Balancing design for a multi-part study

Hi! I am going to conduct a study with two parts (with one week in between). However, this is a 2x2 design that needs balancing between parts. Like participants first will be randomly assigned to condition A1B1, A1B2, A2B1, or A2B2 for the first part. Then in the second part, they will see the counter condition (i.e., A1B1 will go to A2B2, A2B1 will go to A1B2, etc.). I am not sure if it is possible to make it in Prolific.
I learned from the help page that I probably need to initially set 4 studies regarding the 4 conditions in part one, and then create another four to match the participants up with the first part using the participant prescreening function. This is pretty complicated indeed. Also, I am wondering how can I make sure participants only signing in one of the four conditions? On the help page, it is said the prescreening on previous studies only functions when the “previous studies” are “completed”. Does it mean that I cannot collect the 4 conditions at the same time? Is there any other better way to make these all work?

Hello Lei Fan and welcome to the Forum! :wave:

So, for your case I would proceed as following. In your Study 1, I would randomize particpants’ allocation across conditions within a single study (you can manage this through your survey software, such as oTree or Qualtrics). Instead, I would split your Study 2 in 4 micro-studies, as you were suggesting. In this part you don’t need randomization, since you want the specific people who took part in A1B1 to go now in your A2B2, etc. Right? Therefore, you can for each micro-study add the specific IDs of the participants that you need in the “Custom Allowlist” that you can find in the AUDIENCE section. In the same section, you can as well select a “Custom Blocklist” with the IDs of the people allocated to the other 3 conditions. In this way I think that you should be able to run the 4 studies simultaneously.

Let me know if you think that this could work for you.
Just in case, here is a useful link on how to reduce attrition rates.