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Mplus Demo 8.3

Latent variable modeling program

Description

Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program.

Readme

# Start Mplus Demo
turbo run mplus-demo

# Start Mplus Demo with an example
turbo new mplus-demo,mplus-demosamples -- "@DOCUMENTS@\Mplus\Continuous outcome analyses examples\cont3.inp"

The demo version contains all of the capabilities of the regular version of Mplus:

  • Linear regression with multivariate outcomes
  • Probit regression for binary and ordered categorical variables with multivariate outcomes
  • Logistic regression for binary, ordered, and unordered categorical variables
  • Path analysis
  • Exploratory and confirmatory factor analysis
  • Confirmatory factor analysis with covariates
  • General structural equation modeling
  • Growth modeling
  • Survival analysis
  • Time series analysis
  • Multilevel modeling
  • General latent variable mixture modeling

The Mplus demo version is limited only by the number of variables that can be used in an analysis:

  • Maximum number of dependent variables: 6
  • Maximum number of independent variables: 2
  • Maximum number of between variables in two-level analysis: 2
  • Maximum number of continuous latent variables in time series analysis: 2

Release Notes

No release notes

EULA