D-5: Use single-subject experimental designs (e.g., Reversal, Multiple Baseline, Multielement, Changing Criterion) ©
Target Terms: Reversal (A-B-A-B) Design, Multiple Baseline Design, Multielement/Alternating Treatment Design, Changing Criterion Design
Reversal (A-B-A-B) Design
Definition: An experimental design where baseline conditions (A) and an intervention conditions (B) are reversed with the goal of strengthening experimental control (i.e. demonstrating that the change in the dependent variable is due to the change in the independent variable).
Example in clinical context: A behavior analyst collects baseline data (A) on a student’s tantrum behavior. They begin to implement an intervention (B) and collects data on the student’s tantrum behavior. After several trials of the intervention, the behavior analyst withdrawals the intervention, waits for responding to stabilize, and again implements the intervention.
Example in supervision/consultation context: A behavior analyst is consulting in a classroom where they are providing support to the paraprofessionals in the room. The behavior analyst collects baseline data (A) on the paraprofessional’s use of specific praise, and begins to implement an intervention (B) which targets a increase in specific praise behavior using visual and auditory prompts for staff. The behavior analyst withdrawals the intervention, and rates of the target behavior return to baseline rates. The behavior analyst reinstates the intervention and finds that the use of specific praise once again increases.
Why it matters: Reversal designs are a powerful single-subject design for demonstrating a functional relation between an independent and dependent variable. Reversal designs involve prediction, verification and replication. There are several variations of reversal designs depending on the severity of the target behavior or type of reinforcement schedule used. One major limitation of A-B-A-B designs is that they are not suitable for a target behavior that cannot be “unlearned;” for example, teaching someone to read and then withdrawing the intervention would not result in a loss of existing reading ability.
Multiple Baseline Design
Definition: An experimental design where implementation of the intervention is staggered in a stepwise fashion across behaviors, settings, and subjects.
Example in clinical context: A behavior analyst wants to target a student’s dropping behavior in two different settings: the classroom and in the hallway. The behavior analyst begins to collect baseline data on the dropping behavior in both settings. After a steady state of responding is demonstrated, the behavior analyst implements the intervention in the first setting, the classroom, while holding the hallway in baseline. After steady responding is achieved in the first implementation setting, the intervention is applied to the second setting which is the hallway.
Example in supervision/consultation context: A behavioral analyst is consulting for a small company that has a uniform set of goals for employees to achieve. They conduct a multiple baseline design on one of these goals for five employees. The behavior analyst begins to collect baseline data for all five employees. After a steady state of responding is achieved for all five employees, the behavior analyst implements an intervention to address the first employee goal on the first employee while holding the other four employees in baseline. After a steady state of responding is achieved with the first employee, the behavior analyst implements the intervention with the second employee and follows this stepwise fashion with all employees.
Why it matters: Multiple baseline designs are the most widely used design due to their flexibility. They do not require the withdrawal of a treatment variable. Multiple baseline designs involve prediction, verification and replication. There are variations of the multiple baseline design.
Multielement/Alternating Treatments Design
Definition: An experimental design where two or more conditions are presented in rapidly alternating succession independent of the level of responding and the effects on the target behavior.
Example in clinical context: A behavior analysts is comparing two treatments with a client on the response rate of their aggressive behavior. The behavior analyst conducts a multielement/alternating treatments design on Treatment A and Treatment B. Treatment A did not appear to have an effect on the aggressive behavior, but Treatment B showed a sharp decrease in aggressive behavior.
Example in supervision/consultation context: A supervisor is comparing two types of supervision modalities to determine which one is more effective in teaching ABA concepts. The supervisor conducts a multielement/alternating treatments design with their supervisee on supervision types 1 and 2. The first, Type 1, was correlated with a significant amount of change in the supervisee’s knowledge, whereas Type 2 did not demonstrate any change. The supervisor concludes Type 1 is likely to be a more effective means of teaching novel concepts for this supervisee.
Why it matters: Multielement/Alternating treatments designs are used to evaluate which independent variable would be best to utilize with a client. They do not require withdrawal of the intervention and can be used to quickly make comparisons between treatment conditions. Multielement/Alternating treatment designs involve prediction, verification and replication. There are several variations of the multielement/alternative treatment designs including with or without baseline.
Changing Criterion Design
Definition: An experimental design where the initial baseline phases are followed by a series of treatment phases consisting of successive and gradual changing criteria for reinforcement or punishment.
Example in clinical context: A behavior analyst wants to assess how a client’s behavior changes when they provide reinforcement for every five responses per minute, then ten responses per minute and so on. The criterion increases as the client demonstrates stable states of responding.
Example in supervision/consultation context: A behavior analyst is consulting with a client who wants to decrease the number of cigarettes they smoke per day with the goal of quitting. The client currently smokes 16 cigarettes per day. The first criterion the behavior analyst sets before the client can earn reinforcement is 13 cigarettes per day, to 10, seven, five and one. The criteria decrease as the client demonstrates stable states of responding.
Why it matters: Changing criterion designs can only be used when the behavior is already in the learner’s repertoire. They do not require reversal or withdrawal of treatment. Changing criterion designs do not allow for comparison. They also involve prediction, verification and replication. Experimental control is demonstrated by the extent to which the level of responding changes in response to each new criterion.