Using data and research methods in behavior analysis
Using data to measure changes: 1) How we use data. 2) Organizing data. 3) Graphing data
Using graphs and basic research methods: 1) Graphic analysis. 2) Basic research designs
Advanced research designs in behavior analysis: 1) Multiple baseline designs. 2) Changing criterion and
alternating treatment designs
Evaluating resulting changes in behavior: 1) Dimensions of evaluation. 2) Preparing a report
Tips on using graphs and research methods
Using data to measure changes
Using data to measure changes: Anytime applied behavior analysis is used, data must be collected and
How we use data: 1) Frequency of behavior compared to its baseline level. If intervention worked,
frequency of behavior should decrease (or increase) from baseline.
Intervention: A program or period of time in which action is taken to alter an existing situation, such as
a target behavior
Baseline: Two meanings: 1) Refers to the data collected before the intervention begins. 2) Refers to the
period of time during which those data were collected. Main role of baseline data is to give a reverence
point for comparison during the intervention phase.
Data: Tells us the current status and history of variables. Variables: behavior, antecedents,
consequences. The data we collect on these variables can clarify issues or concerns at different points in
planning and conducting a program to change a target behavior (such as choosing the best techniques
Arithmetic calculations: When data varies a great deal, calculating an average or mean for a set of data
smoothes out the record and gives a general level of behavior.
Tables: A table is a systematic arrangement of data or other information in rows and columns for easy
examination. It organizes the data visually, allowing us to see patterns and make comparisons in the
data plainly and quickly.
Characteristics of a table: 1) Assessment method of behavior being measured. 2) Ordered into groups. 3)
Specific variables. 4) Descriptive title
Graphing data Graph: A graph is a drawing that displays variations within a set of data. Typically shows how one
variable changed with changes in another variable.
Types of graphs: 1) Line graphs. 2) Bar graphs. 3) Cumulative graphs.
Line graphs: Uses straight lines to connect successive data points that represent the intersects of
plotted values for the variables scaled along the horizontal and vertical axes. Horizontal axis typically
scales time or sessions (spanning baseline and intervention phases of a program).
Bar graphs: Uses vertically arranged rectangles to represent data points scaled along the vertical axis.
Horizontal axis is usually set conditions, and not scaled.
Cumulative graphs: Measure of behavior accumulates across units scaled along the horizontal axis. This
differs from normal line graphs in the successive accumulation of data points as the trial period
increases. If no responses occur on the next data set, the measurement stays the same, since nothing
has changed. The steeper the slope in a cumulative graph, the higher the response rate.
Five components of a graph:
2. Axis scaling and labels
3. Data points
4. Phase lines and labels: Baseline phase vs Intervention phase
5. Caption / Title
Using graphs and basic research methods
Evaluating success of a program: 1) Has behavior changed? 2) Why did behavior change?
Graphic analysis: Can be used to 1) Assess effectiveness of intervention. 2) Provide feedback as
reinforcement for desired behavior.
Judging effectiveness of a program: Assess two trends (general patterns of change in the behavior over
time): 1) Whether behavior has improved from baseline to intervention. 2) Whether behavior has
continued to improve across time during the intervention.
Clarifying a graphic analysis
Trend lines: Added to graphs to make graphic analysis clearer. A line of best fit (represents all data
points within a time period). A trend line should bisect all points in half. Involves three steps:
1. Calculate the means for the baseline and intervention data you want to compare
2. Place a data point on the graph for each mean halfway across the corresponding time period
3. For each time period you’re comparing, draw a trend line. Data problems in graphic analysis
Difficulties in evaluating trends: Come from data problems of three types:
1. Excessive variability
2. Decreasing baseline trend (behavioral excess)
3. Increasing baseline trend (for behavioral deficit)
Excessive variability: Increasing sample size smoothes out excessive variability.
Behavioral excess: Having a decreasing baseline when there’s a behavioral excess is a problem because
you cannot know if behavioral change was as a result of intervention or due to natural causes.
Behavioral deficit: Having an increasing baseline when there’s a behavioral deficit is a problem because
you cannot know if behavioral change was as a result of intervention or due to natural causes
In general: Whenever baseline data show excessive variability or an increasing or decreasing trend in
relation to a behavioral goal, you should consider delaying the start of the intervention and collect
additional baseline data
Basic research designs
Understanding why change occurred: Conduct experiment to understand why a behavioral change
occurred. Research in behavioral analysis typically uses single-subject designs (or single case designs).
Single-subject designs: Examines the target behavior of a person across time, while intervention is
either in effect or absent.
Variables: Most research includes two types of variables: 1) Independent variable. 2) Dependent
Independent variable: Tested for its potential or suspected influence. The presence or absence of an
intervention is the independent variable.
Dependent variable: Assessed to see if its value corresponds to (depends on) variations in the
independent variable. Target behavior is the dependent variable.
Cause and effect: Did an intervention cause the behavior to change? To determine this relationship, you
must rule out the action of extraneous variables by holding them constant across your other variables.
Functional relation: The behavior changes as a function of the independent variable.
The AB design
The AB design: A: Indicates baseline phase in which intervention was absent. B: Indicates intervention
phase. AB design is useful when you need to determine the extent to which behavior changed. It is less
than idea if you want to isolate the cause of the change. The reversal design, ABA design, ABAB designs
The reversal design: Reversal designs have a series of phases in which an intervention is alternately
absent and present. Usually with three or four phases: 3) ABA design. 4) ABAB design. Reversal designs
have a distinct advantage over AB designs. They can demonstrate increases and decreases in the
behavior that correspond to the presence and absence of the intervention. As a result, reversal designs
are able to provide strong evidence for a functional relationship (cause and effect). Reversal designs
show both that the behavior changed and why it changed.
ABA design: Three phases: 1) Baseline. 2) Intervention. 3) Reversal; return to baseline.
Reversal phase: Allows us to see whether the behavioral changes that occurred during intervention
revert toward baseline levels when the intervention is absent
ABAB design: Four phases: 1) Baseline. 2) Intervention. 3) Reversal. 4) Intervention. By reinstating
intervention in the last phase, we can see whether the behavior responds again to the program’s
Example of ABAB design:
1. Target behavior: reduce loud abusive statements from an individual institutionalized with
2. Intervention applied two methods: 1) Punishment. 2) Rewards.
3. Punishment: moving the person to a corner of the room when outbursts occurred and leaving
them there for 2 minutes
4. Reward: not having an outburst for certain periods of time.
5. Data shows that loud vocalizations dropped sharply when intervention was in force
6. Data shows that it returned to baseline levels during the reversal phase
7. This data suggests that her outbursts were reduced by the intervention
Problems in using reversal designs
1. Effect of the intervention may not be fully or substantially reversible (i.e. when intervention is
withdrawn, the behavior may not revert towards baseline levels). Leads to difficulty interpreting
whether or not it was the intervention that caused a change in behavior during the first
2. Must decide what conditions would constitute a reversal of the intervention. For example;
Intervention involved reinforcing a behavior that was not reinforced in baseline. To arrange a
reversal terminate the reinforcement. Removing the reinforcement also removes the
“contingency” … page 49
3. It may be undesirable or unethical to withdraw an intervention that appears to have produced a
beneficial effect. E.g. Successfully reduced self-harm in disordered children not ethical to
return such behavior. Why doesn’t behavior revert?: Behavior may have been changed permanently by the original
intervention phase (learning occurred). If you think that a target behavior will not regress to baseline,
then you should not use a reversal design.
Advanced research designs in behavior analysis
Multiple baseline designs: More than one AB design is conducted with all baselines started at about the
same time and proceeding together for a while. Each baseline continues for a different length of time
before the intervention begins. Multiple baseline designs have two important characteristics:
1. No reversal phases; useful when behavior change is permanent or when withdrawing the
intervention is undesirable.
2. Introduction of the intervention is staggered across the separate AB designs so that a baseline
phase in at least one AB design overlaps an intervention phase in at least one other AB design
Importance of overlap: Overlap between the multiple baseline designs enables comparison between
target behavior in baseline with behavior in the intervention simultaneously within and across designs.
1. Allows us to see whether the behavior changed
2. Allows us to see why the behavior changed.
3. If the behavior in intervention was markedly improved over baseline in each of the multiple AB
designs, we can conclude that it changed
a. If a change occurred only after the intervention was introduced, we can conclude that
the changes resulted from the intervention and not some other factor.
Multiple baseline across behaviors design:
1. Uses separate AB designs for each of two or more different behaviors for a single individual in a
2. Simultaneously monitor two or more different behavi