Multilevel Structural Equation Modeling with xxM Seminar

Multilevel SEM with xxM Statistics Seminar

An comprehensive 3-day Stats Camp seminar on Multilevel SEM with xxM.

This course teaches skills necessary to conduct analysis of complex multilevel data-structures from an nLevel Structural Equation Modeling perspective.

Conventional multilevel modeling and multilevel-structural equation modeling work well with ‘standard’ multilevel data. The n-Level structural equation modeling (NL-SEM) framework is intended for both conventional and non-standard data-structures.  Non-standard data are indeed very common across multiple domains but rarely analyzed in a satisfactory manner.

UCLA Stats Camp: September 17 – 19, 2018
9am – 5pm Daily
University of California Los Angeles

Seminar Venue:
UCLA – 5308B Math Sciences
View Class Location on a Map

Travel Information

Lunch and snacks plus select drinks including coffee, tea, and water will be provided. There will be a vegetarian option for lunch.

Question? Please contact us for more info or visit our FAQ page.

Enrollment is open to public, students, graduates and professionals. Save a seat today, pay later.

Payment Options

Seminar fee includes all materials, downloads, software access, training, refreshments and access to a recorded video of seminar:
$1,095 Faculty/Professional or
$945 Student/Post-Doc
Only 8 seats left. Register soon!

Topics include:

  • Longitudinal data with switching classification in which nesting of a lower- level unit within the higher-level unit changes over time. For example, students switch teachers and classrooms across grades.
  • Multiple ratings over time (longitudinal data with cross-classification and multiple membership). For example, each student may be rated by multiple different teachers and each teacher may rate multiple different students on multiple occasions. Such data are common in business settings as well.
  • Non-hierarchical teams. Each person may belong to multiple teams over time. Examples of such data are common in business settings. Other examples include patients treated by teams of doctors and nurses.
  • Dependent variables at “multiple lower levels.” For example, in a health-setting context we may have outcome variables for patients (satisfaction), nurses (job-satisfaction) and doctors (stress) over time, and nesting of patients within nurses and doctors may not be hierarchical.
  • Round-robin data where each person may rate multiple other individuals within a small group. Variants of such data are common in business & I/O psychology (360 evaluations, team-performance), social-psychology (person perceptions) and education (peer-evaluations).
  • Partially nested data. Only a subset of subjects are nested within a higher level unit. Such data occur commonly in “intervention studies” (medical, educational etc.) where individual subjects are randomly assigned to intervention and control conditions. Intervention itself is delivered in small groups leading to clustering; however, control subjects are not nested.
  • Data with multiple different types of dependencies and many levels. For example, large longitudinal state datasets with multiple cohorts of students nested within teachers, classrooms, schools and districts. Classification may change across grades.

Some aspects of such non-standard data can be handled within most MLM or ML-SEM packages. Currently available approaches, however, do not scale well with increasingly complex data-structures.  All of these models, on the other hand, are readily specified within the general NL-SEM perspective.  NL-SEM provides a broader and simpler conceptual framework and matching statistical software.  Within the NL-SEM framework all types of dependencies can be described using a consistent jargon-free language. Using the NL-SEM framework, all manner of models for nested data structures are easily specified and estimated using xxM, a free software platform.  This course is designed to introduce participants to the modeling mindset of the NL-SEM framework and gain ample experience with the xxM software package.

Learning Objectives

The three-day course will enable participants to:

  • Understand the ‘big-data’ nature of multilevel, latent variable modeling.
  • See the ‘big picture’ view of multilevel modeling from a single unified NL-SEM framework in which SEM and MLM are the simple building blocks.
  • Understand how different types of complex data-structures imply dependency among observed data.
  • Develop the skills necessary to translate complex multilevel data-structures and the corresponding multilevel hypotheses into a statistical model.
  • Become effective in the use of xxM for fitting multilevel latent variable models.
  • Become acquainted with a variety of novel and useful multilevel models in education, business and social-psychology.
  • Most importantly, the participants will begin to think of study designs within their area of research that can answer more interesting and novel research questions.


The first two days will re-introduce conventional Structural Equation Modeling (SEM) and Multilevel Modeling (MLM) from a unified nLevel SEM perspective.

The last day we will focus on models of complex multilevel data-structures that are often difficult to conceptualize within conventional MLM or ML-SEM frameworks. Real-world examples of complex data and challenging research questions will be used to teach principles and practice of multilevel SEM.  The approach will be practical with an emphasis on model fitting and interpretation of results.

Monday   September 17, 2018
9:00 – 10:45 Welcome and Introductions.
10:45 – 11:00 Snack and Refreshment Break.
11:00 – 12:30 Building blocks: Single level SEM in xxM
12:30 – 1:30 Lunch Break.
1:30 – 3:15 Confirmatory Factor Analysis
3:15 – 3:30 Snack and Refreshment Break.
3:30 – 5:00 Multiple-Group CFA
5:00 – 8:30 Meet and greet mixer with dinner – special event
Tuesday   September 18, 2018
9:00 – 10:45 Building blocks: Multilevel Modeling in xxM
10:45 – 11:00 Snack and Refreshment Break.
11:00 – 12:30 Random intercepts model, contextual-effects and centering
12:30 – 1:30 Lunch Break.
1:30 – 3:15 Random-slopes model
3:15 – 3:30 Snack and Refreshment Break
3:30 – 5:00 Individual Consultations.
Wednesday   September 19, 2018
9:00 – 10:45 N-Level Structural Equations Modeling: Data-structures
10:45 – 11:00 Snack and Refreshment Break
11:00 – 12:30 Multivariate cross-classified data: Emerging class of contextual SEM
12:30 – 1:30 Lunch Break
1:30 – 3:15 Partially nested data: Issues and models
3:15 – 3:30 Snack and Refreshment Break
3:30 – 5:00 Individual Consultations.

Instructor: Paras Mehta Ph.D.

Paras is an Associate Professor of Clinical Psychology and Industrial Organizational Psychology at the University of Houston. His research interests include multilevel structural equations modeling, growth curve modeling, and applications of ML-SEM in educational and organizational research. He is a creator of the xxM R package for N-level Structural Equation Modeling.


xxM ( will be used as the software for estimating models.

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.

Is it difficult to learn?

The new version of xxM (to be released soon) is a user- friendly software package that is very easy to learn and use.  Novel features of the package make it an ideal tool for teaching conventional SEM and MLM. All conventional SEM and MLM models can be specified with few lines of code from a single, unified perspective.  Specification of a fairly complex NL-SEM model with many levels is not any more difficult complex than a single level SEM or two level MLM.

  • xxM is ‘intuitive’. The new version of xxM offers a very simple scripting language that can be mastered quickly. No knowledge of ‘matrices’ is necessary.  Easier done, then said!
  • xxM is ‘graphical’. It allows complex models to be constructed visually. Path-diagrams are drawn automatically with every command. Can be used as a ‘concept-mapping’ tool to quickly explore alternate models.
  • xxM is ‘interactive’. It provides instant visual feedback virtually eliminating syntax errors.
  • xxM is ‘aware’. It understands what a model must look like and provides ‘semantic’ feedback. This feature makes xxM a useful tool for teaching SEM/MLM.


This course may be thought of an introductory-advanced course on Multilevel Structural Equation Modeling.  The course assumes basic knowledge of MLM and SEM.  A graduate course or a five-day workshop in SEM and MLM is probably necessary. That said, all essential concepts of SEM and MLM will be reviewed and re-introduced from a unified perspective during the first two days.

Novice participants with limited experience in MLM or SEM can expect to learn key ideas behind the modeling framework and learn how to fit fairly complex models using xxM. Expert participants will acquire a deeper understanding of how SEM and MLM may be readily and simply extended to

Participants are encouraged to contact the instructor before the workshop with their own data and research questions.

Participants from a variety of fields, including psychology, education, human development, sociology, marketing, business, biology, medicine, political science, and communication, will benefit from the course.

Computer and Software Support

Participants will receive an electronic copy of all course materials, including slides, datasets, fully annotated xxM scripts and output with step-by-step instructions for fitting each model.  Participants should bring a laptop computer and are encouraged to work-through each example on their own. Extra time is allocated for participants to seek individual assistance with the material or to work on their own analysis projects.

Seminar Files

Seminar files and downloads will be posted on the first day of class.

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration