Data Services Consultations

Statistical consulting (pilot) for graduate and undergraduate students.

In support of social distancing, all of our workshops and consultations will be offered remotely until such time as the library and the university revert to normal operations. Click on the button below to schedule time with one of consultants, or click here for instructions on accessing our virtual drop-in help desk.

Research data management and reproducibility best practices consulting for faculty, students, and staff.

  • Data management plans: If you have questions or would like a review of a data management plan before submitting it with a grant proposal, contact us. We've reviewed numerous plans, and can put you in touch with other university staff who can help address your questions.
  • Best practices, tools, resources: we can consult with you or your research team, and present in-depth training or short tutorials on tools and resources.
  • Email: or use schedule appointment button at top of page.


More Workshops for Departments and Groups


Get virtual statistical consulting help:


  • You will be prompted to either install/launch Teams or use the web app through your web browser.
    For most support cases, the web app should be suffice.

  • The on-call consultant will respond to submitted support messages. To meet with a specific consultant, click on the "Schedule Statistical Consulting Appointment" button at the top of this page.
  • Refer to this guide for how to get started with Microsoft Teams.
    • Teams can be accessed through a web browser or installed as an app (Windows, Mac, iOS, Android).
    • You will be able to send instant messages or request a voice or video call
      • Screen sharing is also available through the app (unavailable on mobile or web browser).


Meet the Consultants


Jay Matonte

Jay Matonte is a 2nd year graduate student in the Planning, Policy, and Public Management department obtaining concurrent masters degrees in Community and Regional Planning and Public Administration. Before coming to the University of Oregon, Jay attended Montana State University and obtained a M.S. in Applied Economics with a concentration in developmental economics. Currently, Jay studies economic development, Opportunity Zones, and real estate economics.


  • Stata
  • SAS
  • ArcGIS
  • QGIS
  • Excel

Expanding Software Interests

  • R
  • Python
  • MySQL


Yuan Fang
Yuan Fang is a third year international PhD student in archaeology. She speaks both Mandarin and English. Her master's thesis was on the prevalence of horse mortuary figures in the Han Dynasty tombs in China. Yuan's current research focuses on artistic designs on Chinese bronze vessels from the Xia, Shang, and Zhou dynasties using social network analysis. Yuan was a statistics minor during undergraduate period and has been using R to solve research questions in various disciplines including geography, sociology, and anthropology.

  •  R
  •  Git
  •  Linear Regression
  •  Cluster Analysis


Alicia DeLouize 

Alicia DeLouize is a 3rd year PhD Student in Biological Anthropology who has previously completed a Master’s in Psychological Science. Her main lines of research are global health and human evolutionary biology. Currently, she is focusing on the evolution of the biomolecular mechanisms behind aging and lifespan length. By taking a multidisciplinary approach, she uses human biology and health, psychology, anthropology, sociology, and evolutionary systems to understand health and disease at the microbiological, personal, and population levels. You can follow her work at 


  • R
  • SPSS 
  • AMOS 
  • Data Management (Excel/Access/Electronic Data Capture) 
  • Scale Development 
  • General Linear Model (t-test, ANOVA, regression, ect.) 
  • Generalized Linear Model (log, logit, probit, ect.) Nonparametric Statistics (Chi-squared, Kruskal-Wallis, Mann-Whitney U, ect.)
  • Structural Equation Modeling
  • Latent Variable Analysis 
  • Factor Analysis (PCA, PFA, EFA, ect.)
  • Multilevel Modeling 
  • Mixed-Effect Models 
  • Phylogenetic Analysis 
  • Longitudinal and Survival Analyses 
  • Complex Samples 
  • Power Analysis