A critical feature of multiple-baseline designs is that:

Chapter 3

3 Graphing Behavior and Measuring Change

· ▪ What are the six essential components of a behavior modification graph?

· ▪ How do you graph behavioral data?

· ▪ What different dimensions of behavior can be shown on a graph?

· ▪ What is a functional relationship, and how do you demonstrate a functional relationship in behavior modification?

· ▪ What different research designs can be used in behavior modification research?

As we saw in Chapter 2 , people who use behavior modification define their target behavior carefully, and directly observe and record the behavior. In this way, they can document whether the behavior has indeed changed when a behavior modification procedure is implemented. The primary tool used to document behavior change is the graph.

A graph is a visual representation of the occurrence of a behavior over time. After instances of the target behavior are recorded (on a data sheet or otherwise), the information is transferred to a graph. A graph is an efficient way to view the occurrence of the behavior because it shows the results of recording during many observation periods.

Behavior analysts use graphs to identify the level of behavior before treatment and after treatment begins. In this way, they can document changes in the behavior during treatment and make decisions about the continued use of the treatment. The graph makes it easier to compare the levels of the behavior before, during, and after treatment because the levels are presented visually for comparison. In Figure 3-1 , for example, it is easy to see that the frequency of the behavior is much lower during treatment (competing response) than before treatment (baseline). This particular graph is from a student's self-management project. The student's target behavior involved biting the insides of her mouth when she studied. She recorded the behavior on a data sheet each time it occurred. After 10 days of recording the behavior without any treatment (baseline), she implemented a behavior modification plan in which she used a competing response (a behavior that is incompatible with mouth-biting and interrupts each occurrence of mouth-biting) to help her control the mouthbiting behavior. After implementing this competing response procedure, she continued to record the behavior for 20 more days. She then recorded the behavior four more times, after 1, 5, 10, and 20 weeks. The long period after treatment has been implemented is called the follow-up period. From this graph, we can conclude that the mouth-biting behavior (as recorded by the student) decreased substantially while the student implemented the treatment. We can also see that the behavior continued to occur at a low level up to 20 weeks after treatment was implemented.

Components of a Graph

In the typical behavior modification graph, time and behavior are the two variables illustrated. Each data point on a graph gives you two pieces of information: It tells you when the behavior was recorded (time) and the level of the behavior at that time. Time is indicated on the horizontal axis (also called the x-axis, or the abscissa ), and the level of the behavior is indicated on the vertical axis (also called the y-axis, or the ordinate ). In Figure 3-1 , the frequency of mouth-biting is indicated on the vertical axis, and days and weeks are indicated on the horizontal axis. By looking at this graph, you can determine the frequency of mouth-biting on any particular day, before or after treatment was implemented. Because follow-up is reported, you can also see the frequency of the behavior at intervals of up to 20 weeks.

▪Six components are necessary for a graph to be complete.

▪The y-axis and the x-axis. The vertical axis (y-axis) and the horizontal axis (x-axis) meet at the bottom left of the page. On most graphs, the x-axis is longer than the y-axis; it is usually one to two times as long ( Figure 3-2 ).

▪The labels for the y-axis and the x-axis. The y-axis label usually tells you the behavior and the dimension of the behavior that is recorded. The x-axis label usually tells you the unit of time during which the behavior is recorded. In Figure 3-3 , the y-axis label is “Hours of Studying” and the x-axis label is “Days.”

Thus, you know that the hours of studying will be recorded each day for this particular person.

▪The numbers on the y-axis and the x-axis. On the y-axis, the numbers indicate the units of measurement of the behavior; on the x-axis, the numbers indicate the units of measurement of time. There should be a hash mark on the y-axis and the x-axis to correspond to each of the numbers. In Figure 3-4 , the numbers on the y-axis indicate the number of hours the studying behavior occurred, and the numbers on the x-axis indicate the days on which studying was measured.

▪Data points. The data points must be plotted correctly to indicate the level of the behavior that occurred at each particular time period. The information on the level of the behavior and the time periods is taken from the data sheet or other behavior-recording instrument. Each data point is connected to the adjacent data points by a line ( Figure 3-5 ).

▪Phase lines. A phase line is a vertical line on a graph that indicates a change in treatment. The change can be from a no-treatment phase to a treatment phase, from a treatment phase to a no-treatment phase, or from one treatment phase to another treatment phase. A phase is a period in which the same treatment (or no treatment) is in effect. In Figure 3-6 , the phase line separates baseline (no treatment) and treatment phases. Data points are not connected across phase lines. This allows you to see differences in the level of the behavior in different phases more easily.

▪Phase labels. Each phase in a graph must be labeled. The phase label appears at the top of the graph above the particular phase ( Figure 3-7 ). Most behavior modification graphs have at least two phases that are labeled: the notreatment phase and the treatment phase. “ Baseline ” is the label most often given to the no-treatment phase. The label for the treatment phase should identify the particular treatment being used. In Figure 3-7 , the two phase labels are “Baseline” and “Behavioral Contract.” The behavioral contract is the particular treatment the student is using to increase studying. Some graphs have more than one treatment phase or more than one baseline phase.

Graphing Behavioral Data

As discussed in Chapter 2 , behavioral data are collected through direct observation and recording of the behavior on a data sheet or other instrument. Once the behavior has been recorded on the data sheet, it can be transferred to a graph. For example, Figure 3-8 a is a frequency data sheet that shows 2 weeks of behavior recording, and Figure 3-8 b is a graph of the behavioral data from the data sheet. Notice that days 1–14 on

contract in which the client agreed to smoke one fewer cigarette per day every second day. Behavioral contracts are described in Chapter 23 .

Also notice that the frequency of the behavior listed on the data sheet for each day corresponds to the frequency recorded on the graph for that day. As you look at the graph, you can immediately determine that the frequency of the behavior is much lower during treatment than during baseline. You have to look more closely at the data sheet to be able to detect the difference between baseline and treatment. Finally, notice that all six essential components of a graph are included in this graph.

Consider a second example. A completed duration data sheet is shown in Figure 3-9 a, and Figure 3-9 b is a table that summarizes the daily duration of the behavior recorded on the data sheet. Notice that the duration of the behavior listed in the summary table for each of the 20 days corresponds to the duration that was recorded each day on the data sheet.

To complete Figure 3-9 c, you must add four components. First, you should add the data points for days 8–20 and connect them. Second, include the phase line between days 7 and 8. Data points on days 7 and 8 should not be connected across the phase line. Third, add the phase label “Behavioral Contract,” to the right of the phase line. Fourth, add the label “Days” to the x-axis. When these four components are added, the graph includes all six essential components ( Figure 3-10 ).

FOR FURTHER READING Graphing in Excel

Although it is easy to construct a graph with a piece of graph paper, a ruler, and a pencil, there are graphing programs that allow you to construct a graph on your computer. Graphs can be constructed in two different Microsoft Office programs; PowerPoint and Excel ( Vaneslow & Bourret, 2012 ). Carr and Burkholder (1998) and Dixon et al. (2007) published articles in the Journal of Applied Behavior Analysis providing step-by-step instructions on how to use Microsoft Excel for constructing the types of graphs used in applied behavior analysis or behavior modification. Vaneslow and Bourret (2012) described how to use an online tutorial about constructing graphs using Microsoft excel. Students interested in learning how to construct graphs in Excel are encouraged to read these articles.

Graphing Data from Different Recording Procedures

Figures 3-8 and 3-10 illustrate graphs of frequency data and duration data, respectively. Because other types of data can be recorded, other types of graphs are possible. Regardless of the dimension of behavior or type of data that is being graphed, however, the same six components of a graph must be present. What will change with different recording procedures are the y-axis label and the numbering on the y-axis. For example, if you are recording the percentage of math problems a student completes correctly during each math class, you would label the y-axis “Percentage of Correct Math Problems” and number the y-axis from 0% to 100%. As you can see, the y-axis label identifies the behavior (correct math problems) and the type of data (percentage) that is recorded.

Consider another example. A researcher is studying Tourette's syndrome, a neurological disorder in which certain muscles in the body twitch or jerk involuntarily (these are called motor tics). The researcher uses an interval recording system and records whether a motor tic occurs during each consecutive 10-second interval in 30-minute observation periods. At the end of each observation period, the researcher calculates the percentage of intervals in which a tic occurred. The researcher labels the y-axis of the graph “Percentage of Intervals of Tics” and numbers the y-axis from 0% to 100%. Whenever an interval recording system is used, the y-axis is labeled “Percentage of Intervals of (Behavior).” The x-axis label indicates the time periods in which the behavior was recorded (e.g., “Sessions” or “Days”). The x-axis is then numbered accordingly. A session is a period in which a target behavior is observed and recorded. Once treatment is started, it is also implemented during the session.

Other aspects of a behavior may be recorded and graphed, such as intensity or product data. In each case, the y-axis label should clearly reflect the behavior and the dimension or aspect of the behavior that is recorded. For example, as a measure of how intense or serious a child's tantrums are, you might use the label “Tantrum Intensity Rating” and put the numbers of the rating scale on the y-axis. For a measure of loudness of speech, the y-axis label might be “Decibels of Speech,” with decibel levels numbered on the y-axis. To graph product recording data, you would label the y-axis to indicate the unit of measurement and the behavior. For example, “Number of Brakes Assembled” is a y-axis label that indicates the work output of a person who puts together bicycle brakes.

Research Designs

When people conduct research in behavior modification, they use research designs that include more complex types of graphs. The purpose of a research design is to determine whether the treatment (independent variable) was responsible for the observed change in the target behavior (dependent variable) and to rule out the possibility that extraneous variables caused the behavior to change. In research, an independent variable is what the researcher manipulates to produce a change in the target behavior. The target behavior is called the dependent variable . An extraneous variable, also called a confounding variable, is any event that the researcher did not plan that may have affected the behavior. For a person with a problem, it may be enough to know that the behavior changed for the better after using behavior modification procedures. However, a researcher also wants to demonstrate that the behavior modification procedure is what caused the behavior to change.

When a researcher shows that a behavior modification procedure causes a target behavior to change, the researcher is demonstrating a functional relationship between the procedure and the target behavior. That is, the researcher demonstrates that the behavior changes as a function of the procedure.

A functional relationship is established if:

· (a) a target behavior changes when an independent variable is manipulated (a procedure is implemented), while all other variables are held constant, and

· (b) the process is replicated or repeated one or more times and the behavior changes each time.

A behavior modification researcher uses a research design to demonstrate a functional relationship. A research design involves both treatment implementation and replication. If the behavior changes each time the procedure is implemented and only when the procedure is implemented, a functional relationship is demonstrated.

In this case, we would say that the researcher has demonstrated experimental control over the target behavior. It is unlikely that an extraneous variable caused the behavior change if it changed only when the treatment was implemented. This section reviews research designs used in behavior modification (for further information on behavior modification research designs, see Bailey, 1977 ; Barlow & Hersen, 1984 ; Gast, 2009 ; Hayes, Barlow, & Nelson-Gray, 1999 ; Kazdin, 2010 ; Poling & Grossett, 1986 ).

A-B Design

The simplest type of design used in behavior modification has just two phases: baseline and treatment. This is called an A-B design , where A = baseline and B = treatment. A-B designs are illustrated in Figures 3-1, 3-7, 3-8b, and 3-10. By means of an A-B design, we can compare baseline and treatment to determine whether the behavior changed in the expected way after treatment. However, the A-B design does not demonstrate a functional relationship because treatment is not replicated (implemented a second time). Therefore, the A-B design is not a true research design; it does not rule out the possibility that an extraneous variable was responsible for the behavior change. For example, although mouth-biting decreased when the competing response treatment was implemented in Figure 3-1 , it is possible that some other event (extraneous variable) occurred at the same time as treatment was implemented. In that case, the decrease in mouth-biting may have resulted from the other event or a combination of treatment and the other event. For example, the person may have seen a TV show about controlling nervous habits and learned from that how to control her mouth-biting.

The A-B design is not a true research design. Because the A-B design does not include a replication and thus does not demonstrate a functional relationship, it is rarely used by behavior modification researchers. It is most often used in applied, nonresearch situations, in which people are more interested in demonstrating that behavior change has occurred than in proving that the behavior modification procedure caused the behavior change. You probably would use an A-B graph in a self-management project to show whether your behavior changed after you implemented a behavior modification procedure.

A-B-A-B Reversal Design

The A-B-A-B reversal design is an extension of the simple A-B design (where A = baseline and B = treatment). In the A-B-A-B design, baseline and treatment phases are implemented twice. It is called a reversal design because after the first treatment phase, the researcher removes the treatment and reverses back to baseline. This second baseline is followed by replication of the treatment. Figure 3-11 illustrates an A-B-A-B design.

The A-B-A-B graph in Figure 3-11 shows the effect of a teacher's demands on the aggressive behavior of an adolescent with intellectual disability named Bob. Carr and his colleagues ( Carr, Newsom, & Binkoff, 1980 ) studied the influence of demands on Bob's aggressive behavior by alternating phases in which teachers made frequent demands with phases in which teachers made no demands. In Figure 3-11 , you can see that the behavior changed three times. In the baseline phase (“Demands”), the aggressive behavior occurred frequently. When the treatment phase (“No Demands”) was first implemented, the behavior decreased. When the second “Demands” phase occurred, the behavior returned to its level during the first “Demands” phase. Finally, when the “No Demands” phase was implemented a second time, the behavior decreased again. The fact that the behavior changed three times, and only when the phase changed, is evidence that the change in demands (rather than some extraneous variable) caused the behavior change. When the independent variable was manipulated (demands were turned on and off each time), the behavior changed accordingly. It is highly unlikely that an extraneous variable was turned on and off at exactly the same time as the demands, so it is highly unlikely that any other variable except the independent variable (change in demands) caused the behavior change.

Variations of the A-B-A-B reversal design may be used in which more than one treatment is evaluated. Suppose for example, you implemented one treatment (B) and it did not work, so you implemented a second treatment (C) and it did work. To replicate this treatment and show experimental control, you might use an A-B-C-A-C design. If the second treatment (C) resulted in a change in the target behavior each time it was implemented, you are demonstrating a functional relationship between this treatment and the behavior.

A number of considerations must be taken into account in deciding whether to use the A-B-A-B research design. First, it may not be ethical to remove the treatment in the second baseline if the behavior is dangerous (e.g., self-injurious behavior). Second, you must be fairly certain that the level of the behavior will reverse when treatment is withdrawn. If the behavior fails to change when the treatment is withdrawn, a functional relationship is not demonstrated. Another consideration is whether you can actually remove the treatment after it is implemented. For example, if the treatment is a teaching procedure and the subject learns a new behavior, you cannot take away the learning that took place. (For a more detailed discussion of considerations in the use of the A-B-A-B design, see Bailey [1977] , Bailey and Burch [2002] , Barlow and Hersen [1984] , Gast [2009] , and Kazdin [2010] .)

Multiple-Baseline Design

There are three types of multiple-baseline designs.

▪In a multiple-baseline-across-subjects design , there is a baseline and a treatment phase for the same target behavior of two or more subjects.

▪In a multiple-baseline-across-behaviors design , there is a baseline and treatment phase for two or more behaviors of the same subject.

▪In a multiple-baseline-across-settings design , there is a baseline and treatment phase for two or more settings in which the same behavior of the same subject is measured.

Remember that the A-B-A-B design can also have two baseline phases and two treatment phases, but both baseline and treatment phases occur for the same behavior of the same subject in the same setting. With the multiple-baseline design, the different baseline and treatment phases occur for different subjects, or for different behaviors, or in different settings.

A multiple-baseline design may be used:

· (a) when you are interested in the same target behavior exhibited by multiple subjects,

· (b) when you have targeted more than one behavior of the same subject, or

· (c) when you are measuring a subject's behavior across two or more settings.

A multiple-baseline design is useful when you cannot use an A-B-A-B design for the reasons listed earlier. The multiple-baseline design and the appropriate time to use it are described in more detail by Bailey (1977) , Bailey and Burch (2002) , Barlow and Hersen (1984) , Gast (2009) , and Kazdin (2010) .

Figure 3-12 illustrates the multiple-baseline-across-subjects design. This graph, from a study by DeVries, Burnette, and Redmon (1991) , shows the effect of an intervention involving feedback on the percentage of time that emergency department nurses wore rubber gloves when they had contact with patients. Notice that there is a baseline and treatment phase for four different subjects (nurses). Figure 3-12 also illustrates a critical feature of the multiple-baseline design: The baselines for each subject are of different lengths. Treatment is implemented for subject 1, while subjects 2, 3, and 4 are still in baseline. Then, treatment is implemented for subject 2, while subjects 3 and 4 are still in base line. Next, treatment is implemented for subject 3 and, finally, for subject 4. When treatment is implemented at different times, we say that treatment is staggered over time. Notice that the behavior increased for each subject only after the treatment phase was started for that subject. When treatment was implemented for subject 1, the behavior increased, but the behavior did not increase at that time for subjects 2, 3, and 4, who were still in baseline and had not yet received treatment. The fact that the behavior changed for each subject only after treatment started is evidence that the treatment, rather than an extraneous variable, caused the behavior change. It is highly unlikely that an extraneous variable would happen to occur at exactly the same time that treatment started for each of the four subjects.

A multiple-baseline-across-behaviors design is illustrated in Figure 3-13 . This graph, from a study by Franco, Christoff, Crimmins, and Kelly (1983) , shows the effect of treatment (social skills training) on four different social behaviors of a shy adolescent: asking questions, acknowledging other people's comments, making eye contact, and showing affect (e.g., smiling). Notice in this graph that treatment is staggered across the four behaviors, and that each of the behaviors changes only after treatment is implemented for that particular behavior. Because each of the four behaviors changed only after treatment was implemented for that behavior, the researchers demonstrated that treatment, rather than some extraneous variable, was responsible for the behavior change.

A graph used in a multiple-baseline-across-settings design would look like those in Figures 3-12 and 3-13. The difference is that in a multiple-baseline-acrosssettings graph, the same behavior of the same subject is being recorded in baseline and treatment phases in two or more different settings, and treatment is staggered across the settings.

Draw a graph of a multiple-baseline-across-settings design with hypothetical data. Be sure to include all six components of a complete graph. Assume that you have recorded the disruptive behavior of a student in two different class rooms using an interval recording system. Include baseline and treatment across two settings in the graph.

The graph in Figure 3-14 , from a study by Dunlap, Kern-Dunlap, Clarke, and Robbins (1991) , shows the percentage of intervals of disruptive behavior by a student during baseline and treatment (revised curriculum) in two settings, the morning and afternoon classrooms. It also shows follow-up, in which the researchers collected data once a week for 10 weeks. Notice that treatment is staggered across settings; it was implemented first in one setting and then in the other, and the student's disruptive behavior changed only after treatment was implemented in each setting. Your graph of a multiple-baseline-across-settings design would look like Figure 3-14 .

FOR FURTHER READING Nonconcurrent Multiple-Baseline-Across-Subjects Design

In a multiple-baseline-across-subjects design, data collection starts in each of the baselines (for each of the subjects) at around the same time and the treatment phase is then staggered across time. However, in a nonconcurrent multiple baseline (MBL) across subjects design ( Carr, 2005 ; Watson & Workman, 1981 ) the subjects do not participate in the study concurrently. In a nonconcurrent MBL design, the baselines for two or more subjects may begin at different points in time. The nonconcurrent MBL is equivalent to a number of different A-B designs with each participant having a different baseline length. Treatment is then staggered across baselines of different lengths rather than across time. As long as each of the subjects has a different number of baseline data points before treatment is implemented, the research design is considered a nonconcurrent MBL. The advantage of a nonconcurrent MBL is that participants may be evaluated at different points in time; they may be brought into the study consecutively rather than concurrently, which is often more practical for researchers to carry out ( Carr, 2005 ).

Alternating-Treatments Design

The alternating-treatments design (ATD) , also called a multi-element design, differs from the research designs just reviewed in that baseline and treatment conditions (or two treatment conditions) are conducted in rapid succession and compared with each other. For example, treatment is implemented on one day, baseline the next day, treatment the next day, baseline the next day, and so on. In the A-B, A-B-A-B, or multiple-baseline designs, a treatment phase occurs after a baseline phase has been implemented for a period of time; that is, baseline and treatment occur sequentially. In these designs, a baseline or treatment phase is conducted until a number of data points are collected (usually at least three) and there is no trend in the data. A trend means the data are increasing or decreasing across a phase. In the ATD, two conditions (baseline and treatment or two different treatments) occur during alternating days or sessions. Therefore, the two conditions can be compared within the same time period. This is valuable because any extraneous variables would have a similar effect on both conditions, and thus an extraneous variable could not be the cause of any differences between conditions.

Consider the following example of an ATD. A teacher wants to determine whether violent cartoons lead to aggressive behavior in preschool children. The teacher uses an ATD to demonstrate a functional relationship between violent cartoons and aggressive behavior. On one day, the preschoolers do not watch any cartoons (baseline) and the teacher records the students' aggressive behavior. The next day, the students watch a violent cartoon and the teacher again records their aggressive behavior. The teacher continues to alternate a day with no cartoons and a day with cartoons. After a few weeks, the teacher can determine whether a functional relationship exists. If there is consistently more aggressive behavior on cartoon days and less aggressive behavior on no-cartoon days, the teacher has demonstrated a functional relationship between violent cartoons and aggressive behavior in the preschoolers. An example of a graph from this hypothetical ATD is shown in Figure 3-15 .