VPC Training Series
At the VPC, we are committed to fostering a culture of learning and growth. Our VPC Training Series offers a variety of specialized trainings for students, staff, faculty, health practitioners, community members, and organizations—both governmental and non-governmental. Whether you’re looking to deepen your expertise or eager to share it, we have opportunities designed just for you.
UPCOMING TRAININGS
Stay informed about upcoming sessions! Each training is carefully crafted to address timely topics and to equip participants with practical, research-backed strategies for violence prevention. Check back here regularly to register for new sessions.
Modern Missing Data Analysis Techniques
When: Friday, April 11, 2025, 10m-2pm
Where: Virtual via Zoom
Details below.
Presenters:
Sam Cacace, Ph.D is an Assistant Professor in the Department of Epidemiology and Community Health and Core Faculty with the UNC Charlotte Violence Prevention Center. She specializes in military and veteran health and wellness from an ecological and psychosocial perspective, as well as advanced analytics and measurement, such as psychometrics, factor analysis, structural equation modeling, latent class and profile analysis, and other latent variable approaches.
Skyler Prowten, MA is doctoral student studying public health. Her mentors are Drs. Sam Cacace and Rob Cramer. Her current interests include suicide prevention, gender socialization, LGBTQ+ health, and health disparities among marginalized groups. She previously served as a Research and Data Analysis Consultant in the Office of Research at Appalachian State University.
Summary: Missing data is an unavoidable consequence of quantitative research. This training will focus on how missing data impacts data analytics and conclusions, and modern methods to mitigate the pitfalls of missing data. We begin this process by introducing missing data mechanisms, and how to prepare your dataset for missing data analysis. Next we’ll introduce two popular techniques to handle missing data: full information maximum likelihood and factored regression. Workshop participants see a demonstration with an example dataset in R Statistical Software, focused on how investigate and handle missing data using these approaches.
Learning Objectives: By the end of this session, learners will be able to:
- Identify missing data mechanisms
- Prepare a dataset for missing data analysis
- Apply full information maximum likelihood (FIML) to missing data
- Apply factored regression to missing data
- Gain familiarity with missing data using contemporary approaches in R
Who might benefit from attending?
This workshop is intended for graduate students, faculty, and staff conducting research across disciplines that use primary and secondary quantitative data sources.
PAST TRAININGS
Our past sessions have covered a variety of essential topics and have engaged participants from diverse fields. Highlights include:
