Shortcuts to Understanding the Basics Behind Each Experimental Design

I often hear that experimental design is a source of a lot of anxiety. It makes sense, of course; most of our experiences are applied, and experimental design is like this distant cousin we know some stuff about, but we never see. I do, however, believe that understanding the components of a scientific experiment is an important layer in understanding the more applied content that you need to know for your exam. But, what I want to do today, is give you a few shortcuts to help you on exam day. What this should do, is help you eliminate some answer options but also, more importantly, give you a head start on understanding the basics behind each design. So, I do ask, please, that you make sure that you spend the time needed to get more well-rounded in understanding experimental designs. But, the exam is just a minimum requirement, so you don’t have to be an exam expert; however, reading about it and exploring it like you plan to be can only be a positive experience. Plus, it’s always fun to look at the outcomes of some of the amazing technologies of our field; and the only way that you can really get an appreciation for the efficacy of what we do is by looking at all of these empirically based evidences, which are through experimental design. Anyway, the study hints: Multiple baseline designs– so what you want to remember about each of the designs as I go through them right now, the shortcut study tips thing, is the amount of IVs versus DVs– independent variables versus dependent variables. That should help go through some of the questions, at minimum, okay? Multiple baseline design: one independent variable and two or more dependent variables. This design examines how one independent variable generalizes its effects across two or more behaviors, settings, and subjects; so, this is sort of the generalization design shortcut. Changing criterion design has one independent variable and one dependent variable. This design examines how the criteria for reinforcement can control responding. So, if the behavior is meeting the criteria, then that proves that the criteria are, in fact, controlling the behavior. Reversal design– one independent variable and one dependent variable. This is the design with the strongest establishment of proof or control. It literally looks at how behavior does with the independent variable versus without it. And the withdrawal design does essentially the same thing in terms of the IVs and the DVs. The multiple treatment reversal, now this is a variation of the reversal design, so it also looks to establish proof or control, but this design looks at how the behavior does with multiple independent variables and how those effects are in comparison; this one has two or more independent variables and one dependent variable. And, finally, the alternating treatment or multi-element design or, honestly, a lot of other a.k.a.’s that this design has. This design has a minimum of two independent variables and a maximum of four, and there’s only one dependent variable. This design looks at how the different independent variables affect the behavior. But, unlike the multiple treatment reversal design, this design doesn’t exhibit that control in the same proof way because we don’t remove the IVs but implement them all at once to see the effects. This is a good design for quickly figuring out which independent variable to use, but also, this is the design that we use in functional analysis, except that the IVs are the conditions rather than the treatments when we talk about using this design in the functional analysis. Anyway, this is just a very superficial shortcut, so I do hope it helps. But, do study up more, and make sure you’re super fluent in the designs. I hope that helps, guys, and I’ll see you next time.

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Dependent variable

In an experimental design, this is the target behavior of interest.

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Irreversibility

A behavior for which the effects of treatment cannot be undone. Therefore, it cannot return to a baseline level of responding when an independent variable is removed.

Multielement design

An experimental design in which two or more independent variables are presented in rapidly alternating succession, and the differential effects of each independent variable on the target behavior are measured.

Multiple treatment interference

In an experimental design that requires the implementation of more than one independent variable to one subject, the outcome of one treatment may be influenced by the effect of another.

Multiple baseline design

An experimental design in which the effect of one independent variable is measured and evaluated within and across combinations of two or more subjects, behaviors, or settings.

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Reversal design

Any experimental design in which responding is reversed to a level obtained in a previous condition by alternating between specific independent variables and baseline conditions.

Sequence effects

A confounding effect on a subject’s behavior in a given condition that results from the subject’s experience with a previous condition.

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