A pre- and post-workshop survey should be sent to all participants before and after the Data Club and/or Data Clinic sessions. The post-workshop surveys don’t need to be distributed after each and every session, but after each set of sessions.
-
The intention of the pre-workshop survey is to conduct a baseline and self-reported skills assessment, based on the topics you will be covering. We recommend using a Likert Scale-style skills assessment. For a more informative survey, you could include a short quiz and see how many questions participants are able to answer. You should also collect participant names (or assign them a unique identifier) so that you can link their pre-workshop survey results with their post-workshop survey results.
-
Accordingly template fields are:
-
Name/Unique Identifier: [free text]
-
Have you used [insert name of software you’ll be using] before? [yes/no]
-
On a scale of 1-5 how comfortable are you in completing the following tasks? (1 = not comfortable at all, 5 = very comfortable): [add up to 10 tasks, with a sliding scale to indicate level of comfort]
-
I give consent to my anonymised answers being used for research purposes. [yes/no]
-
The post-workshop survey should have the same skills assessment and/or quiz questions. In addition to this, we recommend adding some feedback questions - a combination of multiple-choice (quantitative) questions (e.g. did you feel that the sessions were a) too long, b) too short, or c) of appropriate length?) and long-answer (qualitative) questions, asking for open-ended feedback. You should also collect participant names (or assign them a unique identifier) so that you can link their pre-workshop survey results with their post-workshop survey results.
-
Accordingly template fields are:
-
Name/Unique Identifier: [free text]
-
On a scale of 1-5 how comfortable are you in completing the following tasks? (1 = not comfortable at all, 5 = very comfortable): [add up to 10 tasks - these should align with what you included in the pre-workshop survey, with a sliding scale to indicate level of comfort]
-
Do you think the amount of material covered was appropriate? [yes, no - too much material, no - too little material]
-
Do you think that the length of the course was appropriate? [yes, no - too long, no - too short]
-
Do you think the difficulty of the material covered was appropriate? [yes, no - too difficult, no - too easy]
-
Are you interested in learning more about [insert name of software you’ll be using]? [yes/no]
-
Are you likely to incorporate [insert name of software you’ll be using] in your routine work? [yes/no]
-
If you answered yes to the question above, please be specific about how you will incorporate it into your routine work. (e.g. will you perform analyses for your thesis with it? Will it help facilitate routine analyses for your job?) [free text]
-
Was the language that the sessions conducted appropriate/understandable? [yes/no]
-
If you answered no to the question above, please provide information on what language you would prefer. [free text]
-
Is there any other feedback you would like to give us so that we can improve the course? [free text]
-
I give consent to my anonymised answers being used for research purposes. [yes/no]
As well, something to consider is certification of workshop attendance and participation. TGHN offers certification, as might your institution. This could act as an incentive for participants to attend Data Clubs and/or Data Clinics.
Finally, evaluating longer term impact may involve sending out periodic surveys to see how well participants are integrating their newly learned skills into their routine work (if at all). However, survey exhaustion should be kept in mind! It is likely that the number and quality of responses will dwindle as more surveys are sent out. Instead, more passive measures of impact may be preferred, such as attendance at follow-up sessions or research outputs related to skills that participants have developed.
|
|
