Mplus

TL;DR

• Software for fitting latent variable models (e.g., SEM, growth-curve, multilevel models).
• Handles missing data using algorithms that impute missing values to allow use of all collected data.
• Flexible: supports linear and nonlinear models and lets users specify custom models.

Definition

Mplus is a statistical software program commonly used for latent variable modeling, which includes techniques such as structural equation modeling (SEM), growth curve modeling, and multilevel modeling.

Explanation

Mplus enables researchers to fit a wide range of complex models to their data. Key features described in the source material include:

• Missing-data handling: Mplus uses algorithms to impute missing values so researchers can use all collected data and obtain more reliable results.
• Model flexibility: the program supports both linear and nonlinear models and allows specification of custom models, making it applicable across varied research designs and contexts (including experimental and observational studies).

Examples

Structural equation modeling

A researcher studies the relationship between self-esteem and body satisfaction. They posit a direct relationship (higher self-esteem → higher body satisfaction) and an indirect relationship (higher self-esteem → more positive attitudes towards their bodies → higher body satisfaction). The researcher collects data on self-esteem and body satisfaction, specifies a structural equation model in Mplus including direct and indirect effects, and uses Mplus to fit the model and estimate effect strengths.

Growth curve modeling

A researcher studies how a person’s weight changes over time, believing weight is influenced by factors such as age, gender, and initial weight, and that these factors may have different effects at different times. The researcher collects longitudinal weight data, specifies a growth curve model in Mplus including effects of age, gender, and initial weight, and uses Mplus to fit the model and estimate the effects at different points in time.

Use cases

• Analyses in experimental and observational study designs.
• Studies focused on complex relationships among observed and latent variables.
• Situations requiring custom model specification or nonlinear model fitting.

Notes or pitfalls

• Missing data can be a major issue in statistical analyses because it may lead to biased or incorrect results; the source notes that Mplus addresses this by using sophisticated imputation algorithms.

Related terms

• Latent variable modeling
• Structural equation modeling (SEM)
• Growth curve modeling
• Multilevel modeling