Multilevel Modeling Seminar

Session 1: June 1 – 5, 2020
Albuquerque, NM – Embassy Suites


FAQVenue Info – Hotel Booking
$1,895 Faculty/Professional or $1,145 Student/Post-Doc

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Details Per Course Select Option
Professionalshow details + $1,895 (USD)  
Studentshow details + $1,145 (USD)  

Seminar fee includes all materials, downloads, software access, training, refreshments and access to a recorded video of seminar:

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

Statistics Seminar Description

This course is designed to provide theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include, for example, students within classrooms, professionals within corporations, patients within hospitals, or repeated observations from the same person. In each of these cases and many more, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling.

This course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Cross-sectional and longitudinal models, the most common multilevel modeling applications, are featured in the seminar. Using real datasets provided in the seminar, participants will learn how to use the R software program to analyze data and interpret results. Further, the course will emphasize proper interpretation of analysis results and illustrate procedures that can be used to specify multilevel models. Coverage of multilevel models for binary outcomes and cross-classified random effects modeling will also be included.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

Seminar Syllabus

Monday June 1, 2020
9:00-9:30 Welcome and introductions
9:30-10:45 Introduction to multilevel modeling and basics of R
10:45-11:00 Snack and refreshment break
11:00-12:30 Review of multiple linear regression
12:30-1:30 Lunch break
1:30-3:00 Methods of handling nested data
3:00-3:15 Snack and refreshment break
3:15-5:00 Adding predictors and random effects
Tuesday June 2, 2020
9:00-10:45 Centering
10:45-11:00 Snack and refreshment break
11:00-12:30 Interactions and contextual effects
12:30-1:30 Lunch break
1:30-3:00 Estimation
3:00-3:15 Snack and refreshment break
3:15-5:00 Multiparameter tests and model selection
Wednesday June 3, 2020
9:00-10:45 Longitudinal models
10:45-11:00 Snack and refreshment break
11:00-12:30 Longitudinal models (continued)
12:30-1:30 Lunch break
1:30-3:00 Alternative error structures
3:00-3:15 Snack and refreshment break
3:15-5:00 Multiple group models
Thursday June 4, 2020
9:00-10:45 Power and sample size
10:45-11:00 Snack and refreshment break
11:00-12:30 Multivariate Models
12:30-1:30 Lunch break
1:30-3:00 Three-level modeling
3:00-3:15 Snack and refreshment break
3:15-5:00 One-on-one consultations with instructor
Friday June 5, 2020
9:00-10:45 Cross-classified random effects modeling
10:45-11:00 Snack and refreshment break
11:00-12:30 Cross-classified random effects modeling (continued)
12:30-1:30 Lunch break
1:30-5:00 One-on-one consultations with instructor

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Seminar Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

Statistical Methods Seminar Description

Overview: An intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data.

Topics:

  • Review of regression and methods of handling nested data
  • Random intercept and random slope models
  • Testing and interpreting interactions in multilevel models
  • Longitudinal multilevel modeling
  • Three-level modeling
  • Cross-classified random effects modeling

Note: This course will focus primarily on with a single outcome variable.  As such, this course will not provide an in-depth coverage of multilevel SEM (MSEM).  See the Summer Stats Camp course, Multilevel SEM with xxM, for training in this topic area.

Instructor: Alex Schoemann, Ph.D.

Dr. Alexander M. Schoemann, is an Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 (after spending several years as a “counselor”). Alex teaches graduate courses in research design, regression, multivariate statistics, structural equation modeling and multilevel modeling. His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation, power analyses, missing data estimation, meta-analysis, structural equation models and multilevel models. Alex is also interested in developing user-friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/).

Seminar Learning Goals

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Acquire an understanding of multilevel modeling techniques as applied in the educational, social, health, and behavioral sciences
  • Manage and clean multilevel data for analysis
  • Specify, estimate, evaluate, and compare different multilevel models using R software
  • Interpret and present the results of a multilevel modeling analysis
  • Critically evaluate applications of multilevel modeling in scientific studies
  • Become acquainted with multilevel modeling for binary outcomes and for non-purely hierarchical data

Participants will also complete the course with a foundation for future learning about multilevel modeling and knowledge about available resources to guide such endeavors.

Seminar Prerequisites

Required:

  • Advanced proficiency in multiple linear regression, including use of categorical independent variables
  • Intermediate fluency with statistical software (e.g. SAS, SPSS, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multivariate data analysis.
  • At least limited experience in binary logistic regression
  • At least limited experience using R

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Multilevel Modeling.
  • Advanced mathematical or statistical topics such as matrix algebra or likelihood theory.

Seminar Software and Computer Support

Participants need to bring a laptop computer with Wi-Fi capabilities.

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.

Note, however, that R and RStudio are the software programs that will be demonstrated. Both programs are free and can be downloaded from https://cloud.r-project.org/ and https://www.rstudio.com/products/rstudio/download/, respectively. Additional directions will be shared with enrolled participants.

Note: Limited examples will also be provided in SPSS and SAS but the majority of the course will be taught using R.

Seminar Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.

After May 1st: Click Here to Access The Multilevel Modeling Seminar Files