Quantitative Methods Core (Qcore)
The Department of Psychiatry Quantitative Methods core (Qcore) provides department faculty with statistical support. The core is comprised of faculty members with advanced training in statistics who also have topical research areas of their own. This experience allows them to work in clear and practical ways with colleagues needing statistical support.
Statistical Support Services:
- Answering questions about statistical methods, results, procedures, or problems
- Analysis of data for faculty projects, including clinically-oriented program evaluations
- Reviewing paper or grant sections for accuracy or clarity
- Power analyses and/or analytic sections for grant submissions
- Pre-study consultation on data collection design and processes
- Educational services, including meeting with faculty to explain statistical methods and procedure
Guidelines prior to submitting a project to Qcore
Administrative Data
The Qcore can help facilitate access to medical records data for analysis upon request. Note that the Qcore does not have direct access to this data, so the time frame for such projects depends on external factors, for example, the availability of ISD resources.
Data Cleaning Policy
Cleaning and warehousing data are not primary functions of the Qcore. The core will perform data management and cleaning required for analysis requests. However, the programming time involved will slow down analysis requests.
A Priori Hypotheses vs. Exploratory Analysis
Exploratory analysis is an important science component, even in well-developed areas. Therefore, the core encourages analyses that distinguish between exploratory or hypothesis-generating analyses and testing a priori-hypotheses.
Requesting Support
Faculty requests for statistical support can be submitted by clicking on the request button below.
The Qcore will respond to your request to discuss consultation.
Qcore Team
Ian Cero, PhD, MS
Ian Cero has PhD in Clinical Psychology and a master's degree in Probability and Statistics. His statistical background a range of methods, including social network analysis, agent-based models, multilevel models, missing data adjustments, and causal inference. His applied interests involve the development of social network enhancing interventions to prevent suicide among acute risk groups.

Hugh Crean, PhD
Hugh F. Crean has a PhD in Community Psychology with specialization in quantitative methods. His statistical interests include clinical trial methodologies, statistical mediation and moderation, missing data imputation, and latent mixture models. His research interests focus on the prevention of psychosocial problems in high-risk children and youth and contextual effects on adolescent risky behaviors.
Dan Maeng, PhD
Daniel Maeng, PhD is a health economist / health services researcher with methodological expertise in applied econometrics and causal inferences (e.g., instrumental variable, propensity score matching, difference-in-difference, healthcare cost data analysis, multilevel modeling, panel data analysis). He has extensive research experiences analyzing large healthcare data, such as health insurance claims data and electronic medical records, as well as survey data.
Patrick Walsh, PhD MPH
Patrick Walsh has a PhD in Health Service Research and Policy and a Master's in Public Health. He has extensive experience utilizing quality improvement, clinical research study, and electronic medical record data to analyze healthcare utilization and health outcomes, as well as perform program evaluations. He has expertise in survey research, including survey development, data analysis, and qualitative assessments.
