About the Graduate Assistantship in Quantitative Methods
The objective of this assistantship is to support graduate training in quantitative methods in the Department of Psychology. You must be a doctoral student in the UNL Department of Psychology to be eligable.
Assistantship activities are broad in scope and include (but are not limited to):
- Holding office hours to provide supervised consultation in quantitative methods (e.g., answering questions about coursework, troubleshooting ongoing data analysis, helping to develop data analysis plans)
- Managing and distributing resources to support quantitative training in the department (e.g., managing a quant resource page, maintaining the quantitative training website, managing the quant events calendar and listserv)
- Promoting department initiatives in quantitative training (e.g., Quant club, invited speakers and trainings)
- Supporting advanced quantitative coursework (e.g., providing consultation to students) with opportunities to provide guest lectures and lead workshops and trainings
Preference will be given to students who have completed several quantitative courses in the Department of Psychology and have experience applying a range of quantitative methods in their own research.
What to expect: This is a 9-month academic year assistantship for which you will receive the standard department stipend. The anticipated weekly time commitment for this assistantship is 19.6 hrs. The position will be supervised by Dr. Becca Brock, in consultation with other core quantitative faculty in the department.
Benefits to you: It is our intention to provide an intensive training opportunity that will serve to develop and solidify key quantitative skills through consultation and teaching. Pending approval by your supervisory committee, this assistantship could meet the exam requirement for the Quantitative Concentration (“Quant Minor”) if supplemented with a reflection statement. You will be provided with a laptop with statistical software and copies of recommended books on quantitative methods to use during the assistantship.
Students who are interested in more information about this position should contact Dr. Becca Brock, quantitative training coordinator, or reach out to the current quantitative assistant. Applications are due in March for the following academic year. Look for an annoucement over the department listserv when we are accepting applications. Click here to apply.
Current Quant Assistant: Amanda Baildon, M.A. (she/her)
Amanda is a fifth-year graduate student in the Social and Cognitive Program. She received her M.A. in psychology from the University of Nebraska-Lincoln in 2022 and is working towards her Ph.D. She works in the Objectification of Women Lab and is advised by Dr. Sarah Gervais. Link to Amanda’s current publications.
In my work, I use social psychology, public health, and women, sexuality, and gender studies to examine the contextual and sociocultural causes and consequences of substance use, as well as situational factors that can be leveraged to reduce alcohol use and its negative consequences. Specifically, my work examines two interrelated questions: when do bystanders intervene to reduce alcohol consumption and associated sexual violence; and when, why, and with what consequences do people, especially women and LGBTQ+ people, consume alcohol?
My quantitative expertise includes the following techniques: T-tests, ANOVAs, correlation and regression, mediation, moderation, path analysis, structural equation modeling, and observational methods (e.g., coding and reliability estimates). I also have familiarity with multilevel modeling for nested data and longitudinal structural equation modeling. My expertise primarily comes from application to cross-sectional and experimental research, but I have also received extensive course training in application to non-experimental data. I am most proficient in SPSS and Mplus, but I have also used G*Power and R.
I can also aid in course planning and have taken the below classes:
- Multilevel Modeling (PSYC 944)
- Structural Equation Modeling (PSYC 948)
- Longitudinal Structural Equation Modeling (PSYC 949)