“It is crucial to invest in data. Without good data, we’re flying blind”. If you can’t see it, you can’t solve it” (Kofi Annan)
Think about the quote from Kofi Annan. How often do we as change agents promote a solution (innovation/new practice) to producers based on clear evidence that we are either fixing a problem or meeting an opportunity? If we’re being honest, the answer is not very often. We convince ourselves that we are basing our solutions on data, but the truth is there is a major imbalance within the RD&E system in Australia when it comes to defining problems and identifying opportunities for the primary production sector; we rely too much on opinion and do not invest anywhere enough in collecting objective evidence (practice data). As a result, the system is caught in a perpetual cycle of opinion-based solution push, rather than problem pull.
The lack of practice data not only impacts our ability to make sensible investment decisions; it means without it we have no way to measure adoption. While most stakeholders within the RD&E system acknowledge that adoption of new practices (change) is a primary determinant of success, no one (i.e. discipline, institution or organisation) is prepared to take responsibility for it. This means the RD&E system, whose primary aim is to effect change within the primary production sector, essentially ignores its key performance indicator. Measuring change starts with understanding what is current practice.
So, the skill that is critical, if not the most critical, to your success as an extension practitioner is the ability to design, collect, collate and interpret data that is focussed on practice and drivers of practice within primary production industries. This skill enables you to complete Stage 1 of the Adoption Planning methodology (see below). The focus of this module is quantifying producer practices, capacities and capabilities. Stages 2 and 3 is covered in the Analysis of the enablers and constraints to adoption resource.
To generate practice data, we need to survey producers. However, designing a survey to collect high quality data is NOT EASY. Everyone thinks it is, but it is not. Here are a few things to keep in mind when developing practice data surveys:
1). Consider data that has been already collected. While it is unlikely it will answer all the questions you have in mind, it may eliminate a few. It also means less questions in your survey, something participants always appreciate. The review process also helps refine your own thinking around a topic and home in on what questions you really need to ask. Finally, it demonstrates to the industry you’re looking to avoid bothering producers unnecessarily with yet another survey.
2). Once you’re sure you require a survey always remember Albert Einstein’s quote when approaching its design; “if I had 20 days to solve a problem, I would take 19 days to define it” i.e. never rush a survey. A high quality-survey will take at least 70 – 80 hours (collectively) to design. To spend any less time on a survey means it will be of poor quality. If you rush a survey the data you collect will not provide you with the answers you’re looking for and you will have wasted your time, the time of the participants and have contributed further to the low regard with which primary producers view surveys.
3). Remember a practice survey is focussed on understanding what practices are (or are not) occurring within an industry and, in some instances, what the drivers of these practices are. Therefore, you’re asking producers what they do and why they do it. Don’t ask them questions such as what they think other producers do or what research needs to be done, that’s just opinion. Similarly, self-serving surveys asking participants whether they are aware of your organisation, web-site, communication streams etc. or to rate the service you offer, likelihood of recommending etc. offers minimal value when it comes to effecting change.
Next resource:
