ALTERNATING TREATMENT DESIGNS and YOU! Tristram Jones, Ph.D. Kaplan University PS512 Unit V

What the heck IS an Alternating Treatment Design? (You ask!)

Alternating treatment designs rely on the rapid alteration of two or more distinct treatments and record their effects on a single target behavior! GREAT SCOTT, DOCTOR --WHAT WOULD A THING LIKE THAT BE USED FOR???

Well… primarily…. The alternating- treatment design is used in order to ascertain the comparative effect of two or more treatments alternated in rapid succession and correlated changes are plotted on a graph to facilitate comparison

Here is a REALLY SIMPLE example!

And you can alternate treatments within sessions, across different times of the day, or even on different days!

All aspects of treatment must be counterbalanced! IVs must be presented in Blocks or randomly. If you have 3 treatments, A, B, & C, then you spray them randomly or group them in the possible blocks, which Are ABC, BCA, CAB, ACB, BAC and CBA, right? Each treatment is repeated the same number of times!

Counterbalancing helps to rule out a variety of extraneous variables! This means that all differences in conditions, such as time of day, experimenter, or location of treatment, must all be repeated so as to be reliably counterbalanced to rule out confounds!

Remember! Some type of repetition is ALWAYS necessary to show INTERNAL VALIDITY in ABA design!

And even if your own textbooks won’t tell you, EXTERNAL VALIDITY is a lost cause!

The second important point is that subjects should be able to discriminate between treatments! Why on earth is this important? The text doesn’t bother to say! Well, for one thing, it ensures that the IVs utilized are sufficiently distinct from one another. Remember when we f DVs studied multiple baseline design we learned that if DVs are too much alike, they may exhibit covariance that confounds research purposes– it is just too likely they will covary, so they don’t add much to verification! Well, the same can be said about Independent Variables. If they are too much alike, we might not be measuring a meaningful variation of the DV! In fact, random chance could be measured by accident! (COMPARE: Assimilation effects – discussed later.)

so make sure your subject is aware, because a distinct stimulus should be associated with each treatment! SO THAT WAS THE BLEACHED WHALE BLUBBER, RICHIE, AND THIS HERE IS THE CURDLED FISH EYES! Amazingly enough, you also do not need to use a baseline with Alternating Treatment Designs! (But you can!)

Who needs a baseline? Whenever possible baseline data ought to be collected, because two of the three basic types of ATDs actually do use such data—but the third does not! This has the advantage of allowing the treatment phase to begin immediately!

The three types of ALTERNATING BASELINE See. I thought you’d ASK, so here you go: ATDs WITHOUT BASELINE BASELINE followed by ATD BASELINE followed by ATD with a FINAL TREATMENT phase

WAIT A MINUTE! There are really four kinds! We can also do a really cool design called Alternating Treatment Design with CONTROL! You just include NO TREATMENT as one of your treatments HINT: Want some fun? Try it with AOD treatment!

“Thus: No treatment” can also be used as an IV to act as a control! Yesterday when you had your seizure we gave you Depakote. Day before we gave you Dilantin! Today, we do nothing! As is always the case with withheld treatment, there can be ethics issues, too!

You can have a real baseline in BASELINE FOLLOWED BY ALTERNATING TREATMENTS design! As in all single subject designs, baseline represents a stable rate of responding. Works when treatment need not be rushed

And with baseline followed by alternating treatments and a final treatment! Where science and ethics combine forces! This works well ethically because it is important to maintain the form of treatment that works best. So:  Collect initial baseline data.  Introduce alternating treatments.  Determine which works best.  Continue using the most effective treatment,  Pretty simple, really!

BUT then there is the danger of dread carry over effect! A carryover effect is an effect that "carries over" from one experimental condition to another. Whenever subjects are placed in more than one condition, like in singles-subjects designs, there is a possibility of carryover effects…and they are internal validity threats!

1. Practice Effects – Occur when subjects get better at the task over time because of practice, so that they perform best in the later conditions. Four types of common carry-over confounds are as follows:

Fatigue Effects 2. Fatigue Effects – Occur when subjects get worse at the task over time because of fatigue. They might even quit trying and just “go through the motions.”

ASSIMILATION EFFECTS 3.Assimilation Effects – Occur when a stimulus is perceived as particularly similar to a preceding stimulus (assimilation)

Finally, a type of “Catching On…” 4. Hypothesis guessing” This is when repeated exposure to conditions makes it clear to subjects what the independent variable is, so that they “catch on” to the hypothesis being tested. In many cases, subjects will then behave the way they think you want them too, or the opposite if they are the stubborn type!.

PRESENTING THE DATA Teacher is interested in increasing her student’s rate of reading. She has two fluency programs that she believes may work with Bob. She implements the two different programs on alternate days over 19 days. After the completion of the experiment, she is confident that intervention #1 will achieve better results for improving Bob’s reading skills

WITHDRAWAL? What about using WITHDRAWAL? withdrawal procedures have been instituted following the sequential administration of treatments to target behavior(s) (Russo & Koegel), But not so much!

Counterbalancing is the key! In many cases, the carryover effect in one direction will simply cancel out the carryover effect in the other direction. This will always be truer if you test many subjects.

And here are the big three as they apply in ATD! PREDICTION! Each data point serves as a predictor of future behavior under the same treatment!

VERIFICATION! Each data point serves as verification of PREVIOUS performance under same treatment!

REPLICATION: Each data point replicates the differential effects of other treatments—in other words, the spread should remain the same!

There is also a simultaneous and an adapted multiple treatment design—so you can have it all at once, or target different behaviors! They are tricky to analyze! So don’t worry about them unless you’re REALLY interested!