Mplus 6.12 Base Program And Combination Add-on
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How to Use Mplus 6.12 Base Program and Combination Add-on for Advanced Statistical Modeling
Mplus is a powerful software package that allows researchers to perform a wide range of statistical analyses, such as regression, factor analysis, structural equation modeling, growth modeling, mixture modeling, multilevel modeling, and survival analysis. Mplus 6.12 is the latest version of the software, which includes several new features and enhancements.
One of the most useful features of Mplus 6.12 is the Combination Add-on, which combines the Mixture Add-on and the Multilevel Add-on. The Combination Add-on enables researchers to estimate models that involve both latent class variables and clustered data. For example, researchers can use the Combination Add-on to conduct latent class growth analysis with multilevel data, latent transition analysis with longitudinal data, or finite mixture models with complex survey data.
In this article, we will provide a brief overview of how to use Mplus 6.12 Base Program and Combination Add-on for advanced statistical modeling. We will assume that the reader has some familiarity with Mplus syntax and basic concepts. For more detailed information and examples, we recommend consulting the Mplus User's Guide and the Mplus website.
Installing Mplus 6.12 Base Program and Combination Add-on
To install Mplus 6.12 Base Program and Combination Add-on, you need to have a valid license for both products. You can purchase a license from the Mplus website or from an authorized distributor. The pricing for different types of licenses (university, commercial, student) and discounts for multiple copies are also available on the website.
Once you have obtained a license, you can download the installation file from the Mplus website. The installation file contains both the Base Program and the Combination Add-on. You need to run the installation file and follow the instructions on the screen. You will be asked to enter your license number and agree to the terms and conditions of use. After the installation is complete, you can launch Mplus from the Start menu or from your desktop.
Specifying Models with Mplus 6.12 Base Program and Combination Add-on
To specify models with Mplus 6.12 Base Program and Combination Add-on, you need to create an input file that contains the commands and options for your analysis. You can create an input file using any text editor, such as Notepad or WordPad. Alternatively, you can use the Mplus Editor, which is a graphical user interface that helps you write and edit input files.
An input file consists of several sections: TITLE, DATA, VARIABLE, DEFINE (optional), ANALYSIS, MODEL (optional), OUTPUT (optional), SAVEDATA (optional), PLOT (optional), MONTECARLO (optional). Each section begins with a keyword followed by a colon (:). The sections must appear in the order listed above, except for DEFINE, MODEL, OUTPUT, SAVEDATA, PLOT, and MONTECARLO sections which can be omitted or interchanged.
The TITLE section gives a name to your analysis. The DATA section specifies the location and format of your data file. The VARIABLE section defines the names and types of your variables. The DEFINE section allows you to create new variables or modify existing ones using arithmetic or logical expressions. The ANALYSIS section sets the estimation method and other options for your analysis. The MODEL section describes the model that you want to estimate using equations or diagrams. The OUTPUT section requests additional output such as standard errors or modification indices. The SAVEDATA section saves results such as parameter estimates or residuals to a new file. The PLOT section generates graphs of your results such as path diagrams or growth curves. The MONTECARLO section simulates data from a specified model for power analysis or testing purposes.
For example, suppose you want to estimate a latent class growth model with three classes and two random intercepts for a continuous outcome variable y measured at four time points (t1-t4) using multilevel data with individuals nested within schools. You also want to include two covariates (x1 and x2) at the individual level and one covariate (w) at
the school level in your model. Your data file is called lcgm.dat and has one row per individual with missing values coded as -99.
A possible input file for this analysis is: