Statistical Methods Seminar Description
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.
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 SEM Foundations!
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.
Software and Computer Support
Participants will receive an electronic copy of all seminar 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.
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.
View Seminar Materials Page (Password Protected)
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.