How We Work: A Collaborative Model
UConn's Statistical Consulting Services (SCS) operates primarily as a collaborative research partner, not simply a troubleshooting or analysis service. Our goal is to support high-quality, reproducible research through substantive engagement—often from study design through publication.
Depending on project needs, clients may work with the SCS through ongoing collaboration, a short-term consultation, or instructional offerings such as workshops and trainings.
Ways to Engage with the SCS
Collaborative Projects
Projects requiring substantial statistical involvement (typically more than ~15 hours of work) may be supported through a collaborative consulting model. These engagements often include study design input, advanced modeling, interpretation of results, and contribution to manuscripts or grant applications.
Learn more about collaborative consulting
For collaborative projects, discussions of authorship on resulting scholarly products are common and follow disciplinary norms.
Free Online Consultations (30 minutes)
The SCS offers free, one-time 30-minute online consultations intended for focused questions, early-stage project guidance, or brief methodological advice. These sessions are designed as a one-and-done resource and are not intended to support ongoing analysis.
Workshops & Trainings
The SCS regularly offers workshops on introductory and advanced statistical topics, including data management, modeling strategies, and best practices for reproducible research.
Departments or research groups interested in customized or topic-specific workshops are encouraged to contact the SCS to discuss their needs.
EDITME-custom-workshop-linkRequest a custom workshop or training
Fees & Billing
Initial intake meetings are free of charge.
All subsequent work is billed based on time spent on project setup, consultation, analysis, or interpretation.
Internal Rates
$75/hour — Graduate Students (setup or consulting time)
$95/hour — Postdoctoral Consultants (setup or consulting time)
$120/hour — Director (setup or consulting time)
External clients should contact Timothy E. Moore for fee information.
Project Scope & Authorship
Most SCS engagements begin as short-term consultations, typically requiring no more than approximately 15 student-hours of work. These engagements often focus on targeted methodological questions, exploratory analysis, or guidance on analytic strategy. Examples of a typical short-term workflow and associated effort are outlined here:
Projects anticipated to exceed this level of effort, or those requiring sustained involvement across study design, analysis, interpretation, and reporting, may be recommended for collaborative consulting. In these cases, SCS consultants function as research partners rather than ad hoc advisors.
When SCS consultants make a substantive intellectual contribution to a project—including contributions to study design, analytic strategy, statistical modeling, interpretation of results, or manuscript preparation—authorship on resulting presentations, grants, or publications should be considered in accordance with disciplinary norms.
Authorship expectations are typically discussed as project scope evolves.
For limited-scope consultations that inform scholarly products but do not involve substantive intellectual contribution, acknowledgment of support provided by Statistical Consulting Services is requested.
If you have questions about anticipated project scope, authorship considerations, or appropriate acknowledgment practices, please contact Timothy E. Moore.
Preparing Your Data
While consultants can assist with data formatting, clients are encouraged to prepare data prior to submission when possible.
Resources on tidy data practices, OpenRefine, and example data formats are provided below.
We strongly recommend that submitted datasets be accompanied by a codebook.
A codebook provides essential context about the variables in a dataset and helps ensure that analyses are interpretable and reproducible.
For each variable, the codebook should include:
• A concise, machine‑readable column name
Short names with minimal punctuation; use consistent capitalization; avoid spaces, special characters, and embedded units (e.g., age, sbp_baseline, not Age (years)).
• A descriptive variable label or explanation
A clear, human‑readable description of what the variable represents.
• Units of measurement
For continuous variables, specify units (e.g., years, kg, mmol/L).
• Possible values and coding schemes
For categorical or ordinal variables, list allowable values and what they represent (e.g., 0 = No, 1 = Yes).
• Missing or special values
Explicitly describe how missing data are coded (e.g., NA, -99, blank) and whether special values have meaning.
Codebooks may be provided as a separate document (e.g., Word, PDF, or spreadsheet) or as an additional worksheet within the data file.
Clear documentation at the time of submission reduces ambiguity and allows consulting time to be focused on analysis rather than data clarification.
Additional guidance on data organization and formatting, including examples of wide and long formats, is available in the resources below.
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Mailing Address
Nathan L. Whetten Graduate School Building
438 Whitney Rd Ext.
Storrs, CT 06269