I study decision making using a measure known as discounting. I am wondering if there's an easier way to write a study that can adapt to changing values without having to manually recreate the question tree every time.
In a typical discounting experiment, participants select between two options that change based on a previous answer, typically with hypothetical amounts of money.
For example, the initial question might ask if they'd rather have (A) $50 now or (B) $100 after 1 week.
When a participant selects A, the value of A would decrease by 50%: the next question would ask if they'd prefer (A) $25 now vs (B) $100 after 1 week. If they selected B the value of A would increase by 50%: (A) $75 now vs (B) $100 after 1 week.
The process would then repeat with $25 or $75, creating 2 branches for selecting the immediate A value or the later B value. (A) would change each time the question is asked for 6 or 7 trials.
The process would then repeat for several more iterations, and then the process would repeat with another delay (1 month, 1 year, etc).
If I create the study, each delay would include writing 128 separate questions, so the number of questions gets to be overwhelming when we start changing variables like the starting value.
What I'm looking for is a way to create the measure by changing the starting value, and letting qualtrics present the next 126 options based on the algorithm (increasing or decreasing the value offered in position A).
I have found that you can carry over values and loop the question, but I haven't found a way to apply the increase or decrease to a value automatically between trials. Any thoughts?