The Journal of Extension - www.joe.org

February 2013 // Volume 51 // Number 1 // Ideas at Work // 1IAW2

The Data Party: Involving Stakeholders in Meaningful Data Analysis

Abstract
A hallmark of Extension includes the involvement of stakeholders in research and program needs assessment, design, implementation, evaluation, and reporting. A data party can be used to enhance this stakeholder involvement specifically in data analysis. This type of event can not only increase client participation in Extension programming and research but also enhance community buy-in, reinforce human and community development, and provide more authentic data interpretation.


Nancy K. Franz
Associate Dean for Extension and Outreach for Families and 4-H Youth
Director, Iowa State Extension to Families
Iowa State University
Ames, Iowa
nfranz@iastate.edu

Those of us who collect data for needs assessment, program evaluation, and research sometimes struggle with ways to ensure authentic and meaningful community-based interpretation of that data. Participatory evaluation and participatory action research are gaining more attention as methods of community engagement for program evaluation and research by higher education (Franz, 2011). These participatory processes have been found to enhance community buy-in (Fetterman, 2010), reinforce human and community development (Patton, 2011), and authentic data interpretation (Gillespie & Gillespie, 2006).

The Data Party as Community Engagement in Data Analysis

Extension faculty and staff are good at collecting data for a variety of uses. However, data analysis is often slow or can stall out. Some Extension workers also struggle with whether or not their own interpretation of the data authentically reflects the phenomena being measured. In response to these and other data analysis dilemmas, I've tried a variety of ways to create more participatory environments with other Extension workers and clients to improve data analysis.

One tool I've used to encourage participation in data analysis by academics, practitioners, and community members is the data party. This event is promoted as an enjoyable activity where those present make meaning out of data collected around an issue, project, or phenomena. Here are the steps I've used to implement a data party:

  • Discuss the data party concept with potential participants to determine if it matches their interests.
  • With data analysis partners, plan a theme and logistics of the party (e.g., how long should the party be, will food be an important part of the event, who are the best people to invite, where should it be held, should there be any remuneration for participants, should there be introductions to each other and the project before starting the data analysis).
  • Determine who should best lead the data analysis (e.g., a trained facilitator due to delicate or volatile data or perspectives about the data, co-facilitators to represent the groups of people invited).
  • Determine what process or processes should be used to analyze the data.
  • Summarize each section of data analysis and provide next steps at the end of the event.
  • Ask participants for feedback on their party experience to gauge the value of the event and its impact on the data analysis and community engagement goals.

An Example Data Party

One recent participatory research project explored how farmers prefer to learn and what that means for Extension education (Franz, Piercy, Donaldson, Richard, & Westbrook, 2010a, 2010b). This project included 10 farmers and Extension educators in a steering committee that helped guide the research. Specifically for data analysis, steering committee members helped decide that a data analysis party was appropriate, that the whole committee should be invited but participation was voluntary, that the party should take place at a centrally located Extension office, and that the Extension staff and researchers would set up the other logistics related to the event. The steering committee was comfortable with the lead researcher facilitating the event. A stipend and mileage was set up for steering committee members who participated in the event. Data summaries were provided by the researchers to all committee members prior to the party.

The data party lasted 5 hours, including a lunch break held in the meeting room. Two researchers, two farmers, and two Extension staff participated in the data analysis. Each research question was presented, and participants provided their analysis of the data for each question. Participants also answered general questions about the data, such as:

  • What surprised you about the data?
  • What was confirmed by the data that you already knew?
  • What was missing in the data that you thought you would see?
  • What other meanings do you see in the data that we haven't already discussed?
  • What other comments do you have about the data?

The researchers recorded discussions with flip charts, notes taken by individuals, and audio recording. Probing of responses was common to reveal deeper meanings interpreted by the participants. After summarizing the day and discussing next steps, the researchers conducted a brief round-robin activity to determine the immediate impact of the data party. It was the first time any of the participants had been involved in a data party. They felt this method of data analysis helped them articulate what the data meant for those similar to themselves. They also felt it deepened their understanding of the research project results. Finally, they became very committed to sharing the project results with others.

Data parties have also been useful for analyzing program needs assessment data, program evaluation data, and other bodies of data. The event helps make data analysis more pleasurable and brings a variety of perspectives to the task.

Lessons Learned

Over the last 10 years of conducting data parties for data analysis, important lessons have surfaced:

  • Tie the data party to food and traditions (e.g., holidays, tailgate).
  • Provide summary data to participants to review before the event rather than raw data because many people have low or selected tolerance for large amounts of data.
  • Supply analysis tools for participants to use prior to the event to help make sense of the data (e.g., matrices, tables, figures) (Miles & Humberman, 1994).
  • Enhance participation of community members by providing food and other incentives.
  • Hold the event so partners can easily participate—location, parking, accessibility, time of day.
  • Be open to new meanings or different meanings in data provided by participants based on their own experience and perspectives.
  • Constantly watch to see that all participants are involved in the data analysis.
  • Be prepared to help participants discuss multiple meanings of data and potential conflicts related to those meanings.
  • Strike a balance between the rigor needed for quality data analysis and building relationships amongst the participants and the data.

Above all, provide the data party as a voluntary event so people freely participate. This helps insure more accurate data analysis.

Summary

A hallmark of Extension includes the involvement of stakeholders in research and program needs assessment, design, implementation, evaluation, and reporting. A data party can be used to enhance this stakeholder involvement specifically in data analysis. This type of event can not only increase client participation in Extension programming and research but also enhance community buy-in, reinforce human and community development, and provide more authentic data interpretation.

References

Fetterman, D. (2010). Foundations of empowerment evaluation. Thousand Oaks, CA: Sage.

Franz, N. (2011, October). Methods and practices of community-based research and creative activities. Panel presentation at the National Outreach Scholarship Conference. East Lansing Michigan.

Franz, N., Piercy F., Donaldson, J., Richard, R., & Westbrook, J. (2010a). Farmer, agent, and specialist perspectives on preferences for learning among today's farmers. Journal of Extension [On-line], 48(3) Article 3RIB1. Available at: http://www.joe.org/joe/2010june/rb1.php

Franz, N., Piercy F., Donaldson, J., Richard, R., & Westbrook, J. (2010b). How farmers learn: Implications for agricultural educators. Journal of Rural Social Sciences, 25(1).

Gillespie A., & Gillespie, G. (2006). Generating grounded theory with community partners. Journal of Community Nutrition. 8(1), 16-23.

Miles, M., & Huberman, A. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage.

Patton, M. (2011). Developmental evaluation: Applying complexity concepts to enhance innovation and use. New York, NY: The Guildford Press.