How about the power analysis?

The required sample size for an interventional research study using a multiple baseline small N design can vary depending on several factors. The multiple baseline design is often used in single-case experimental research, where individual participants serve as their own control, and baseline data are collected across multiple baselines before the intervention is introduced.

Since small N designs focus on studying a few participants in-depth, the concept of "sample size" is a bit different than in traditional group-based research. Instead of aiming for a large sample size like in traditional experimental designs, the emphasis is on the number of cases or participants being studied intensively.

The specific considerations that influence the required sample size in a multiple baseline small N design include:

Given the nature of small N designs, there isn't a strict rule for a specific required sample size. Researchers often conduct power analyses or simulations to estimate the necessary sample size based on the factors mentioned above. The goal is to strike a balance between having a sufficiently large enough sample to detect meaningful effects while also maintaining the intensive and detailed nature of the design.

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The sample size for an interventional research using multiple baseline small N design depends on several factors, such as the number of participants, the number of phases, the number of behaviors or outcomes measured, the expected effect size, and the desired statistical power. There is no definitive formula for calculating the sample size for this type of design, but some general guidelines are:

• The number of participants should be at least three, and preferably more, to allow for a reasonable degree of generalization and replication1.
• The number of phases should be at least three (baseline, intervention, and follow-up), and preferably more, to allow for a clear demonstration of functional relation between the intervention and the outcome12.
• The number of behaviors or outcomes measured should be at least two, and preferably more, to allow for a comparison of the effects of the intervention across different dimensions or domains12.
• The expected effect size should be large enough to be clinically or practically meaningful, and to be detectable with the available sample size3.
• The desired statistical power should be at least 0.8, which means that there is an 80% chance of detecting a true effect if it exists3.

One way to estimate the sample size for a multiple baseline small N design is to use a simulation approach, which involves generating hypothetical data based on the assumed parameters of the design, and then applying appropriate statistical tests to see how often the null hypothesis is rejected3. This can be done using specialized software or online tools4. Alternatively, one can use existing data from similar studies or pilot studies to estimate the sample size needed to achieve a certain level of power3.