The Center for Human Growth and Development (CHGD) Statistical Analysis Core provides study design and data analysis services for new and ongoing research projects implemented by CHGD members.
The CHGD Statistical Analysis Core offers members:
- Free initial consultation regarding project needs
- Study design services
- Sample size and power calculations
- Statistical analysis
- Presentation of findings/results for abstracts, manuscripts, posters, and/or presentations
- Group education and training
- Access to a statistics expert for grant applications
What kind of data can be analyzed by the Statistical Analysis Core?
The CHGD Statistical Analysis Core can analyze data related to child and adolescent health, behavior and development. The Core can provide some typical data manipulation (such as creating new variables, classifying cases based on present conditions, merging a limited number of files) but is not able to provide substantial data management (manipulating large number of raw data files) or statistical programming lessons.
Timeframe Guidelines for Services
New projects and proposals
(Initial meeting, analysis and results discussion)
- Usually requires 3-6 weeks for completion
- Additional time may be required during high-volume times of the year, such as grant submission deadlines
Data analysis for ongoing studies
- Original analyses can usually be completed in 2-4 weeks
- Any follow-up analyses require an additional 2-4 weeks
- Additional time may be required during high-volume times of the year, such as conference abstract deadline periods
How to get started
To schedule your free consultation with the CHGD Statistical Analysis Core, please fill out the intake form here.
Best Practices to Make the Most of Your Free CHGD Statistical Analysis Core Services Consultation
- Define your study aims/hypothesis.
- Meet early to discuss the study design and feasibility of testing study aims within a given time period. It is best to do this before data collection or and entry begins.
- Gather information required for power analysis
- Both the budget and feasibility of the study will depend on the sample size
- Confirm or reframe aims if power is limited
- Provide a data dictionary (a document with variable names, variable labels, codes for what numeric values stand for)
- Ex: Clearly mark missing data with “999” or something similar
- Ex: Cleary define for statistician which values are out of range, etc.
- Ex: Avoid highlighting cases in Excel, as this designation is lost when analyst reads data into data analysis program
- Avoid merging data on your own