me and a colleague are recently working on our dissertation in the area of privacy research. In an 2x2x2 full factorial online experiment we set up an e-commerce-shop and manipulated the degree of convenience (high/low), personalization (high/low) and data sensitivty (high/low). As the dependet variable we measured privacy valuation (via a bidding mechanism) in €, specifically what amount of money participants would demand to disclose the requested data. The value was capped at 20€ since earlier focus groups indicated that this is a sensitive amount for disclosing some data types. Therefore the dependent variable varies from 0-20 and is truncated. Since the participants had to state their privacy valuation on their own, the data destribution looks also a little weird (fixation on values like 5,10,15 and a high conncentration at 20).
We want to use brms package to fit a model considering the three manipulation (degree of conveneince/personalization/data snesitivity) and the group assignment in the experiment. The manipulated variables handled like index variables. 2 was assigend to the high and 1 to the low condition. We already tried several models. The lastest was:
Modelnew <- brm(WTA | trunc(ub =20) ~ Conven_index * Personaliz_index * Sensit_index + (1|group), data = PriValuation, family = hurdle_lognormal(), control = list(adapt_delta = 0.99,max_treedepth = 13), iter = 3000, warmup = 1000, chains = 2, cores = 4)
The fit was rather poor. Since we new to stan/brms we were wondering how to improve our modeling an where hoping for some good clues here. If you need more info on the data please contact us.
Maik & Jan