Statistics advice (intensive longitudinal data)

Closed Posted 3 years ago Paid on delivery
Closed Paid on delivery

Just need some advice about the best modeling approach for a particular model. I'm generally strong in this area, but I could just use someone to consult with and help point me in the right direction.

I have a three-level dataset in which people completed up to 7 surveys each day for 30 days. One of these surveys each day asks about the whole day, all others ask about right now. So, within-day surveys are nested within days, which are nested within people. People rate whether or not a specific event happened each day, and every survey before that asks whether they think that event will happen later. What I want to know is: **When** do people's predictions of whether or not the outcome will happen become most accurate?

So, the outcome is a day-level (Level 2) binary variable. Predictors are between 2-6 binary ratings of whether the outcome is likely to happen, collected at various points before it actually does. Is there an approach to modeling that might work best for this kind of question/data, especially given that the predictors are likely to be strongly correlated?

Statistics Statistical Analysis R Programming Language SPSS Statistics

Project ID: #24778395

About the project

12 proposals Remote project Active 3 years ago

12 freelancers are bidding on average $54 for this job

suyashdhoot

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