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Instructions: Select either reversal designs or multiple-baseline designs and discuss the primary limitations.

Multiple-baseline designs have emerged as fundamental tools in behavioral research, providing valuable insights. However, it is crucial to acknowledge the inherent limitations of this design, such as the frequent use of small sample sizes, which could restrict the applicability of findings to broader populations. In this context, researchers must carefully examine the characteristics of participants and studied behaviors, being aware of challenges associated with generalizing results.

Furthermore, this week's reading highlights another limitation related to concerns about the potential extension of treatment effects to a second behavior, creating a conflict in clinical objectives. According to Gravetter and Forzano (2018), in the context of a multiple-baseline experiment, it is imperative that the treatment exclusively influences the specific behavior under examination. This exclusivity would ensure the credibility of treatment effects.

Moreover, in multiple-baseline studies, result clarity may face challenges due to individual differences between participants or across different behaviors. A notable scenario arises when examining various behaviors within the same research; while one behavior may exhibit a significant and immediate change in response to applied treatment, another may show only a subtle or gradual change. This discrepancy in response patterns between behaviors introduces uncertainties about the consistency of treatment effects, posing potential obstacles in interpreting study findings.

Therefore, addressing the possible presence of biases in these designs is crucial to enhance the validity and robustness of the results. This can be achieved through strategies such as careful matching and consideration of confounding variables. Additionally, managing the variability in the duration of baseline phases among participants should be handled with approaches that standardize durations or provide clear justifications for observed variations. This approach will significantly strengthen conclusions and promote a more comprehensive understanding of behavior patterns and effective interventions.