Counterbalancing is a way to remove confounding factors from an experiment by having slightly different tasks for different groups of participants. This sounds abstract, so let's consider two examples.
Example 1: Counterbalancing response rule
Consider a lexical-decision experiment in which participants classify words as verbs by pressing 'z' with their left hand, or as nouns by pressing 'm' with their right hand. This design has a problem: If you find that participants respond faster to nouns than to verbs, this could be because nouns are processed faster than verbs, or because participants respond faster with their right than their left hand. You can fix this problem through counterbalancing the response rule.
For even participant numbers:
- verb → z
- noun → m
For uneven participant numbers:
- verb → m
- noun → z
Example 2: Rotating stimulus conditions
Consider a masked-priming experiment in which participants read aloud target words. On each trial, the target word is preceded by one of three types of priming words:
- An unrelated prime, e.g. priming with 'berry' for target 'house'.
- An ortoghraphically related prime, e.g. priming with 'mouse' for target 'house'
- A semantically related prime, e.g. priming with 'garden' for target 'house'
To avoid repetition effects, you only want to show target words only once per participant. Therefore, you create three different sets of target words, one for each prime type. This is a between-word design, which has less statistical power than a within-word design, in which each target word occurs in each condition. (For the same reason that between-subject designs are less powerful than within-subject designs.)
You can use counterbalancing to change this experiment into a within-word design by 'rotating' the condition in which each word occurs between participants. We have three conditions, and we therefore have three groups of participants:
- Participants 1, 4, 7, etc.
- Word A in condition 1
- Word B in condition 2
- Word C in condition 3
- Participants 2, 5, 8, etc.
- Word A in condition 2
- Word B in condition 3
- Word C in condition 1
- Participants 3, 6, 9, etc.
- Word A in condition 3
- Word B in condition 1
- Word C in condition 2
When you run an experiment in OpenSesame, you are asked for a subject number. This subject number is available as the experimental variable
subject_nr. In addition, the experimental variable
subject_parity has the value 'odd' or 'even', depending on whether the subject number is odd or even. Now say that you want to counterbalance the response rule as in Example 1, you could add the following inline_script to the start of the experiment.
if var.subject_parity == 'odd': var.verb_response = 'z' var.noun_response = 'm' else: var.verb_response = 'm' var.noun_response = 'z'
Now, in your block_loop, instead of setting
correct_response to a fixed value, you set it to a variable:
[noun_response]. You can take a look at the lexical-decision task example to see how this works (Menu -> Tools -> Example experiments).
Counterbalancing Video tutorial
The video below illustrates both a simple counterbalancing example, for two block orders or conditions, and a more complex example, for three or more block orders or conditions.