Independent Measures Design

In independent measures design, also known as between-subjects design, separate groups of participants are assigned to different treatment conditions. Each group of participants represents a different level of the independent variable being studied.

Definition

Independent measures design refers to a research design where participants are randomly assigned to one of two or more groups, with each group receiving a different treatment or level of the independent variable being investigated.

Procedure

The independent measures design involves the following steps:

  1. Participants are randomly assigned to different groups.
  2. Each group receives a different treatment or level of the independent variable.
  3. Data is collected from each group.
  4. The dependent variable is measured and compared between the different groups.

Advantages

Independent measures design offers several advantages:

  • Reduces the likelihood of order effects, such as practice effects or fatigue effects, as each participant experiences only one treatment condition.
  • Allows researchers to study multiple treatment conditions simultaneously.
  • Helps minimize demand characteristics and experimenter bias, as participants are unaware of other treatment conditions.

Disadvantages

There are some drawbacks associated with independent measures design:

  • Requires a larger sample size to achieve sufficient statistical power compared to other designs.
  • Potential individual differences between groups can introduce bias and confounding variables.
  • Random assignment may not always be feasible or ethical.

Example

An independent measures design could be used to compare the effectiveness of two different study techniques on exam performance. Participants would be randomly assigned to either a group using technique A or a group using technique B. Their exam scores would then be compared to determine if one technique is more effective than the other.