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Mark D. Hayward, Director 305 E. 23rd Street, Stop G1800 78712-1699 • 512-471-5514

Statistical Consulting

The Population Research Center (PRC) has a statistical consulting program available to PRC affiliates.  Currently, two PRC Graduate Trainees, under the direction of the center's Graduate Training Director, Kelly Raley, have been hired to serve as consultants and are available for statistical, programming, and data management questions.  They will hold regular office hours, but we encourage you to send an e-mail to to schedule a consultation and give a brief overview of your question before showing up. The consultants are here to help you with questions related to your research.  We ask that questions related to classwork be directed toward your professor or TA.

David McClendon and Eve Pattison are our current consultants. Below is information on their office hours and a brief description of their skill sets. 

We are excited about this service and hope it provides added support to our research community. 

We can help with:

  • Data set up & management
  • Programming questions
  • Statistical analysis
  • Missing data issues
  • Other data & analysis issues

Office Hours:

Held in the PRC Computer Lab (CLA 2.706)

Eve: Mondays and Wednesdays, 12-1p
David: Tuesdays 3-4p and Thursdays, 12-1p

David McClendon is a doctoral student in the Department of Sociology. His specialties include event history analysis, categorical and longitudinal data analysis, and demographic techniques. He has worked extensively with a wide range of family and fertility surveys as well as vital statistics and census data. He has programming experience with Stata, Excel, and GIS.

Eve Pattison is a graduate student in the department of sociology. She primarily codes in STATA, but also has experience with SAS, R, and HLM. Eve has worked with several different data structures such as longitudinal, clustered, cross-sectional, and hierarchical data. Her specialties include categorical data analysis, hierarchical linear modeling, and longitudinal data analysis.

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