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IN PERSON – 5-day Statistics Short Course

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Seminar Overview:

This seminar teaches skills necessary to conduct analysis of complex multilevel data-structures using xxM from an n-Level Structural Equation Modeling (NL-SEM) perspective. The n-Level structural equation modeling framework is compatible for both conventional and non-standard data-structures. Currently aspects of such non-standard data can be handled within most MLM or ML-SEM packages, but do not scale well with increasingly complex data-structures.

Seminar Topics:

  • Coming Soon…

Seminar Description:

The innovative software package xxM provides a broader, simpler conceptual framework to match the consistent jargon-free language within the NL-SEM framework. All manner of models for nested data structures are easily specified and estimated using xxM, which is a free software program developed for the R platform.
This seminar is designed to introduce participants to the modeling mindset of the NL-SEM framework and gain ample experience with the xxM software package.

The perfect follow up for those who have taken our SEM Foundations & Extended Applications Course.

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.

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APA Continuing Education Credits:

This course offers ? hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able 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 proficient 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.
  • Think of study designs within your area of research that can answer more interesting and novel research questions.
  • Conduct multilevel modeling with xxM using random intercepts, contextual-effects, and centering.
  • Evaluate multilevel cross-classified data, an emerging class of contextual SEM.
  • Conduct multilevel measurement invariance across multiple groups.
  • Evaluate complex data structures, such as longitudinal, cross-classified, and multiple membership data.
  • Evaluate longitudinal data with switching classification.
  • Implement a social relations model with reciprocal ratings in round-robin designs and 360 evaluations.

 

Example Data Structures:

  • 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.

Software and Computer Support:

Coming Soon…

Seminar Audience:

This seminar may be thought of an introductory-advanced seminar on Multilevel Structural Equation Modeling. The seminar assumes basic knowledge of MLM and SEM. A graduate seminar 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 seminar.

Seminar Files

Instructor will provide password on first day of seminar.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

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

The last three days 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.

Summer Stats Camp 2023: Multilevel SEM with xxM
Day 1
9:00 – 9:30 Welcome and Introductions
10:45 – 11:00 Rest Break
11:00 -12:30 Building blocks: Single level SEM in xxM
12:30 – 1:30 Rest Break
1:30 – 3:15 Confirmatory Factor Analysis
3:15 – 3:30 Rest Break
3:30 – 5:00 Multiple-Group CFA
Day 2
9:00 – 10:45 Building blocks: Multilevel Modeling in xxM
10:45 – 11:00 Rest Break
11:00 -12:30 Random intercepts model, contextual-effects and centering
12:30 – 1:30 Rest Break
1:30 – 3:15 Random-slopes model
3:15 – 3:30 Rest Break
3:30 – 5:00 Individual Consultations
Day 3
9:00 – 10:45 N-Level Structural Equations Modeling: Data-structures
10:45 – 11:00 Rest Break
11:00 -12:30 Multivariate cross-classified data: Emerging class of contextual SEM
12:30 – 1:30 Rest Break
1:30 – 3:15 Multilevel measurement invariance: Multiple groups model
3:15 – 3:30 Rest Break
3:30 – 5:00 Individual Consultations
Day 4
9:00 – 10:45 Latent Growth Curves: Long and Wide format
10:45 – 11:00 Rest Break
11:00 -12:30 Complex data structures: Longitudinal, Cross-classified and Multiple membership data
12:30 – 1:30 Rest Break
1:30 – 3:15 Complex data structures: Longitudinal data with switching classification
3:15 – 3:30 Rest Break
3:30 – 5:00 Individual Consultations
Day 5
9:00 – 10:45 Partially nested data: Issues and models
10:45 – 11:00 Rest Break
11:00 -12:30 Social Relations Model: Reciprocal ratings in round-robin designs/360 evaluation
12:30 – 1:30 Rest Break
1:30 – 4:30 Individual Consultations

 

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