis with a continuous, a categorical, and a cluster-level observed dependent variable9.9: Two-level SEM with categorical factor indicators on the within level and cluster-level continuous observed and random intercept factor indicators on the between level9.15: Two-level multiple indicator growth model with categorical outcomes (three-level analysis)11.11: Monte Carlo simulation study for a two-level mediation model with random slopesMplus Version 4.21

## Mplus 6.12 Base Program and Combination Add-on

Mplus Version 3 significantly enhances the two major strengths of Mplus, simplicity of use and modeling generality. Mplus Version 3 introduces a multitude of unique features in areas of structural equation modeling, growth modeling, mixture modeling, multilevel modeling, and combinations of such modeling features.Mplus Version 3 is divided into a base program and three modules that can be added to the base program.The Mplus Base Program estimates models with continuous latent variables representing factors and random effects. It provides factor analysis models, path analysis models, structural equation models (SEM), growth, and discrete-time survival analysis models.The three modules are: a Mixture Add-On, a Multilevel Add-On, and a Combination Add-On. This arrangement allows users flexibility in selecting the add-on modules that best meet their analysis needs. Add-On modules can be purchased at any time after the base program is purchased.Click here for information on Mplus Version 3 Pricing.Click here for answers to frequently asked questions.Program Content

Following is a brief description of what is included in the Mplus Version 3 Base program and each Add-On module.Each module includes graphical displays of descriptive statistics and analysis results, and Monte Carlosimulation capabilities. Examples of some new statistical features are given below.Click here for more information on new Monte Carlo and graphics features that are available in the base program and the three modules.Mplus Base Program

The latent variable mixture modeling capabilities of Mplus have been greatly expanded in Version 2.An important expansion is that missing data are now allowed for the categorical latent classindicators in the mixture part of the model and for the continuous observed outcomes in thestructural equation part of the model. Maximum likelihood estimation is performed under MAR.The missing data capability also makes it possible to do new types of analyses such asmixture discrete-time survival analysis and non-ignorable missing data modeling.Another expansion is that latent class indicator variables can now be binary and/orordered categorical. In addition, these variables can be repeated measures of the samevariable and have a growth model specified for them.Fit indices have been added including a chi-square test against the unrestricted modelfor the latent class indicators, an entropy measure, and a classification table. Factorscores are also available for mixture models along with many additions to the outputincluding modification indices, standardized parameter estimates, and missing datainformation. In addition, training data can now include fractional class probabilities.The mixture model algorithms have been robustified numerically to avoid computationalfailures and error messages have been added to aid in diagnosing estimation problems.More Fit IndicesMore fit indices are available for all models. The additional fit indices include SRMR,CFI, and TLI. RMSEA has been added for all models that did not have it previously.In addition, a new fit index has been developed called WRMR. This index uses residualsweighted by their standard deviations which is particularly useful for models that includesample statistics on different scales such as models with mean or threshold structures.Factor Scores For More ModelsFactor scores are now available for all models in Mplus except models with clustereddata and for exploratory factor analysis. This includes models with ordered categoricaloutcomes and combinations of categorical and continuous outcomes and also models with missing data.Monte Carlo Capabilities For Mixture ModelsThe Monte Carlo facilities in Mplus have been expanded in Version 2. Data can now begenerated for mixture models within the Monte Carlo facility in Mplus.New Convenience FeaturesRevised Mplus User's GuideThe Mplus User's Guide has been revised for Version 2. Important additions are new examplesfor mixture models and expanded output descriptions.Expanded Language GeneratorVersion 2 of Mplus includes a language generator to assist in preparing input files.The language generator takes users through a series of screens to help them quickly setup an Mplus input file. The language generator contains all of the Mplus commands exceptDEFINE, MODEL, and MONTECARLO.Additional Data Saving FeaturesVersion 2 of Mplus allows users to save many analysis results that were previouslyonly printed in the output. These include sample statistics in the form of correlationor covariance matrices, the estimated sigma-between covariance or correlation matrix andthe sample pooled-within correlation and covariance matrices for multilevel models, thecovariance matrix of parameter estimates, and the means and covariance matrix for thelatent variables. All of these results are saved using an E15.8 format. In addition,an identification variable can be saved to aid in merging with other data sets outside ofMplus.Parameter estimates, standard errors of the parameter estimates, and fit statisticscan also be saved. This is useful for Monte Carlo studies when data have been generatedoutside of Mplus, and Mplus is used for subsequent analyses. A DOS batch file to aid inthese types of analyses is available with Version 2 on the Mplus website.More Output OptionsThe Mplus output has been expanded to include new information. Users can now requestthat analysis results be presented as confidence intervals in addition to the standard format.When modeling with missing data, a summary of the missing data patterns in the data being analyzed is now printed.It is also now possible to request the standard errors for the H1 model and the estimated covarianceand correlation matrices for the parameter estimates of the H1 model. Factor scorecoefficients and a factor score posterior covariance matrix can be requested for confirmatoryfactor analysis models with continuous factor indicators. An option also exists forobtaining a factor score determinacy value for each factor in the model.Increased Computational SpeedThe program now executes faster for most problems. In particular, improvementshave been made for mixture problems. For mixture models, speed is also improved dueto a change from using a pre-specified number of iterations to automatically terminatingiterations after convergence.Mplus Version 1, November 19, 1998