Multilevel SEM Modeling with xxM

How to estimate a multilevel SEM model containing both observed and latent variables and any number of dependent levels.
3.1 (8 ratings) Instead of using a simple lifetime average, Udemy calculates a
course's star rating by considering a number of different factors
such as the number of ratings, the age of ratings, and the
likelihood of fraudulent ratings.
544 students enrolled
$19
$25
24% off
Take This Course
  • Lectures 46
  • Length 6.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
Wishlisted Wishlist

How taking a course works

Discover

Find online courses made by experts from around the world.

Learn

Take your courses with you and learn anywhere, anytime.

Master

Learn and practice real-world skills and achieve your goals.

About This Course

Published 9/2015 English

Course Description

Multilevel modeling is a term alternately used to describe hierarchical linear models, nested models, mixed-effects models, random-effects models, and split-plot designs. They are statistical models for estimating parameters that vary at more than one level and which may contain both observed and latent variables at any level. They are generalizations of linear models, particularly linear regression, although they may be extended to non-linear models.

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 xxM is the only software tool in the world that is capable of estimating the effects of both observed and latent variables in a SEM nomological network across an unlimited number of levels.

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 xxM. To convey a practical understanding of implementing the core model specification and construction concepts of xxM, seven complete illustrative examples are detailed over the six class sessions. One who completes this course will then be able to construct more complex multilevel models tied to their own research projects. The seven complete examples detailed in the course begin with: (1) a streamlined two-level bivariate random-intercepts model; and (2) a two-level random-slopes model. Then a (3) multilevel confirmatory factor analysis (CFA) and a (4) random-slopes multilevel CFA are detailed, followed by random-slopes (5) 'wide' and (6) 'long' latent growth curve model examples. Finally, a (7) three-level hierarchical model containing both observed and latent variables is fully demonstrated. All of the necessary software, data, manuals, slides and course materials to productively specify and estimate all seven of the course model examples are provided and included in 'resources' folders associated with the video lessons.

What are the 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.

What am I going to get from this course?

  • 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.

What 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to xxM and Multilevel Modeling
Introduction to Course
01:32
09:39

Multilevel models (also hierarchical linear models, nested models, mixed models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level.[1] These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available.

10:42

xxM is a package for multilevel structural equation modeling (ML-SEM) with complex dependent data structures. xxM implements a modeling framework called n-Level Structural Equation Modeling (NL-SEM) and can estimate models with any number of levels. Observed and latent variables are allowed at all levels.

More xxM Description and Explanation (slides)
07:44
09:47

A mixed model is a statistical model containing both fixed effects and random effects. These models are useful in a wide variety of disciplines in the physical, biological and social sciences. They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units. Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA.

Mixed-Effects Models: Important Concepts (slides, part 2)
12:06
09:52

LISREL (which stands for “linear structural relations”) involves eight matrices that organize the causal paths, loadings, correlations, and error terms in any model. Although this makes a cumbersome syntax, it is used in the mathematical description of SEM in the vast majority of statistical articles. A shortened, simpler four-matrix version can be used instead for specifying models in LISREL and is presented in some articles.

13:51

The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.

Section 2: Bivariate Random Intercepts Model (BRIM) Example
11:02

This tutorial introduces core concepts of xxM as a modeling framework and as a software package. Key elements of model specification in xxM are introduced in the context of fitting a bivariate random-intercepts model (Mehta, Neale, & Flay, 2005). Although the example is relatively trivial, once you understand the building blocks presented in this tutorial, you should be able to construct complex models easily.

The presentation is in three sections:

  1. Bivariate random-intercepts model: Representation using four different perspectives.
    • Multilevel modeling (MLM)
    • xxM
    • Linear mixed-effects (LME) model
    • Path diagram
  2. Description of the process and steps of fitting the model in xxM.
  3. Code listing
    • xxM: An annoated summary of a session of fitting the model
11:48

A random intercepts model is a model in which intercepts are allowed to vary, and therefore, the scores on the dependent variable for each individual observation are predicted by the intercept that varies across groups. This model assumes that slopes are fixed (the same across different contexts). In addition, this model provides information about intraclass correlations, which are helpful in determining whether multilevel models are required in the first place.

BRIM (slides, part 3)
11:38
BRIM Preliminaries with xxM Scripts
Preview
06:31
Fit and Estimate BRIM with xxM Scripts (part 1)
10:17
Fit and Estimate BRIM with xxM Scripts (part 2)
08:57
Section 3: Random Intercept and Slope (RANSLP) Model Example
Errata (in advance) to RANSLP Description that Follows
06:47
RANSLP Model Description (slides, part 1)
08:18
09:48

A model that includes both random intercepts and random slopes is likely the most realistic type of model, although it is also the most complex. In this model, both intercepts and slopes are allowed to vary across groups, meaning that they are different in different contexts.

RANSLP Model Fit and Estimation (slides and script, part3)
Preview
08:04
RANSLP Model Fit and Estimation (slides and scripts, part 4)
08:22
RANSLP Model Fit and Estimation (scripts, part 5)
10:29
Conclude RANSLP Model Fit and Estimation (scripts, part 6)
10:09
Section 4: Multilevel Confirmatory Factor Analysis (CFA) Examples
10:00

In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research.

Multilevel CFA (slides, part 2)
07:10
Multilevel CFA (slides, part 3)
07:09
Multilevel CFA (slides, part 4)
08:28
Multilevel CFA (slides, part 5)
05:56
Multilevel CFA Fit and Estimation (scripts, part 1)
Preview
07:09
Multilevel CFA Fit and Estimation (scripts, part 2)
07:51
Random Slope Multilevel CFA Example (slides, part 1)
06:03
Random Slope Multilevel CFA Example (slides, part 2)
07:45
Random Slope Multilevel CFA Fit and Estimation (scripts)
04:50
Section 5: Random Slopes, Wide- and Long- Latent Growth Curve Models Examples
Review RANSLP (part 1)
10:17
Review RANSLP (part 2)
07:50
10:21

Latent growth modeling is a statistical technique used in the structural equation modeling (SEM) framework to estimate growth trajectory. It is a longitudinal analysis technique to estimate growth over a period of time. It is widely used in the field of behavioral science, education and social science. It is also called latent growth curve analysis. The latent growth model was derived from theories of SEM. General purpose SEM software, such as OpenMx, lavaan (both open source packages based in R), AMOS, Mplus, LISREL, or EQS among others may be used to estimate the trajectory of growth. The R xxM package can estimate the trajectory of growth using multilevel models comprised of both latent (SEM-like) and observed (directly-measured) variables.

Long Format LGC Example (script, part 1)
08:52
Long Format LGC Example (script, part 2)
09:52
Wide Format LGC Example Slides and Script (part 1)
Preview
09:08
Wide Format LGC Example Script (part 2)
10:07
Section 6: Three-Level Hierarchical Model Example
10:28

Multilevel models (also hierarchical linear models, nested models, mixed models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level.[1] These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available.

3-Level Hierarchical Model Specification (slides, part 2)
07:51
3-Level Hierarchical Model Specification (slides, part 3)
10:07
3-Level Hierarchical Model Specification (slides, part 4)
09:39
3-Level Hierarchical Model Specification (slides, part 5)
07:35
3-Level Hierarchical Model Specification (slides, part 6)
06:12
Estimate 3-Level Hierarchical Model in xxM (part 1)
Preview
07:59
Estimate 3-Level Hierarchical Model in xxM (part 2)
07:18

Students Who Viewed This Course Also Viewed

  • Loading
  • Loading
  • Loading

Instructor Biography

Geoffrey Hubona, Ph.D., Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

Ready to start learning?
Take This Course