1. Data Collection and Management: Where the Rubber Hits the Road Karen L. Franck, PhD Extension Specialist

2. UT SNAP-Ed Program 92 out of 95 counties funded 2008-2009 fiscal year – 71,405 volunteer educators – 7,234,830 indirect contacts – 802,398 direct contacts

3. System for University Planning Evaluation & Reporting (SUPER) Statewide database Multiple functions County Agent responsible for data entry

4. Education & Administrative Reporting System Consistent reporting across states – # of participants – # contacts – Impacts – Demographics

5. Dueling Databases Racial Groups for UT SUPER White Black or African American American Indian or Alaskan Native Asian or other Pacific Islander Hispanic Racial Groups for USDA EARS White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander All racial groups are classified as either non-Hispanic or Hispanic

6. Before Collecting Data Data Collection Plan  WHO, WHAT, WHERE, WHEN & HOW  WHY should have been answered long ago

7. Training for Data Collection Need for consistent and accurate data Address all procedures and special instructions Anticipate potential issues

9. Lessons Learned the Hard Way Nutrition education classes for adults implemented in 12 counties  1 county received older version of surveys  3 month follow-up right at Christmas SNAP-Ed programming in local school system  Program Assistant was not aware of need for data collection until last day of school

10. During Data Collection Ongoing training and support Quality checks  Address major & minor issues  Look at data during collection

11. More Hard Lessons Summer cooking camp for middle graders  Introduced post-collection: “Make sure your scores have improved!” No participant identification numbers on mailed follow-up surveys Over 75% of participants didn’t fill in last question Transposed numbers  532 out of 412 participants reported eating more fruit

12. After Data Collection Acknowledge issues and limitations resulting from data collection methods Learn from successes & mistakes!

13. Final Hard Lessons 5 years of data sitting in a file cabinet Outcome indicators changed but same survey continued to be used

14. Data Management Plan Maintain a raw data set Backup data set Guard confidentiality Develop a codebook for all variables (collected & created)

15. Quality Checks For all variables (collected & created) Run frequencies Crosstabs Check the range Have a plan for outliers Missing value analyses

16. Sources Fink, A. (2003). The Survey Handbook. London: Thousand Oaks. Schalock, R. L. (2001). Outcome-based Evaluation. New York: Kluwer Academic/Plenum Publishers. The University of Texas-Houston Health Science Center School of Public Health and The Texas Department of Health. Practical Evaluation of Public Health Programs Workbook. Available at http://www2.cdc.gov/phtn/Pract-Eval/workbook.asp. http://www2.cdc.gov/phtn/Pract-Eval/workbook.asp Wholey, J. S., Hatry, H. P., & Newcomer, K. E. Editors. (2004). Handbook of Practical Program Evaluation. San Francisco: Jossey-Bass. Townsend surveys and training tools: http://townsendlab.ucdavis.edu/http://townsendlab.ucdavis.edu/

17. THANK YOU Karen L. Franck, PhD Extension Specialist University of Tennessee Extension Tennessee Nutrition & Consumer Education Program 865-974-7457 kfranck@utk.edu