Hi Roger
I've set up a few quizzes and questionairres in Opus.
In test construction, questions (or statements) are preferably researched on a representative population, producing correlations that then allow for some predictive validity. If the formula available to you has been derived in this manner, then it's likely all answers will, as discussed in other replies,
not be equal and will need to be weighted to account for this variance.
On the other hand, if the formula is not based on research but informally created, (for example, a score of 80% predicts a "high" likelihood of an event's future occurance), then your task may be alot easier. If this is the case, you can use a Likert scale which has 5 answers, ranging from always, frequently, sometimes, rarely, never or any of a number of variations on this theme. Always correlates with a full weighted score. I haven't used percents, rather I give always a 4, frequently a 3, sometimes a 2, rarely a 1 and never a 0. These can be reversed for questions where a full weight is given to a negative answer, so that always is given a 0, etc. So, as an example, for "I always get prompt attention for serious health problems," an always would be scored a 4 and a never would be scored a 0. If worded "negatively," for "I never get prompt attention for a serious health problem," if that's "always" the case, that gets a 0.
In Opus, an easy way to score the answers is to set a score (set variable action) based on 0-4, for each answer, storing it in a separate "answer" variable for each question (for example, "answer1," "answer2," etc.). You can require that each question is answered by not allowing the page to forward to the next page and question until a response is selected (can script something like "if answer1==null show. text box//words like please answer all questions// else, GoToPage.pagenumber").
Then on a total score page, a script like answer1+answer2, etc. If the score range is more intuitively created than normed using research and a representative population with resulting formula: then predictions are hypothesized based on each range of scores. Not very scientific but, absent an empirically verified formula based on rigorous research protocols, what you have.
If this is not too muddy and remotely useful, and you'd like to inquire further, feel free to send a private message. Happy to try to help further.
Kind Regards,
Stephen
