My name is Dan Carvalheiro. As a Data Analyst for UConn Extension, part of my role is supporting UConn Extension professionals in their use of data to guide programming decisions and develop scholarly Extension publications. I am a doctoral candidate in the UConn Human Development and Family Sciences department, through which I have received methodological training and research experience. My academic research has been on children and families in their development and behaviors, but my expertise can lend itself to data on agriculture, animals, landscapes, climate, and more relevant to Extension and CAHNR topics. My academic specialization has been in Quantitative Research methods, with additional experience in Qualitative and Mixed-Methods research.
SERVICES
Data Consultation
You may find yourself in one of the following situations:
- Are you planning on submitting a grant or proposal which will include data collection?
- Do you have an idea for a study but aren’t sure what kind of analyses make sense?
- Have some data but aren’t sure what to do with it?
- Need an opinion on reviewer feedback about your analyses?
I can meet with you to discuss possible analytical methods that can fit your data needs.
Technical Support for Data Analysis
I have worked with various statistical software. Sometimes you may run into a technical issue with this software when preparing, analyzing, or visualizing your data. Sometimes the technical issue can be a result of the data, your chosen methodology, or your analytical steps/code. I can meet with you to go over your technical issue and see if I have any recommendations.
Recommendations for Learning and Resources
I have gained my quantitative expertise through graduate classes and a lot of self-studying. If you want to gain a better understanding of a certain methodology, I can point you in the direction of potential learning resources (books, articles, classes, blogs, topics to explore) or help clarify something about which you may be confused.
SELECT EXPERTISE & COMPETENCIES
Data Manipulation: combining variables, shifting from wide to long format and vice-versa, merging datasets, renaming columns, replacing variable values, data transformations
Statistical Models: linear regression, nonlinear regression, multilevel models, path models, latent variable models, social network models, Bayesian models, neural networks, growth curve models
Psychometrics: factor analysis, item response theory, mixture models, reliability, item analysis
Misc: Python, R, SPSS, mixed-methods research, visualization, causal inference
