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Chapter 3

# PSYB45H3 Chapter Notes - Chapter 3: Applied Behavior Analysis, Dependent And Independent Variables, Binary Relation

Department
Psychology
Course Code
PSYB45H3
Professor
Amanda Uliaszek
Chapter
3

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Chapter 3
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
evaluated.
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
to apply).
Organizing data
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

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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.
Preparing graphs
Five components of a graph:
1. Axes:
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.

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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
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
variable.
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.