ABAB Design Ethical considerations Is it ethical to remove a treatment that appears to be beneficial (i.e., implement the second “A” baseline stage)? Dilemma between goal of understanding and goal of creating change.

Multiple-Baselines Design The multiple-baselines design solves this ethical problem. Also uses a comparison between baseline and treatment Does not withdraw treatment establish several baselines implement treatment in one baseline at a time Multiple baselines can be implemented across situations One individual observed in several situations across individuals Several individuals are observed across behaviors Several behaviors of one individual are measured

Multiple-Baselines Design Comparing baseline to treatment Observing individual(s) behavior(s) across time to establish baselines Implement treatment when there is a steady baseline Only implement one treatment at a time for a particular baseline To interpret the treatment If treatment is effective, behavior changes when the treatment is implemented and does not change for the remaining baselines. Behavior changes only when the treatment is implemented and directly following the introduction of treatment.

Multiple-Baselines Design Multiple baseline across situations Example of Leslie with selective mutism Would speak at home but not in other situations Target several situations; restaurant, meeting adults, playing with peers Start collecting baseline data in these situations Several baselines started around the same time Apply treatment to one situation at a time In this case treatment was role playing with reinforcement at home

Multiple-Baselines Design Multiple baselines across individuals Example: Allison and Ayllon (1980) evaluating the effectiveness of a behavioral coaching on athletics procedures included systematic verbal feedback, positive and negative reinforcement Establish baselines for each of the individuals Start baselines around the same time When the behavior has stabilized An intervention is introduced one individual at a time

Multiple-Baselines Design

Multiple-Baselines Design Multiple baseline across behaviors Example: A schoolteacher is using a behavioral intervention that will help manage the behavior of a problem child in the classroom. The child does not stay at her desk when asked to do so, does not remain quiet during “quiet times,” and exhibits other behaviors that disrupt the teaching environment. Use a positive reinforcer such as tokens or small toys in a multiple-baseline across behaviors design to improve the child’s behavior.

Multiple-Baselines Design Multiple-baselines across behaviors: Behavior #1: Leaving desk. Implement treatment (reward system) on Day 3 Behavior improves (decrease in frequency)

Multiple-Baselines Design Multiple-baselines across behaviors: Behavior #2: Blurt out questions without raising hand Implement treatment (reward system) on Day 6 Behavior improves (decrease in frequency)

Multiple-Baselines Design Multiple-baselines across behaviors: Behavior #3: Not quiet during quiet times Implement treatment (reward system) on Day 8 Behavior improves (decrease in frequency)

Multiple-Baselines Design How many baselines are needed? A minimum of two; three or four are recommended. FIGURE 9.3 Frequency of spoon-banging responses across baseline, treatment, and follow-up phases of study What if behavior changes before the intervention? The reasons for behavior change become hard to interpret; researchers analyze the situation to see if some aspect of treatment generalized. What if the treatment generalizes to other behaviors, situations, or individuals? Researchers should try to anticipate when generalization may occur and modify their research design accordingly.

Problems with All Single-Subject Designs Problems with Baseline Records Unstable baselines increasing, or decreasing trends in behavior, the effects of treatment are hard to interpret. Extreme variability in baseline behavior difficult to detect a clear discontinuity in behavior when treatment is implemented. Solutions: Look for factors that may contribute to variability wait for baseline behavior to stabilize average baseline data points across observations.

Problems with All Single-Subject Designs Problems with Baseline Records

Problems with Single-Subject Designs Baseline Records, Whether increasing or decreasing baseline trends are a problem depends on the desired direction of behavior change. Suppose the goal is to increase the frequency of a behavior. If the baseline shows an increasing frequency of behavior, determining whether behavior increases following treatment will be difficult. However, if the baseline shows a decreasing trend and treatment reverses this trend, we can be confident about the effect of the treatment.

Problems with Single-Subject Designs External Validity Single-subject designs are frequently criticized for their limited external validity. Will treatment effects observed for one individual generalize to other individuals? Reasons why external validity may not be limited: Treatments are usually powerful. Multiple-baselines designs can be used to demonstrate generality of effects. Group treatment can be used to demonstrate effectiveness of treatment.

Quasi-Experimental Designs Chapter Ten Goal: To improve the conditions in which people live and work. Natural settings: Messy, “real world” — hard to establish experimental control. Quasi-experiments: Experimental procedures that approximate the conditions of highly controlled laboratory experiments. Program Evaluation: Applied research used to learn whether real-world treatments work.

Experiments in Natural Settings. Researchers do experiments in natural settings for several reasons Test the external validity of a laboratory finding Attempt to improve conditions under which people live and work Determining if a treatment is effective make decisions about continuing the treatment make decisions about spending money make decisions about investing more time and effort Natural Settings: such as schools, work place, government, hospitals, playground, etc.

Characteristics of True Experiments True experiments have a high degree of control manipulate an independent variable random assignment to conditions hold conditions constant Allows comparison of treated to untreated groups A true experiment is one that leads to an unambiguous outcome regarding what caused a result on the dependent variable. However, this much control usually can not be done in a natural setting so there is less internal validity. A trade off between better external validity and less internal validity

Differences between experiments in the lab and in natural settings Experiments that are conducted outside the laboratory are likely to differ in a number of significant ways from those done in the laboratory. Control in experiments from manipulation of I.V. random assignment of participants hold other factors constant External validity Better in natural settings then the laboratory Goals basic laboratory research to understanding how things work will have very good internal validity Applied research in natural settings tries to improve the lives of people and has internal validity Consequences Applied research in a natural setting can impact large numbers of people For example: Head Start program, Sesame Street program Sometimes referred to as “social experiments”

FIGURE 10.1 As a social experiment, Sesame Street was designed to improve the education of hundreds of thousands of children.

Obstacles to Conducting True Experiments in Natural Settings Obtaining permission and gaining access to participants Can not get a random sample of participants Random assignment of participants Random assignment is the best way to determine if a new treatment really is effective May not be able to use random assignment because of intact groups such as classrooms of children Also can be viewed as unfair because some people who may need treatment don’t receive it. A waiting-list control group may be used so that people randomly assigned to the control group receive treatment after the study is completed.