Now let me be honest. I don't pull the smaller companies to task but focus on the bigger companies with deeper pockets and analytics departments. It is frustrating to see low quality multiple choice questions written in the absence of any concern toward research methodology or rigorous question design.
I am not particularly clever about how to market these opportunities but several forward thinking clients decided what they needed. We scheduled a few live customized webinars to support the in-person training and now we are off to the races.
Let me share a few of the insights that seemed the most helpful and easily integrated into processes that either already exist--or should.
I have nothing against Survey Monkey. If you are a skilled analytics professional or a subject matter expert with learning platform expertise and can distinguish satisficing question responses and how to avoid them--go get that banana.
We won't delve into the statistical layer that makes it all possible--not yet--but conjoint analysis is the right tool to answer complex decisions like those made at the point of care. Health care providers and patients consider multiple attributes (characteristics) and features of an intervention or recommendation in complicated ways that it is impossible to measure or address if you are only asking in a Likert or multiple choice format.
I can recognize when teams aren't up to the challenge. In these cases, just use ranking questions. At least you can safely talk about probabilities if you know how options are prioritized.
We are trying to uncover, "Are health care providers/patients/payers willing to sacrifice______ in order to have ______?
When presented with choices--respondents select the higher level of utility.
- Discrete choice experiments allow estimation of the relative importance of different aspects of care, the trade offs between these aspects, and the total satisfaction or benefit respondents derive from health care services. Monetary values of attributes may be indirectly estimated by including time-based attributes such as "waiting time"...Ryan et al. Use of discrete choice experiments to elicit preferences
There isn't likely to be anything ready to go out of the gate but if you step through some of the methods carefully you can build your foundation and then perhaps reach out for the analytic platform.