MM4DBER
MM4DBER Training Materials
Mixture Modeling for Discipline-Based Education Researchers (MM4DBER) is an NSF-funded training grant to support STEM Education scholars in integrating mixture modeling into their research.
“Parking Lot” document with questions and answers HERE
Day 1 Training (September 14, 2023): Introduction to the Latent Class Analysis (LCA) Model
Learning Outcomes:
- Recognize the LCA statistical model
- Interpret LCA model parameters
- Evaluate conditional item probability plots
- Have a basic understanding of the Mplus code and output for an LCA model
Synchronous Activity:
- Slide deck here
Asynchronous Activity:
- Activity 1: Run your first LCA model and do the scavenger hunt looking for important parts of the Mplus output. All materials are found here
Answer key: Questions & Answers , Annotated Mplus Output
- Activity 2 (optional): This introductory paper on LCA may be a helpful read here
Day 2 Training (September 18, 2023):
Learning Outcomes:
- Understand the principles of item selection in LCA
- Know the principles of evaluating mixture models
- Understand the statistical tools available to evaluate models
Synchronous Activity:
- Slide deck here
Asynchronous Activity:
Activity 1:
- Watch this code-along video and follow along in Rstudio: Video
- Tutorial handout: Link here
- Download the Github repository here: Intro_to_LCA
Day 3 Training (September 21, 2023):
Learning Outcomes:
- Understand the principles of evaluating mixture models
- Been exposed to the complex enumeration process
- Understand the multi-step process for auxiliary variables
Synchronous Activity:
- Slide deck here
Asynchronous Activity:
- Review slides 36 - 72 on auxiliary variables that we did not get to in today’s training (recording).
- Finish the code-along activity from day-2: We recommend reviewing sections of the video that were covered today (I.e., the enumeration table, IC plot, classification diagnostics table, and response pattern table).
- Review the 10 FAQs in LCA article if you haven’t done so already.
Day 4 Training (September 25, 2023):
Learning Outcomes:
- Understand the utility of auxiliary variables in mixture modeling
- Learn about the multi-step approach to auxiliary variables
- Understand how to incorporate covariate and distal outcomes into mixture models
Synchronous Activity:
- Slide deck Here
- Mplus output file example: c4_covariate_only.out
Asynchronous Activity:
Activity 1: ML 3-Step Method
- Watch this code-along video and follow along in Rstudio: Video
- Tutorial handout: Link here
- Download the Github repository here: 3-Step
Activity 2 (optional): Interpretation of auxiliary variables
- Watch this code-along video and follow along in Rstudio: Video
- Tutorial handout: Link here
- Download the Github repository here: Interpretation of auxiliary variables
Day 5 Training (September 28, 2023):
Learning Outcomes:
- Understand what LPA is and how it differs from LCA
- Have exposure to extensions of the LCA and LPA models
- Know more about the year long MM4DBER training program
Synchronous Activity:
Helpful Links:
-
Please visit our website to learn more.
-
Visit our GitHub account to access all the MM4DBER materials.
-
Follow us on Twitter!
