If you are an early- or mid-career, discipline-based educational researcher (DBER) interested in learning how to apply mixture models to your equity-focused research questions, we encourage you to apply to a unique training opportunity funded by the National Science Foundation. Mixture modeling is an advanced quantitative method that supports researchers in addressing questions around diversity, equity and inclusion. The training provides virtual training and year-long mentoring to help fellows integrate mixture modeling in their own research. A learning outcome of this training is for participants to use mixture modeling to explore their own data. Please check out our website or twitter (@mm4dbers) for more information. Dr. Karen Nylund-Gibson (mm4dbers@education.ucsb.edu) can be contacted with any questions. Applications are due March 1, 2024!
Overview
- Two cohorts of 10 participants will be selected (Cohort 1: 2023-2024; Cohort 2: 2024-2025)
- Applications for Cohort 2 will be due by March 1, 2024. Participants will be notified of their selection by April 1, 2024 and must commit to participating by the end of the month. Training for the first cohort will start in May 2024.
- Each participant will be provided with a $2,150 stipend to support their research (e.g., purchasing software or other needed research support)
- Training will include: 10 days of virtual training (synchronous and asynchronous) and will cover
- R/RStudio and Mplus Automation
- Fundamentals of data science for reproducible science
- Latent class analysis, Latent profile analysis
- Latent transition analysis, growth mixture modeling
- Covariates and distal outcomes in mixture modeling
- Writing and communicating mixture modeling results
- R/RStudio and Mplus Automation
- Mentoring will include year-long one-on-one monthly meetings with project staff to discuss each participant's analysis plans and opportunities to share findings with other cohort members.
- Ongoing methodological and data analysis support