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IN PERSON – 5-day Statistics Short Course

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Seminar Overview:

An intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data. Everything is nested, so you need something more than multiple regression or analysis of variance to get the job done! Nested data structures can include students within classrooms, professionals within corporations, patients within hospitals, or repeated observations from the same person. Multilevel modeling (MLM) is built to handle this kind of data. You will use real datasets and the R software environment to learn how to analyze multilevel data sets and interpret results of multilevel models.

Seminar Topics:

  • Review of regression and methods of handling nested data
  • Random-intercept and random-slope models
  • Testing and interpreting interactions in multilevel models
  • Cross-sectional and Longitudinal multilevel models
  • Multilevel models for binary outcomes
  • Cross-classified random effects modeling

Note: MLM is sometimes referred to as mixed-effects modeling, hierarchical linear modeling, or random coefficients modeling. This course will focus primarily on with a single outcome variable.  As such, this course (https://www.statscamp.org/courin combination with a course in SEM Foundations) would provide an ideal introduction to the foundations necessary to prepare for the advanced Summer Stats Camp course, Multilevel SEM with xxM.

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.

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/).

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APA Continuing Education Credits:

This course offers 29 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:

  • Identify the basic functions of R that are relevant to multilevel modeling.
  • Understand the principles behind multiple linear regression.
  • Describe the methods of handling nested data.
  • Modify regression models by adding predictors and random effects.
  • Apply methods of centering.
  • Evaluate models with interactions.
  • Conduct multiparameter tests.
  • Make informed decisions about model selection.
  • Evaluate longitudinal models.
  • Implement alternative error structures.
  • Evaluate multiple group models.
  • Understand the roles sample size and power play in a multilevel framework.
  • Evaluate multivariate models.
  • Evaluate three-level models.
  • Evaluate cross-classified random effects models.
  • Evaluate models with categorical outcome variables.

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.

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 Audience:

Coming Soon…

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.

Instructor will provide password on first day of seminar:
Click Here to Access The Multilevel Modeling Seminar Files

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.

Monday June 5, 2023
9:00-9:30 Welcome and introductions
9:30-10:45 Introduction to multilevel modeling and basics of R
10:45-11:00 Rest Break
11:00-12:30 Review of multiple linear regression
12:30-1:30 Rest Break
1:30-3:00 Methods of handling nested data
3:00-3:15 Rest Break
3:15-5:00 Adding predictors and random effects
Tuesday June 6, 2023
9:00-10:45 Centering
10:45-11:00 Rest Break
11:00-12:30 Interactions and contextual effects
12:30-1:30 Rest Break
1:30-3:00 Estimation
3:00-3:15 Rest Break
3:15-5:00 Multiparameter tests and model selection
Wednesday June7, 2023
9:00-10:45 Longitudinal models
10:45-11:00 Rest Break
11:00-12:30 Longitudinal models (continued)
12:30-1:30 Rest Break
1:30-3:00 Alternative error structures
3:00-3:15 Rest Break
3:15-5:00 Multiple group models
Thursday June 8, 2023
9:00-10:45 Power and sample size
10:45-11:00 Rest Break
11:00-12:30 Multivariate Models
12:30-1:30 Rest Break
1:30-3:00 Three-level modeling
3:00-3:15 Rest Break
3:15-5:00 One-on-one consultations with instructor
Friday June 9, 2023
9:00-10:45 Cross-classified random effects modeling
10:45-11:00 Rest Break
11:00-12:30 Cross-classified random effects modeling (continued)
12:30-1:30 Rest Break
1:30-5:00 One-on-one consultations with instructor

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