Multilevel SEM Modeling with xxM

608 students enrolled

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How to estimate a multilevel SEM model containing both observed and latent variables and any number of dependent levels.

608 students enrolled

What Will I Learn?

- Specify, model, and estimate either: (1) multilevel SEM models using latent variables; and/or (2) multilevel regression models using only observed data: and/or (3) mixed (both observed and latent variables) multilevel path-based models.
- Effectively use the only software that exists in the world (which is freely provided with the course materials) capable of N-Level (any number of levels) multilevel SEM modeling.
- Incorporate both observed and latent variables in a data-dependent structural network both within and across levels.
- Incorporate any number of data-dependent levels in the SEM model.
- Be able to include both fixed and random effects for the observed data across any number of dependent levels.
- Be able to specify both random-intercept and random-slopes multilevel SEM models.

Requirements

- Students will need to install no-cost R software and the provided xxM package. Both are freely-provided with the course materials and come with both written and video instructions.

Description

** Multilevel modeling** is a term alternately used to describe

**xxM** is an R package which can estimate multilevel SEM models characterized by complex level-dependent data structures containing both observed and latent variables. The package was developed at the University of Houston by a collaborative team headed by Dr. Paras Mehta. **xxM** implements a modeling framework called ** n-Level Structural Equation Modeling **(NL-SEM) which allows the specification of models with any number of levels. Because observed and latent variables are allowed at all levels, a conventional SEM model may be specified for each level and across any levels. Also, the random-effects of observed variables are allowed both within and across levels. Mehta claims that

Some of the complex dependent data structures that can be effectively modeled and estimated with **xxM**** **include:

⦁ Hierarchically nested data (e.g. students, classrooms, schools)

⦁ Longitudinal data (long or wide)

⦁ Longitudinal data with switching classification (e.g. students changing classrooms)

⦁ Cross-classified data (e.g. students nested within primary and secondary schools)

⦁ Partial nesting (e.g. underperforming students in a classroom receive tutoring)

Model specification with **xxM **uses a “LEGO-like building block” approach for model construction. With an understanding of these basic building blocks, very complex multilevel models may be constructed by repeating the same key building steps.

This six-session ** Multilevel SEM Modeling with xxM** course is an overview and tutorial of how to perform these key basic building block steps using

Who is the target audience?

- Anyone interested or involved with covariance-based structural equation modeling (SEM) or variance-based path modeling (for example, PLS path modeling) would benefit from taking this course.
- Anyone interested in acquiring with the course materials, and learning to use the only software in the world capable of N-Level multilevel modeling with both observed and latent variables would benefit from this course.
- The course is useful for anyone involved with multilevel modeling using either observed variables and/or latent variables.
- The course is relevant and helpful for undergraduate and graduate students involved with linear regression or linear mixed-effects modeling or SEM.
- Quantitatively-oriented working professionals (research scientists, data analytics professionals) who utilize regression, path modeling, and/or SEM would benefit from the course.
- It is helpful to have some knowledge about and understanding of linear regression before taking this course.
- It is helpful (but not essential) to have some knowledge of either path modeling and/or SEM using latent variables.

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About the Instructor

Professor of Information Systems