6-fundamentals of single case experimental design ll Flashcards
Based on an AB design, but the treatment phase is DIVIDED into subphases
Each sub phase involves a DIFFERENT behavioral CRITERION i.e., a different value of the IV
CRITERION in each subphase more closely resembles the TERMINAL behavior GOAL
Changing Criterion Design: procedure
Each sub phase provides:
- A means to gauge the effects of changing the IV value from the PREVIOUS phase
- REPLICATES the effects of changing the IV value
- A baseline for the FOLLOWING phase
PREDICTION if no other change in the IV is made
Changing criterion design: logic
Experimental CONTROL is demonstrated when performance closely matches the specified criteria:
UNLIKELY that an EXTRANEOUS variable produced the change across conditions if the behavior changes when and ONLY when a NEW criterion is ??
Changing criterion design: CONTROL
Do you need perfect correspondence with criteria to show experimental control?
NO…
-Examine mean SHIFT to determine if it shows STEP-WISE changes
Examine PERCENTAGE of points that meet criterion
(Changing criterion Design: evaluation
Implement BI-DIRECTIONAL changes to BOLSTER demonstration of experimental control
- Changing criteria to a PREVIOUS subphase VALUE and observing if that behavior reverts to that criteria - /Rules out THREATS such as MATURATION and PRACTICE effects
Bi-Directional criteria… Changing criteria
Number of criterion changes
At minimum, TWO, otherwise an AB design
- Too many changes, to rapidly, may OBSCURE orderly EFFECTS
Changing criterion: number of phases
As always, determined by STABILITY (again each sub phase acts as baseline for the next phase)
Phases can be shorter if behavior can change rapidly
LENGTH of phases should VARY- an additional demonstration of control: you change or keep behavior at a given level for as long or as briefly as you can
Changing criterion: PHASE LENGTH (I.e., session per phase)
SMALL, INITIAL criterion changes maximize Probability of success, but…
If too small, why not be able to detect CHANGES IN BEHAVIOR
If too LARGE, may be difficult for the subject to
MEET CRITERIA
Rule of thumb: SMALL changes for a very STABLE behaviors, LARGER changes for VARIABLE behaviors
Changing criterion: degree of change.
SMALL changes for very STABLE behaviors, LARGER changes for variable behavior
Rule of thumb: changing criterion
Treatments do NOT have to be WITHDRAWN. (relative to reversal designs)
Does NOT require multiple behaviors, subjects, or settings (relative to multiple baseline)
All subjects can receive TREATMENTS after the SAME length of baseline
Changing criterion: advantages
Requires considerable TIME and EFFORT in planning
Phase length, degree of change, phase number should be planned in advance
Difficult to INTERPRET when behavior does NOT closely MATCH criteria
What if behavior drops to zero during first criterion change? Where is the experimental control?
Changing criterion: limitations
Use when it is meaningful to MEASURE behavior change in STEPWISE increments
E.g., NUMBER of products such as cigarettes smoked: math problems completed; connecting links in a behavioral chain
Use to demonstrate experimental CONTROL during FADING and SHAPING procedures.
Changing criterion use
Also known as:
SIMULTANEOUS treatment design – but not really simultaneous
CONCURRENT schedule design – but unlike a concurrent schedule of reinforcement
Multiple SCHEDULE design – but not necessarily like a multiple schedule
MULTIELEMENT design
Alternating Treatment Design BUT NOT Necessarily TREATMENTS
Rapid ALTERNATION of two or more IVs or LEVELS of the independent variable
Each session may be a different condition
REPEATED measurement of behavior while the conditions alternate RAPIDLY
INDEPENDENT VARIABLES continue alternating INDEPENDENT of the level of responding
No WAITING for STEADY state
Multi elemental description
Shares logical properties with the REVERSAL DESIGN (akin to A reversal design with very brief phases)
Each data point :
•PREDICTS FUTURE behavior in the same condition
•VERIFIES previous predictions
•COMPARISONS against the PREDICTIONS made by the data in the OTHER conditions
Multi element logic
Rapid comparison of TREATMENT to BASELINE
Comparison of TWO or more treatments
Comparison of two or more ASSESSMENT conditions (e.g., functional analysis of behavior)
YOKED Elements - rapid alternation makes yoking more meaningful.
—Yoking: elements from one condition are linked to elements of a second condition
Multi element: Common Uses
Elements from one condition are linked to elements of a second condition
Yoked
Plus pre-treatment baseline, preferable unless clinically contra indicated
With no baseline
•Often includes a baseline or control as one of the alternating conditions
•If so, may differ from a pre-treatment baseline due to multiple treatment interference
With baseline plus a final treatment phase
• Clinically prudent in applied research.
•Permit detection of possible multiple treatment interference
Multielement: common variations
This Multi element variation is preferable unless clinically contra indicated
Multi element plus pre-treatment baseline
This multi element variation often includes a baseline or control as one of the alternating conditions. If so, may differ from a pre-treatment baseline due to multiple treatment interference.
Can include as one of its conditions, a “no treatment “ condition, Which is essentially a baseline condition that is not the initial distinct phase but rather is alternated randomly with all the other conditions under consideration.
Multi element with no baseline
This variation is clinically prudent in applied research. Permits detection of possible multiple treatment interference
Multi element with baseline plus a final treatment phase
Each sub phase involves a different behavioral criterion i.e., a different value of the IV
Changing criterion: Procedure
Criterion in each subphase more closely resembles the terminal behavior goal
Changing criterion design
Concluding that the independent variable has produced a change in the dependent variable but in fact the relation does not exist… False positive
Type l error
Analysis of single subject data
Concluding that the independent variable has not produced a change in the dependent variable when in fact it has… False negative
Type ll error
Doing a treatment analysis, a behavioral intervention is evaluated and found effective in decreasing problem behavior. A drug intervention was arm concurrently, independent of the behavioral intervention. The drug is later withdrawn , and problem behavior increases during the behavioral treatment analysis. The initial conclusion of the behavioral interventions effect may be considered…
A type l error
Implement bi-directional changes to
Bolster demonstration of experimental control
Treatments do not have to be withdrawn wrote it to reversal designs.
Does not require multiple behaviors, subjects, or settings, relative to multiple baseline
All subjects can receive treatment after the same length of baseline
Changing criterion advantages
Subject to multiple treatment interference. Would the effects of anyone treatment be different if it wasn’t being simultaneously compared with another?
Can be examined/controlled by including a final best treatment phase.m
Unsuitable for individuals that have problems forming discriminations. Differences between conditions will appear as a function of how easily the conditions can be discriminated. I.e., how dissimilar They are
Like reversal limited to behavior that is reversible at least pliable
Less suitable for interventions that produce change slowly or require continuous implementation e.g., weight control intervention
May require considerable care in doing the necessary COUNTERBALANCING
Multielement limitations
Less of a problem if the independent variables are themselves quite different.
Providing and anchoring additional stimuli e.g., therapist, ambient stimuli, to facilitate discrimination
Reducing the number of conditions
Instructional control been appropriate
Reverting to other designs
Multi element enhance discriminability
Inclusion of elements from two or more designs within the SAME experiment.
•Enhances the certainty of experimental control if it meets the requirements of multiple designs, especially if the conclusions of the planned design are tenuous, e.g., discriminability problems in multi element, add reversal.
•Can’ answer multiple questions, e.g., is A better than BL and is B better than A?
Often not planned, but used to enhance conclusions as the data evolve. Let data be your guide
Design combinations
Systematically withdrawing treatment components to see if behavior change is maintained
Used To evaluate maintenance of treatment effects in the absence of the intervention
Used as a Fading process
Component analysis/sequential withdrawal
The systematic examination of the effects of a range of values of the IV. Not just absences versus presence of the IV
Examples, the effects of differing values of a reinforcement schedule, e.g., FR1 verses of FR 10
Comparison of treatments at different strengths, e.g., brief time out versus long time out
Parametric analysis: description
Determining effective paramedic values of contingencies, such as Reinforcer Duration or magnitude, reinforcer delay, reinforcer quality, response effort or schedule.
As such, useful for
ease of implementation questions,
treatment degradation questions
Parametric analysis use
Assessment of behavior on occasions when the contingencies arranged in the analysis are not in effect
Uses, evaluate whether treatment effects are evident before treatment occurs.
Is further training really needed?
Examples, transfer of training, multiple probe technique
Use of probes: description
Events not related to the independent variable that may affect the dependent variable.
Potential to alter the results if not controlled
Extraneous variables