In Applied Behavior Analysis (ABA), multiple-baseline design is a widely used method for analyzing the effects of an independent variable (IV) across different conditions—whether it’s behaviors, settings, or participants. This design is particularly useful when you want to avoid withdrawing treatment but still need to demonstrate experimental control. Let’s break down how it works, the variations of the design, and its pros and cons.
How Does Multiple-Baseline Design Work?
The core idea of multiple-baseline design is to introduce the IV at different times across behaviors, settings, or participants, while keeping others in baseline. This staggered approach helps demonstrate a functional relationship by showing that changes only occur when the IV is applied, and not before.
The design relies on the stability of baseline measures to clearly indicate that changes in the target behavior, setting, or participant are due to the IV.
Variations of the Multiple-Baseline Design
- Multiple Baselines Across Behaviors
The IV is introduced to different behaviors one at a time, with others remaining in baseline. This method helps assess how the IV impacts multiple behaviors in the same individual. - Multiple Baselines Across Settings
Here, the IV is applied in different settings sequentially, while keeping other settings in baseline. This variation is useful when a behavior occurs in multiple contexts. - Multiple Baselines Across Participants
The IV is introduced to different participants one at a time. This is a common approach when you’re analyzing the same behavior across multiple people. - Multiple Probe Design
Similar to multiple baseline, but data is collected intermittently during the baseline phases rather than continuously. This is helpful when continuous baseline measurement is impractical. - Delayed Multiple Baselines
In this variation, baseline measures are added in a staggered fashion. This design is helpful when immediate implementation is not possible, allowing flexibility in the experiment’s start times.
Assumptions and Guidelines for Using Multiple-Baseline Design
- Select Independent Yet Functionally Similar Behaviors: The behaviors should be functionally similar, but independent enough to show a distinct effect when the IV is applied.
- Avoid Applying the IV Too Soon: Wait until the baseline is stable before introducing the IV to ensure that changes are due to the intervention, not natural fluctuations.
- Vary the Lengths of Baseline: Having different lengths of baseline phases for each behavior, setting, or participant helps establish stronger experimental control.
- Intervene on the Most Stable Baseline First: Begin with the behavior, setting, or participant that shows the most stable baseline to ensure a clear trend.
- Use Variations When Needed: If collecting extensive baseline data isn’t feasible, consider using delayed multiple baseline or multiple probe designs.
Advantages of Multiple-Baseline Design
- No Withdrawal Required: One of the biggest advantages is that you don’t have to withdraw the intervention, making it ideal for behaviors where treatment removal is not ethical or practical.
- Easy to Conceptualize: It provides a clear, step-by-step approach to evaluating the effects of the IV across different conditions, making it accessible even to those new to ABA.
Disadvantages of Multiple-Baseline Design
- Not the Strongest Evidence of Experimental Control: While it’s widely used, this design may not always provide the most convincing demonstration of causality compared to other designs like reversal or alternating treatments.
- Time and Resource-Intensive: Collecting stable baseline data across multiple behaviors, settings, or participants can be time-consuming and require significant resources.
- Potential Delay in Treatment: Treatment may be delayed for certain behaviors, settings, or participants, which can be a drawback when addressing urgent issues.
Conclusion
Multiple-baseline design is a versatile and ethical tool in ABA, offering a way to demonstrate experimental control without withdrawing treatment. Although it can be resource-intensive and may not always provide the strongest evidence of causality, its variations allow flexibility in application. By following the key guidelines, behavior analysts can effectively implement this design to assess and improve interventions across various settings, behaviors, and participants.
