Mplus Version History

Mplus Version 1 was first released in November 1998. Since that time, Mplus has undergone four major version updates with a few minor updates for each major version. The current Mplus version is Version 5.1. Here is an approximate timeline of Mplus release dates:

Version 5	November 19, 2007
Version 4	February 24, 2006
Version 3	March 29, 2004
Version 2	February 2, 2001
Version 1	November 19, 1998

Mplus Version 5.1

Version 5.1 contains corrections to minor problems that have been found since the release of Version 5, general speed improvements, and the following new features. See the Version 5.1 Language Addendum and the Version 5.1 Examples Addendum for further information.

Exploratory Structural Equation Modeling

• EFA factors specified in the MODEL command
• Target rotation allowing user-specified rotation targets
• Factor scores for EFA factors
• Monte Carlo simulation for EFA factors

Exploratory Factor Analysis

• Change of the default rotation from oblique QUARTIMIN to oblique GEOMIN
• Row standardization

Missing Data Modeling

• Auxiliary m setting to specify variables that are missing data correlates in addition to the analysis variables for TYPE=GENERAL with continuous outcomes and maximum likelihood estimation

New Models For Count Variables

• Negative binomial model with a dispersion parameter
• Zero-inflated negative binomial model
• Zero-truncated negative binomial model
• Negative binomial hurdle model

Mixture Modeling

• Auxiliary r option for investigating covariates not in the model that predict latent class membership using pseudo-class draws

Multilevel Modeling

• Speed improvements for two-level weighted least squares estimation with many categorical outcomes and many latent variables
• Convergence improvements with near-singular estimated between-level covariance matrices
• Option to fix unrestricted model to sample statistics or estimate the unrestricted model in TYPE=TWOLEVEL EFA
• Automatic creation of cluster-mean variables
• Eigenvalue plots for within- and between-level correlation matrices

General Features

• Three new functions in the DEFINE command: SUM, MEAN, and CLUSTER_MEAN
• Expanded MODEL CONSTRAINT features

Monte Carlo Simulation

• New data generation options for count variables
• Monte Carlo simulation for EFA factors specified in the MODEL command

New Technical Appendices

• Exploratory structural equation modeling
• Auxiliary variables predicting missing data

New Examples

• 1: EFA with covariates (MIMIC) with continuous factor indicators and direct effects
• 2: SEM with EFA and CFA factors with continuous factor indicators
• 3: EFA at two timepoints with factor loading invariance and correlated residuals across time
• 4: Multiple-group EFA with continuous factor indicators
• 5: Monte Carlo simulation study for a multiple-group EFA with continuous factor indicators with measurement invariance of intercepts and factor loadings
• 6: GMM for a count outcome using a negative binomial model

Mplus Version 5

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 Processors

• Mplus Version 5 is available not only on 32-bit but also on 64-bit operating systems allowing the following memory capacity
  • 32-bit Mplus on 32-bit machine: 2GB, or 3GB if system booted with /3GB
  • 32-bit Mplus on 64-bit machine: 4GB
  • 64-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 Features

• Standard errors for standardized solutions and R-square
• P-values
• Standardized and normalized residuals
• New option: MODEL = NOCOVARIANCES which fixes all covariance parameters at zero
• Default changes: MISSING, MEANSTRUCTURE, H1 as the default
• New options: LISTWISE = ON, NOMEANSTRUCTURE, NOCHISQUARE
• Saving standardized results

Exploratory Factor Analysis

• Additional factor loading matrix rotations: Quartimin, Geomin, and many others
• Standard errors for rotated loadings and factor correlations
• Non-normality robust standard errors and chi-square tests of model fit
• Modification indices for residual correlations
• Maximum likelihood estimation with censored, categorical, and count variables
• Exploratory factor analysis for complex survey data (stratification, clustering, and weights)
  TYPE = COMPLEX EFA # #;
• Exploratory factor mixture analysis with class-specific rotations
  TYPE = MIXTURE EFA # #;
• Two-level exploratory factor analysis for continuous and categorical variables with new rotations and standard errors, including unrestricted model for either level
  TYPE = TWOLEVEL EFA # # UW # # UB;

Mixture Modeling

• Faster computations using random starts distributed over several processors
  PROCESSORS = 4 (STARTS);
• Equality tests of means across classes for variables not in the model using posterior probability-based multiple imputations
  AUXILIARY = x1-x10(e);
• Modified TECH14 LRTSTARTS and new K-1STARTS option
• New language for c ON c and u ON x
• TECH10 and chi-square for counts
• P-values saved for TECH11 and TECH14 with Monte Carlo simulation

Multilevel Modeling

• Simple two-level limited-information weighted least squares estimator for categorical variables
  • computational demand virtually independent of number of factors/random effects
  • high-dimensional integration replaced by multiple instances of one- and two-dimensional integration
  • generalization of the Muthen (1984) single-level WLS
  • possible to explore many different models in a time-efficient manner
  • variables can be categorical, continuous, combinations
  • residuals can be correlated (no conditional independence assumption)
  • model fit chi-square testing
  • can produce unrestricted level 1 and level 2 correlation matrices for EFA
  • saving sample statistics and weight matrix for subsequent analyses
    TYPE = TWOLEVEL;
    ESTIMATOR = WLSM;

• Improved integration algorithms for two-level mediation models

New Technical Appendices

• Standardized Coefficients and Their Standard Errors
• Standardized and Normalized Residuals
• Mixture Exploratory Factor Analysis
• Equality Test of Means Across Latent Classes Using Wald Chi-Square Based on Draws From Posterior Probabilities
• Two-Level Weighted Least Squares Estimation. Proceedings of the Joint Statistical Meeting, August 2007, Biometrics Section

New Examples in the Version 5 Mplus User's Guide

• 4.1: Exploratory factor analysis with continuous factor indicators
• 4.2: Exploratory factor analysis with categorical factor indicators
• 4.3: Exploratory factor analysis with continuous, censored, categorical, and count factor indicators
• 4.4: Exploratory factor mixture analysis with continuous latent class indicators
• 4.5: Two-level exploratory factor analysis with continuous factor indicators
• 4.6: Two-level exploratory factor analysis with both individual- and cluster-level factor indicators
• 6.18: Multiple group multiple cohort growth model
• 9.1: Two-level regression analysis for a continuous dependent variable with a random intercept
• 9.2: Two-level regression analysis for a continuous dependent variable with a random slope
• 9.4: Two-level path analysis with a continuous, a categorical, and a cluster-level observed dependent variable
• 9.9: Two-level SEM with categorical factor indicators on the within level and cluster-level continuous observed and random intercept factor indicators on the between level
• 9.15: 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 slopes

Mplus Version 4.21

Version 4.21 contains general improvements to statistical algorithms and corrections of minor problems that have been found since Version 4.2.

Mplus Version 4.2

Version 4.2 contains corrections to minor problems that have been found since the release of Version 4.1 along with the following changes.

Speed Improvements

• Multi-processing speed gains due to parallel computations with dual/multi-core processor computers and computers that support Hyper-Threading Technology
• Overall speed improvements
• Speed improvements for examples with multiple latent class variables

Timing trials were carried out for several examples using different computer configurations. Click here to see the results of the timing trials.

Two-level Modeling With Multiple Latent Class Variables

• Two-level latent transition analysis and Markov modeling (see User's Guide Addendum Example 7)

Modeling With Between-Level Latent Class Variables

• Two-level regression with between-level mixtures for regression coefficients (see User's Guide Addendum Examples 1 and 2)
• Two-level IRT mixture analysis with between-level classes (see User's Guide Addendum Example 3)
• Two-level latent class analysis with between-level classes (see User's Guide Addendum Example 4)
• Two-level growth analysis with between-level classes (see User's Guide Addendum Example 5)
• Two-level growth mixture analysis with between-level classes (see User's Guide Addendum Example 6)
• Two-level latent transition analysis with between-level classes (see User's Guide Addendum Example 8)

New Options For Complex Survey Data Weights With TYPE=TWOLEVEL

• Level-1 and level-2 weights
• Options for different scaling of weights such as scaling to cluster size or effective cluster size

Click here for technical details about the scaling of sampling weights for two-level models.

Indirect Effects In Monte Carlo Simulation Studies

• Indirect effect Monte Carlo summaries with MODEL INDIRECT

Added Output For Exploratory Factor Analysis

• Factor structure
• Factor determinacy

Added Output for TYPE = Imputation

• Standardized solution and R-square

Response Pattern Classification

• Response pattern summaries for categorical outcomes arranged by frequency, latent class, factor scores, and posterior probabilities

Added Mixture Output

• Odds ratios for regression of latent class variable on covariates

Added Output For Estimates In IRT Metric

• WLSMV estimator
• ML estimator with probit link

New Graphics Features

• IRT curves also for the WLSMV estimator and ML estimator with probit link
• Added options for highlighting individual curves
• Posterior probabilities added to the graphing information
• Offsetting feature for overlapping mean curves
• Window-management options for adjusted mean curves

Mplus Version 4.1

Version 4.1 contains corrections of minor problems that have been found since the release of Version 4 along with the following changes.

Algorithmic Changes

• Speed has been improved for mixture models that use bootstrap standard errors
• Speed has been improved and new options added for the parametric bootstrapped likelihood ratio test for the number of classes in mixture models (TECH14)
• The algorithm for count variables has been improved

Item Response Theory (IRT) Additions

• Item Characteristic Curves and Information Curves are now available in the PLOT command
• Output in 2PL IRT metric is provided with maximum likelihood estimation of one- factor models with binary items

Other New Features

• The CONSTRAINT option of the VARIABLE command is available with numerical integration
• The BOOTSTRAP option is available with sampling weights
• The LRTSTARTS option and the LRTBOOTSTRAP options have been added to be used in conjunction with TECH14

The LRTSTARTS option is used in conjunction with the TECH14 option of the OUTPUT command to specify for the k-1 and k class models the number of random sets of starting values to generate in the initial stage and the number of optimizations to use in the final stage.

The LRTBOOTSTRAP option is used in conjunction with the TECH14 option of the OUTPUT command to specify the number of bootstrap draws to be used in estimating the p-value of the parametric bootstrapped likelihood ratio test (McLachlan & Peel, 2000). The default number of bootstrap draws is determined by the program using a sequential method in which the number of draws varies from 2 to 100.

Mplus Version 4

Mplus Version 4.0 contains several new general features as well as features specific to multilevel modeling and complex survey data analysis, mixture modeling, and analysis with categorical outcomes. Mplus Version 4.0 also introduces continuous-time survival analysis that is fully integrated into the general latent variable modeling framework of Mplus. Some of the new features such as those related to parameter constraints make twin, sibling, and family genetics modeling convenient and flexible.

The Version 4 Mplus User's Guide contains 19 new examples and 19 examples revised from their earlier versions to make the input simpler. The new examples are listed at the end of this description. Chapter 13 of the Mplus User's Guide has three new sections that describe the testing of measurement invariance for both continuous and categorical outcomes, how to avoid local maxima in mixture modeling, and the calculation of probabilities from probit regression coefficients. The Version 4 Mplus User's Guide is available online.

Following is a list of the new features in Mplus Version 4.0. The page numbers in parentheses refer to the pages in the Version 4 Mplus User's Guide where these features are described. An asterisk following the page numbers indicates that these pages describe a new example.

General Features

Data transformation

• Rearrange data from wide to long format (246-248*, 371-373)
• Rearrange data from long to wide format (373-375)
• Create a binary and a continuous variable from a continuous variable with a floor effect for use in two-part (semicontinuous) modeling (105-107*, 301-303*, 375-378)
• Create binary missing data indicators for missing data modeling (378-379)
• Create binary event-history variables for discrete-time survival modeling (379-381)
• Rearrange longitudinal data from a format where time points represent measurement occasions to a format where time points represent age or another time-related variable (381-384)

Extension of the MODEL CONSTRAINT command (484-487)

• Including parameters not used in the MODEL command (72-73*, 74-75*, 75-76*, 76-77*, 164-166*, 167-169*, 485)
• Implicit constraints (72-73*, 487)
• Parameter can appear on both LHS and RHS (72-73*, 487)
• Parameter can appear more than once on the LHS
• Inequality constraints (72-73*)
• Constraints involving observed variables (76-77*, 399-400, 486)

Wald test of parameter constraints (487-488)

Simplified language for equality constraints involving a list of parameters (465-466)

Bollen-Stine (residual) bootstrapped standard errors and chi-square p-value (434-435)

Display of outliers and influential observations (530-531)

• Mahalanobis distance
• Individual log likelihood contribution
• Influence of each observation on the fitting function, likelihood displacement
• Influence of each observation of the parameter estimates, Cook's D

Allowing a singular sample covariance matrix for covariates

Log-likelihood correction factors for chi-square testing with ESTIMATOR = MLR

Printing of chi-square contribution from each group in multiple-group analysis

Monte Carlo simulations of missing data expanded to let the missing data probability be a function of dependent variables that are continuous, censored, binary, ordered categorical, and counts, enabling, for example, generation of discrete-time survival data (299-300*), two-part data (301-303*), and data with non-ignorable missingness

Overall speed improvements

Interactive controls during iterative optimization (442-444)

Multilevel Modeling and Complex Survey Data Analysis

Two-level modeling with censored, nominal, and count outcomes (249-251*)

Complex survey data options (400-403)

• Analysis of three-level data using TYPE = COMPLEX TWOLEVEL;
• Subpopulation analysis
• Improved treatment of a single PSU in a stratum

Reduction of dimensions of numerical integration for certain multilevel models using maximum likelihood estimation

Improved multiple-group analysis of complex survey categorical data using WLS, WLSM, and WLSMV estimation

Two-level MODEL INDIRECT

Between-level factor scores for random intercepts

Random slopes for dependent variables without numerical integration

Mixture Modeling

Bootstrapped parametric likelihood ratio test to determine the number of classes in mixture modeling (519)

Training data in the form of prior probabilities for latent classes (406)

Extended output for modeling with categorical latent variables

• Alternative reference classes for displaying regression coefficients for a categorical latent variable regressed on covariates
• TECH7 and RESIDUAL output for covariates in models with categorical outcomes

Analysis of Categorical Outcomes

Choice of probit or logit link for maximum likelihood estimation (428)

Improvements of bootstrapped standard errors for categorical data analysis with WLSM and WLSMV

Improvements in handling zero cells with categorical data analysis using WLS, WLSM, and WLSMV including a new option to add a value to the frequency of each cell (438)

Modified check of information matrix singularity for categorical data analysis using WLS, WLSM, and WLSMV estimation

Continuous-Time Survival Analysis

Cox regression - non parametric estimation (111-112*)

Parametric proportional hazards model - baseline hazard as a step function (112-113*)

Cox regression with random effects

Parametric proportional hazards model with random effects (114-115*)

Cox regression mixture model (203-204*)

Parametric proportional hazards mixture model

Multilevel Cox regression (251-252*)

Multilevel parametric proportional hazards model

Twin, Sibling, and Family Genetics Modeling

Two-group twin model for continuous outcomes where factors represent the ACE components (68-70*)

Two-group twin model for categorical outcomes where factors represent the ACE components (70-72*)

Two-group twin model for continuous outcomes using parameter constraints (74-75*)

Two-group twin model for categorical outcomes using parameter constraints (75-76*)

QTL sibling model for a continuous outcome using parameter constraints (76-77*)

Two-group twin model for categorical outcomes using maximum likelihood and parameter constraints (164-166*)

Two-group IRT twin model for factors with categorical factor indicators using parameter constraints (167-169*)

New Examples in the Version 4 Mplus User's Guide

5.18: Two-group twin model for continuous outcomes where factors represent the ACE components

5.19: Two-group twin model for categorical outcomes where factors represent the ACE components

5.20: CFA with parameter constraints

5.21: Two-group twin model for continuous outcomes using parameter constraints

5.22: Two-group twin model for categorical outcomes using parameter constraints

5.23: QTL sibling model for a continuous outcome using parameter constraints

6.20: Continuous-time survival analysis using the Cox regression model

6.21: Continuous-time survival analysis using a parametric proportional hazards model

6.22: Continuous-time survival analysis using a parametric proportional hazards model with a factor influencing survival

7.27: Factor mixture (IRT) analysis with binary latent class and factor indicators

7.28: Two-group twin model for categorical outcomes using maximum likelihood and parameter constraints

7.29: Two-group IRT twin model for factors with categorical factor indicators using parameter constraints

8.16: Continuous-time survival mixture analysis using a Cox regression model

9.16: Linear growth model for a continuous outcome with time-invariant and time-varying covariates carried out as a two-level growth model using the DATA WIDETOLONG command

9.17: Two-level growth model for a count outcome using a zero-inflated Poisson model (three-level analysis)

9.18: Two-level continuous-time survival analysis using Cox regression with a random intercept

11.8: Monte Carlo simulation study for discrete-time survival analysis

11.9: Monte Carlo simulation study for a two-part (semicontinuous) growth model for a continuous outcome

11.10: Monte Carlo simulation study for a two-level continuous-time survival analysis using Cox regression with a random intercept

Mplus Version 3.13

Standard error estimation and chi-square model testing is now possible with complex survey data obtained by stratified sampling. This new feature avoids standard errors that are too large due to ignoring stratification. A description of how stratification is implemented in Mplus can be found in Mplus Web Note #9. Click here to download Mplus Web Note #9.

The STRATIFICATION option of the VARIABLE command is used in conjunction with TYPE=COMPLEX to identify the variable that contains stratification information. The STRATIFICATION option can be used in conjunction with the CLUSTER and WEIGHT options of the VARIABLE command.

Improved Optimization Algorithms With Numerical Integration

For analyses requiring numerical integration, the computational algorithm has been improved. This results in faster computations and also allows the use of fewer integration points which further increases computational speed.

Keyboard Shortcuts

Two keyboard shortcuts have been created, one to run Mplus and a second to view graphs.

Run Mplus - Alt+R
View graphs - Alt+V

Mplus Version 3

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 Carlo simulation 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 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, and growth models. The Mplus Base Program includes statistical developments beyond Mplus Version 2. An example of a new feature is maximum-likelihood estimation with interactions between continuous latent variables using both continuous and categorical latent variable indicators. Another new feature that has been frequently asked for by users is indirect, specific indirect, and total effects with Delta method and bootstrap standard errors and asymmetric confidence intervals.

Click here for more information.

Mixture Add-On

The Mixture Add-On estimates models with categorical latent variables using latent classes. It provides latent class analysis, finite mixture modeling, and growth mixture modeling. It contains statistical developments beyond Mplus Version 2. An example of a new feature is fully automated starting values and automatic search for multiple maxima. Another new feature is the regression of one categorical latent variable on another categorical latent variable as in latent transition analysis and hidden Markov modeling.

Click here for more information.

Multilevel Add-On

The Multilevel Add-On estimates models for clustered data. It provides conventional multilevel analysis as well as new extensions that integrate multilevel modeling and SEM. The Multilevel Add-On contains statistical developments beyond Mplus Version 2. An example of one new feature is two-level logistic regression. Another example is two-level factor analysis with categorical indicators.

Click here for more information.

Combination Add-On

The Combination Add-On includes both the Mixture Add-On and the Multilevel Add-On. In addition, it includes models that handle both clustered data and latent classes at the same time. It contains statistical developments beyond Mplus Version 2. An example of one new feature is two-level latent class analysis. Another example is multilevel growth mixture modeling.

Mplus Version 2.14

Mplus Version 2.14 contains improved error messages and corrections of minor bugs that have been found since Version 2.13. No new developments are included in Version 2.14.

Mplus Version 2.13

Mplus Version 2.13 contains speed and output improvements for multilevel models with random slopes, convergence improvements for multilevel and mixture models, and new standard error options for missing data analysis.

Mplus Version 2.12

Mplus Version 2.12 contains three new output options to guide in choosing the number of latent classes in mixture modeling.

Mplus Version 2.1

Mplus Version 2.1 contains several additions and changes relative to Version 2.02. They are listed below:

• Multilevel modeling
• Missing data with non-normal outcomes
• Mixture modeling
• Modeling with categorical outcomes
• Convergence
• Monte Carlo

Multilevel Modeling

Mplus Version 2.1 contains new options for the analysis of multilevel data in both cross-sectional and longitudinal settings. Below is a description of these new features. Mplus Version 2.1 also includes multilevel Monte Carlo simulations. This is described in the section Monte Carlo.

Full-information maximum likelihood estimation for continuous outcomes is now available for TYPE=TWOLEVEL. Mean structures and multiple group analysis are allowed. TYPE=TWOLEVEL MISSING provides maximum likelihood estimation under the assumption of MAR. Non-normality robust standard errors and a chi-square test of model fit are available using ESTIMATOR=MLR which is the default. These standard errors are obtained using a sandwich estimator and the test of model fit is obtained using an extension of the Yuan-Bentler T2* test statistic (Sociological Methodology, 2000) to multilevel models. Random slopes for independent observed ariables are available for the regression of observed and latent dependent variables using TYPE=TWOLEVEL RANDOM.

New features are available for the analysis of longitudinal data with continuous outcomes using TYPE=RANDOM and TYPE=RANDOM MISSING. These include individually varying times of observation and random slopes for time-varying covariates. Unlike conventional structural equation modeling and in line with conventional multilevel modeling, individually varying times of observation are included as variables in the data set. Random slopes for time-varying covariates describe slope variation across individuals.

TYPE=COMPLEX MIXTURE can now be used to obtain standard errors corrected for non-independence of observations in mixture models. A sandwich estimator is used. Note that a non-mixture analysis can be obtained by using TYPE=COMPLEX MIXTURE and specifying one class. This provides an alternative to the standard errors in TYPE=COMPLEX.

Missing Data On Non-Normal Outcomes

Mplus Version 2.02 allowed missing data for continuous outcomes under the assumption of multivariate normality using maximum likelihood estimation under MAR. Version 2.1 has added standard errors and a chi-square test statistic for missing data with non-normal outcomes by using a sandwich estimator and the Yuan-Bentler T2* (Sociological Methodology, 2000) test statistic. This estimator can be selected for TYPE = MISSING by asking for ESTIMATOR = MLR.

Mixture Modeling

Several additions and changes have been made for mixture modeling in Mplus Version 2.1. A major improvement has been made with respect to computational speed. Computational time has been reduced by more efficient data handling and by improved optimization algorithms. The ALGORITHM option has been added to the ANALYSIS command. This option allows the choice of three optimizations algorithms: ODLL, EM, and EMA. ODLL optimizes the observed-data log-likelihood directly. EM optimizes the complete-data log-likelihood using the expectation-maximization (EM) algorithm. EMA is an accelerated EM procedure. EMA is used as the default.

Another improvement is the addition of sample weights to mixture modeling.

Additional model fit information is now available for the latent class indicator part of the model. This is obtained by using the TECH10 option of the OUTPUT command. TECH10 includes information about the cell frequency contributions to the chi-square and univariate and bivariate estimated probabilities and residuals.

In the estimation of the thresholds of the latent class indicator part of the model, thresholds approaching extreme values are fixed to the values specified in LOGHIGH and LOGLOW and are not included in the set of free parameters in the model. This results in fewer convergence problems due to a singular information matrix.

For all analyses, model identification status is now checked using the information matrix estimate of the MLF estimator because this has been found to be the most reliable check of identification of the model.

Mixture modeling with complex sample data has also been added using TYPE=COMPLEX MIXTURE. This is discussed in the multilevel modeling section above.

Modeling With Categorical Outcomes

Mplus Version 2.1 contains several additions and changes for the analysis of categorical dependent variables. Version 2.1 offers an alternative parameterization when one or more dependent variables are categorical. In addition, computational efficiency has been improved.

Alternative Parameterization

Mplus now allows users two options for the parameterization of the model when one or more dependent variables are categorical. The first parameterization is referred to as DELTA. This is the parameterization that has been available in earlier versions of Mplus and is the default in Version 2.1. In the DELTA parameterization, scale factors are allowed to be parameters in the model, but residual variances for latent response variables of observed categorical outcome variables are not. The second parameterization is referred to as THETA. In the THETA parameterization, residual variances for latent response variables of observed categorical outcome variables are allowed to be parameters in the model, but scale factors are not.

The DELTA parameterization was selected as the default because it has been found to perform better in many situations. The THETA parameterization is preferred when hypotheses involving residual variances are of interest such as with multiple group analysis and analysis of longitudinal data. In addition, there are certain models that can only be estimated using the THETA parameterization because they have been found to impose improper parameter constraints with the DELTA parameterization. These are models where a categorical dependent variable is both influenced by and influences either another observed dependent variable or a latent variable.

Computational Efficiency

Space allocation demands have been reduced in Mplus Version 2.1 for models where one or more dependent variables are categorical. This has resulted in the ability to analyze models with more variables. In addition, computational speed has been improved. A NOCHISQUARE option in the OUTPUT command has also been added to avoid the computation of chi-square for WLSM and WLSMV. This greatly reduces computation time for problems with many variables.

Convergence

Numerical robustification of optimization when variables are on very different scales has been implemented in the estimation of all models except TYPE=MIXTURE, TYPE=RANDOM, TYPE=TWOLEVEL when ESTIMATOR=ML, MLR, or MLF, and when one or more dependent variables are categorical. The SDITERATIONS option has been added to allow users to change the number of steepest descent iterations.

Monte Carlo

Two new Monte Carlo features have been added to Mplus Version 2.1. The MONTECARLO command has been expanded to include the generation of clustered data with and without missing data for single or multiple groups. Data can also be generated for analysis with random effects using the | symbol. The data generation model is allowed to be different from the analysis model.

An Addendum to the Mplus User's Guide, explaining the latest additions and changes found in Version 2.1, is available for download.

Mplus Version 2

Version 2 of Mplus contains several new features that make the analyses more flexible, more convenient, and faster.

• New Statistical Analysis Features
  • Expanded mixture modeling capabilities
  • More fit indices
  • Factor scores for more models
  • Monte Carlo capabilities for mixture models
  
  
• New Convenience Features
  • Revised Mplus User's Guide
  • Expanded language generator
  • Additional data saving features
  • More output options
  
  
• Increased Computational Speed

New Statistical Analysis Features

Expanded Mixture Modeling Capabilities

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 class indicators in the mixture part of the model and for the continuous observed outcomes in the structural 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 as mixture discrete-time survival analysis and non-ignorable missing data modeling.

Another expansion is that latent class indicator variables can now be binary and/or ordered categorical. In addition, these variables can be repeated measures of the same variable and have a growth model specified for them.

Fit indices have been added including a chi-square test against the unrestricted model for the latent class indicators, an entropy measure, and a classification table. Factor scores are also available for mixture models along with many additions to the output including modification indices, standardized parameter estimates, and missing data information. In addition, training data can now include fractional class probabilities. The mixture model algorithms have been robustified numerically to avoid computational failures and error messages have been added to aid in diagnosing estimation problems.

More Fit Indices

More 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 residuals weighted by their standard deviations which is particularly useful for models that include sample statistics on different scales such as models with mean or threshold structures.

Factor Scores For More Models

Factor scores are now available for all models in Mplus except models with clustered data and for exploratory factor analysis. This includes models with ordered categorical outcomes and combinations of categorical and continuous outcomes and also models with missing data.

Monte Carlo Capabilities For Mixture Models

The Monte Carlo facilities in Mplus have been expanded in Version 2. Data can now be generated for mixture models within the Monte Carlo facility in Mplus.

New Convenience Features

Revised Mplus User's Guide

The Mplus User's Guide has been revised for Version 2. Important additions are new examples for mixture models and expanded output descriptions.

Expanded Language Generator

Version 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 set up an Mplus input file. The language generator contains all of the Mplus commands except DEFINE, MODEL, and MONTECARLO.

Additional Data Saving Features

Version 2 of Mplus allows users to save many analysis results that were previously only printed in the output. These include sample statistics in the form of correlation or covariance matrices, the estimated sigma-between covariance or correlation matrix and the sample pooled-within correlation and covariance matrices for multilevel models, the covariance matrix of parameter estimates, and the means and covariance matrix for the latent 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 of Mplus.

Parameter estimates, standard errors of the parameter estimates, and fit statistics can also be saved. This is useful for Monte Carlo studies when data have been generated outside of Mplus, and Mplus is used for subsequent analyses. A DOS batch file to aid in these types of analyses is available with Version 2 on the Mplus website.

More Output Options

The Mplus output has been expanded to include new information. Users can now request that 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 covariance and correlation matrices for the parameter estimates of the H1 model. Factor score coefficients and a factor score posterior covariance matrix can be requested for confirmatory factor analysis models with continuous factor indicators. An option also exists for obtaining a factor score determinacy value for each factor in the model.

Increased Computational Speed

The program now executes faster for most problems. In particular, improvements have been made for mixture problems. For mixture models, speed is also improved due to a change from using a pre-specified number of iterations to automatically terminating iterations after convergence.