The multiple baseline family of designs includes multiple baseline and multiple probe designs. Coincidental events include divorce, changing of living situation, changes in school or work schedule, physical injury, changes in a setting such as construction, changes in coworkers or staffing, and many others. Cooper et al. If a potential treatment effect is seen in one tier, the researcher cannot refer to data from the same day in an untreated tier because the tiers are not synchronized in real time and may not even overlap in real time. - 216.238.99.111. These views of multiple baseline designs have been carried through into much of the single-case methodological literature and textbooks to the current day. Only through repeated measurement across all tiers from the start of a study can you be confident that maturation and history threats are not influencing observed outcomes. Hayes argued that fortunately the logic of the strategy does not really require (p. 206) an across-tier comparison because the within-tier comparison rules out these threats. Disadvantages Having identified the criticisms of nonconcurrent multiple baseline designs, we now turn to a detailed analysis of threats to internal validity and features that can control these threats. Creating Single-Subject Research Design Graphs It is interesting that this emphasis on across-tier comparisons is the opposite of that evident in Baer et al. Additionally, the Johnston, J. M., Pennypacker, H. S., & Green, G. (2010). This raises the question of how many replications are necessary to establish internal validity. On resolving ambiguities of the multiple-baseline design: Problems and recommendations. WebExtended baselines or interventions may threaten experimental control, delayed intervention may pose a risk to client or others as an ethical concern. The multiple baseline design was initially described by Baer et al. We use the term potential treatment effect to emphasize that the evidence provided by this single AB within-tier comparison is not sufficient to draw a strong causal conclusion because many threats to internal validity may be plausible alternative explanations for the data patterns. It is possible that a coincidental event may be present for all tiers but have different effects on different tiers. Adding multiple tiers to the design allows for two types of additional comparisons to be used to evaluate, and perhaps rule out, these threats: (1) replications of baseline-treatment comparisons within subsequent tiers (i.e., horizontal analysis), and (2) comparisons across tiers (i.e., vertical analysis). Multiple a potential treatment effect in the first tier would be vulnerable to the threat that the changes in data could be a result of Attachment L: Strengths and Limitations of the Single- Subject Google Scholar.