Threats to Internal Validity in Multiple-Baseline Design Variations PMC
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The plotting of individual participants’ data, examining the data, and making judgements about whether and to what extent the independent variable had an effect on the dependent variable. The researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. We will use it to expand the Project Baseline database and develop advanced tools for collecting, organizing, and analyzing health information. As well, in partnership with qualified researchers and organizations, we will use the data to uncover new medical insights or develop new health products.
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If an effective treatment were to have a broad impact on multiple tiers, the logic of the design would be to falsely attribute these effects to possible extraneous variables. Second, the across-tier comparison assumes that extraneous variables will affect multiple tiers similarly. If an extraneous variable were to have a tier-specific effect, it would be falsely interpreted as a treatment effect. This comparison can reveal the influence of an extraneous variable only if it causes a change in several tiers at about the same time. 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. As Kazdin and Kopel point out, it is clearly possible for treatments to have broad effects on multiple tiers and for extraneous variables to have narrow effects on a specific tier.
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Therefore, concurrent and nonconcurrent designs are virtually identical in control for testing and session experience. Testing and session experience encompasses features of experimental sessions (both baseline and intervention phases) other than the independent variable that could cause changes in behavior. 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.
Managing threats to validity
Maturation refers to extraneous variables such physical growth, physiological changes, typical interactions with social and physical environments, academic instruction, and behavior management procedures that tend to cause changes in behavior over time (cf., Shadish et al., 2002). For example, physical growth and experiences with the environment can accumulate and result in relatively sudden behavioral changes when a toddler begins to walk. Figure 4.4 uses the multiple baseline design to determine if the timing of treatment is important. Notice how the behavioral change took place for each subject immediately following the introduction of treatment.
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Thus, the assumption that the coincidental event contacts all tiers would be valid and the across-tier analysis might reveal the effects of this sort of event. This insensitivity is not due to poor experimental design or implementation, it is built in to the nature of multiple baseline designs across participants. Each tier involves a unique participant and there is a class of coincidental events that contact a single participant. The across-tier comparison is an additional basis for evaluating alternative explanations.
A Second Methodological Criticism of Nonconcurrent Designs: Prediction, Verification, Replication
Change sets work much the same in both applications, except that DM allows you to roll back a change set. Other possible stream strategies involve multiple streams to support other patterns such as promotion and parallel development. When you create your component, the system creates an initial stream for you, also called the “default stream”. In a single stream strategy, you could continue to use this initial stream indefinitely.
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Multiple-Baseline Designs
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If a potential treatment effect is seen in one tier and on the same day there is no change in other tiers, this is taken as strong evidence that the potential treatment effect was not a result of a coincidental event, because a coincidental event would have had an effect on all tiers. 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. The strength of this control is a function of our certainty that no single coincidental event could have caused more than one change in the dependent variable. The dimension of time is recognized in the requirement that phase changes be lagged in real time—that is, the date on which the phase changes are made. In general, a longer lag is better because it reduces the chance that an event could impact multiple tiers.
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Examples could include family events, illness, changed social interactions (e.g., breaking up with a partner), losing or gaining access to a social service program, etc. These coincidental events would contact all tiers of a multiple baseline that include this individual participant, but not tiers that do not involve this participant. In a concurrent multiple baseline that involves a single participant across settings, behaviors, antecedent stimuli etc., this kind of event would be expected to contact all tiers.
This controversy began soon after the first formal description of nonconcurrent multiple baseline designs by Hayes (1981) and Watson and Workman (1981). However, the specific issues in this controversy have never been thoroughly identified, discussed, and resolved; and instead a consensus emerged without the issues being explicitly addressed. This consensus is that nonconcurrent multiple baseline designs are substantially weaker than concurrent designs (e.g., Cooper et al., 2020; Johnston et al., 2020; Kazdin, 2021).
The multiple baseline design was first reported in 1960 as used in basic operant research. It was applied in the late 1960s to human experiments in response to practical and ethical issues that arose in withdrawing apparently successful treatments from human subjects. In it two or more (often three) behaviors, people or settings are plotted in a staggered graph where a change is made to one, but not the other two, and then to the second, but not the third behavior, person or setting. Differential changes that occur to each behavior, person or in each setting help to strengthen what is essentially an AB design with its problematic competing hypotheses.
You will be part of the dedicated group of people working on our mission, with opportunities to shape the Project Baseline community over time. Your involvement matters, and we hope you are as excited as we are to take action and tackle the challenge of preventing disease for future generations. If necessary, in any of the applications, you can also branch a new stream from any baseline. For these reasons, you can consider baselines like backups, so take them as often as the amount of time you are willing to lose to rework. In RM and DM, teams can use separate change sets to temporarily isolate changes from the main stream, but all change sets are delivered to the main stream. Multi-family home plans are available in many different architectural styles and square footage sizes.
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