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Mix order of blocks within each condition

edited August 15 in OpenSesame

Dear swarm intelligence,


I am quite new to the community and to OpenSesame and Python as well. Although I have a general understanding and very basic knowledge of scripting and did not struggle too hard with the beginner's, intermediate and advanced tutorials, I do have a hard time finding a trivial solution for the following issue:


First, I want to assign subjects to three different subject groups/conditions, let's say conditions "1", "2" and "3". That worked quite well so far:


if var.subject_nr % 3 == 0:
  var.subject_group = '1'
elif var.subject_nr % 3 == 1:
  var.subject_group = '2'
elif var.subject_nr % 3 == 2:
  var.subject_group = '3'

Now I want to split these groups again and mix blocks so that half of the participants of each subgroup start with block 1 (then block 2) and the other half starts with block 2 (then block 1).


The "Attentional Blink"-Tutorial implements this sort of counterbalancing for odd and even subject_parity, so that some start with the experimental condition first, and others start with the control condition.

if var.subject_parity == 'even':
    var.condition1 = 'experimental'
    var.condition2 = 'control'
else:
    var.condition1 = 'control'
    var.condition2 = 'experimental'



I'm assuming (or hoping?) that technically there is a somewhat simple way to implement a similar balancing for three subject groups / conditions, but I just cannot seem to find it yet. I hope some of you can help me out here :-)

To illustrate what I tried to explain verbally:


Comments

  • Dear Mr. Fugbaum,

    As far as i can see, your answered it almost by yourself. Just adapt a little the second part of your script:

    if var.subject_parity == 'even':
        var.order= '1'
    if var.subject_parity == 'odd':
        var.order= '2'
    

    Just as a suggestion. I prefer to use names that have some meaning. Same for varnames as well as for values. Eg. var.PracticeGroup = 'MP' or 'PP'; var.order = 'IMA_first' or 'EXE_first'. Makes it easier for everyone to read your data. Even for oneself if you reanalyse it two years later because of reviewer commentaries ;)

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