Randomization & Balancing

Randomization in experiments and psychology research is an important mechanism to reduce bias and assure strong experimental design integrity. Randomization in experimental design and research is essentially the process of randomly assigning participants across different groups (treatment / intervention / experimental vs. control). By making this assignment process random, it assumes that all participants are equally likely to be assigned to any condition.

In addition to participant assignment, randomization in experiments also refers to stimuli presentation and task order. Randomization in experimental design can address how tasks and stimuli are presented. As a theme, randomization in research is a major topic in experimental design and creation.

Balancing is a related, equally important concept. Balancing ensures that each condition is equally replicated, ie. any number of measurements or observations from each experimental condition (such as trial order) is equal. This is important because randomization relies on the ‘law of large numbers’ which for experiments with a small number of participants could ultimately result in an imbalanced design, unless taken care of. For example, by balancing, you can ensure that an equal number of participants follow a random trial order. A balanced experiment design further ensures that equal conditions are replicated.

Labvanced as a code-free psychology experiment building platform offers many ways to ensure randomization in your experimental research, as well as a balanced experimental design.

Randomization and Balance Settings for Trials and Conditions in Labvanced

Randomization and Balance Settings for Trials and Conditions in Labvanced

Balance & Randomization in Labvanced Experiments

Balance and randomization in psychology experiments created in Labvanced is handled by multiple parts and features across the platform, such as with the:

Multiple Tasks & Blocks

  • Study Design Tab: When a study is selected, the Study Design tab is where you can add randomization separators (see image below, the thick black line) to indicate which blocks and sessions should be randomized.

Stimuli WITHIN a Single Task / Trial

  • Factors (Trial System): Using factors in the Trial System, a block design can be created for your experiment. You can subsequently specify which factors should be fixed or random.
  • Task Editor Randomization Settings: This dialog box (image above) demonstrates the type of randomization settings you can specify while creating a task. You can also indicate that the trials within each task should be balanced.
  • Data Frames:open in new window Data frames can be used to set up and create a study, but they also be applied to ensure randomization in experiments. Data frames hold stimuli similar to an array and can be used in a variety of events.
  • The Events System: Sometimes randomization in psychology experimental research requires you to counter variables or index arrays. The Events System allows you to create triggers/actions so that arrays are shuffled (rearranging the values in a random order), countering variables (fetching a value for each trial), and subsequently pulling the value from the data frame.

Adding Randomization Separators in the Study Design Tab for Blocks and Sessions

Adding Randomization Separators in the Study Design Tab for Blocks and Sessions

In order to ensure your experiment is appropriately balanced and randomized, you will most likely utilize data frames in combination with events. It is also possible to utilize factors (the Trial System). By working with the features described above, you can ensure integrity in your experimental design.

If you have any questions about how to handle randomization in your experiments, please contact us!