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Thus, to demonstrate experimental control, the effects of the independent variable must not generalize; and to detect an extraneous variable through the across-tier comparison, the effects of that extraneous variable must generalize. This raises the question of how many replications are necessary to establish internal validity. As we mentioned above, across-tier comparisons require the assumptions that coincidental events will (1) contact and (2) have similar effects on all tiers of the design. First, studies differ with respect to the experimental challenges imposed by the phenomena under study. PubMed Other threats to internal validity such as (1) ambiguous temporal precedence, (2) selection, (3) regression, (4) attrition, and (5) instrumentation are addressed primarily through other design features. 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. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. Reasons for these specifications will become clear later in the article.) Houghton Mifflin. Without the latter you cannot conclude, with confidence, that the intervention alone is responsible for observed behavior changes since baseline (or probe) data are not concurrently collected on all tiers from the start of the investigation. Therefore, we view this approach as less desirable than the standard multiple baseline design across subjects and suggest that it should be employed only when the standard approach is not feasible. For example, in a multiple baseline across participants, all the residents of a group home may contact peanut butter and jelly sandwiches for lunch but this change may disrupt the behavior of residents with a mild peanut allergy, but not other residents. This is a significant problem for the across-tier comparison because its logic is dependent on these two assumptions. Tactics of scientific research. An important drawback of pre-experimental designs is that they are subject to numerous threats to their validity. It is clear that we cannot claim that these assumptions are always valid for multiple baseline designs. 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. We will explore these issues extensively after we sketch the historical development of multiple baseline designs and criticisms of nonconcurrent multiple baselines. The tutorial begins with instructions for how to create a simple multiple condition/phase (e.g., withdrawal research design) line graph. This might be conveniently reported in the methods section or a small table in an appendix. Longer lags and more isolated tiers can reduce the number of tiers necessary to render extraneous variables implausible explanations of results. Journal of Applied Behavior Analysis, 30(3), 533544. https://doi.org/10.1037/a0029312, Watson, P. J., & Workman, E. A. Attachment L: Strengths and Limitations of the Single- Subject Each replication requires an assumption of a separate event coinciding with a distinct phase change. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. For example, phase changes in two consecutive tiers may be lagged by three sessions, but if one to three sessions are conducted per day, the baseline phases could include the same number of days (problem for controlling maturation) and the phase change could occur on the same day in both tiers (problem for controlling coincidental events). Some current dimensions of applied behavior analysis. However, if this within-tier pattern is replicated in multiple tiers after differing numbers of baseline sessions, this threat becomes increasingly implausible. Kazdin and Kopel (1975) parallel much of Hersen and Barlows (1976) commentaryFootnote 3 but they also point out an apparent contradiction in the assumptions about behavior on which the multiple baseline design is built. https://doi.org/10.4324/9781315537085. 234235). The nature of control for coincidental events (i.e., history) provided by the within-tier comparison in both concurrent and nonconcurrent multiple baseline designs is relatively straightforward. A researcher who puts great confidence in the across-tier comparison could falsely reject the idea that coincidental events were the cause of observed effects. WebWeaknesses of multiple baseline designs: There are certain functional relations that may not be clearly understood by this design This design is time consuming and We can identify at least three general categories of issues that influence the number of tiers required to render threats implausible: challenges associated with the phenomena under study, experimental design features, and data analysis issues. If the pattern of change shortly after implementation of the treatment is replicated in the other tiers after differing lengths of time in baseline (i.e., different amounts of maturation), maturation becomes increasingly implausible as an alternative explanation. Each of these three types of threats point us to distinct dimensions of the lag between phase changes that must be controlled for in order to achieve experimental control: for maturation, we control for elapsed time (e.g., days); for testing and session experience, we must be concerned with the number of sessions; and for coincidental events, we must be concerned with the specific time periods (i.e., calendar dates) of the study. We examine how these comparisons address maturation, testing and session experience, and coincidental events. If either of these assumptions are not valid for a coincidental event, then the presence and function of that event would not be revealed by the across-tier analysis. This would draw attention to the relationship between the prediction from baseline and the (possible) contradiction of that prediction by the obtained treatment-phase data, and the replication of this prediction-contradiction pair in subsequent tiers. In both forms of multiple baseline designs, a potential treatment effect in the first tier would be vulnerable to the threat that the changes in data could be a result of testing or session experience. Single-case designs for educational research. Part of Springer Nature. cycles approach: a multiple baseline Thus, both of the articles introducing nonconcurrent multiple baselines made explicit arguments that replicated within-tier comparisons are sufficient to address the threat of coincidental events. The author has no known conflicts of interest to disclose. In such an instance, there may be a disruption to experimental control in only one-tier of the design and not others, thus influencing the degree of internal If this requirement is not met and a single extraneous event could explain the pattern of data in multiple tiers, then replications of the within-tier comparison do not rule out threats to internal validity as strongly. Multiple baseline procedure. However, an across-tier comparison is not definitive because testing or session experience could affect the tiers differently. In addition, arranging tiers that are isolated in other dimensions (e.g., location, behaviors, participants) confers overall strength, not weakness, for addressing coincidental events. Ten sessions of baseline would be expected to have similar effects whether they occur in January or June. Effects of instructional set and experimenter influence on observer reliability. These could include presence of observers, testing procedures, exposure to testing stimuli, attention from implementers, being removed from the typical setting, exposure to a special setting, and so on. In general, in a concurrent multiple baseline design across any factor, the across-tier analysis is inherently insensitive to coincidental events that are limited to a single tier of that factor. Single-Subject Research Designs Research Methods in A multiple baseline design with tiers conducted at different times during each day could show disruption due to this coincidental event in the tier assessed early in the day but not in tiers that are assessed later in the day. Kazdin, A. E., & Kopel, S. A. An alternative explanation would have to suggest, for example, that in one tier, experience with 5 baseline sessions produced an effect coincident with the phase change; in a second tier, 10 baseline sessions had this effect, again coinciding with the phase change; and in a third tier, 15 baseline sessions produced this kind of change and happened to correlate with the phase change. This argument rests on the assumptions that any extraneous variable that affects one tier will (1) contact all tiers and (2) have a similar effect on all tiers. So, for example, session 10 in tier 2 must take place at some time between tier 1s session 9 and 11. A close examination of threats to internal validity in multiple baseline designs reveals and clarifies the critical design features that determine the degree of experimental control and internal validity of either type of multiple baseline. All three of these dimensions of lag are necessary to rigorously control for commonly recognized threats to internal validity and establish experimental control. Google Scholar. Thus, a multiple baseline with phase changes sufficiently lagged (in terms of number of sessions) provides rigorous control for this threat. The across-tier comparison of concurrent multiple baseline designs is less certain and definitive than it may appear. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. If a nonconcurrent multiple baseline has a long lag in real time between phase changes (e.g., weeks or months), this may provide stronger control than a design with a lag of one or several days. Rand McNally. PubMedGoogle Scholar. The details of situations in which this across-tier comparison is valid for ruling out threats to internal validity are more complex than they may appear. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). (1975). https://doi.org/10.1007/s40614-022-00343-0, DOI: https://doi.org/10.1007/s40614-022-00343-0. https://doi.org/10.1037/0022-006X.49.2.193. 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. They argue that because nonconcurrent multiple baseline designs lack an across-tier comparison in real time (the criticism described above), they cannot verify the prediction of the behavior pattern in the absences of intervention. The across-tier analysis can provide an additional set of comparisons that may reveal a maturation effect, but it is not a conclusive test. Recognizing these three dimensions of lag has implications for reporting multiple baseline designs. The bottom line is that the experimenter can never know whether a coincidental event has contacted only a single tier of a concurrent multiple baseline and, therefore, whether it is possible for the across-tier comparison to detect this threat. This is consistent with the judgements made by numerous existing standards and recommendations (e.g., Gast et al., 2018; Horner et al., 2005; Kazdin, 2021; Kratochwill et al., 2013). This controversy began soon after the first formal description of nonconcurrent multiple baseline designs by Hayes (1981) and Watson and Workman (1981). Second, in a remarkably understated reference to the across-tier comparison, Baer et al. In a review of the SCD literature, Shadish and Sullivan (2011) found multiple baseline designs making up 79% of the SCD literature (54% multiple baseline alone, 25% mixed/combined designs).