Chapter 3: Graphing Data
Chapter 3: Graphing and Experimental Design
Graphing Data
Basic Elements of a Graph
Axes: Y (vertical), X (horizontal)
Axis Labels: Y (R), X (sessions, days, etc.)
Units (numbers) for Y and X axes
Data points: X-Y plots representing values of R across sessions
Phase-change lines: Vertical lines separating experimental conditions
Phase labels: Descriptions of experimental conditions
Examples of Graphs (see examples on p. 2)
Multiple Plot, More than One Set of Data
Multiple Plot, More than One X Axis
Multiple Plot, Double-Y Axis (Used when data represent different units of measurement)
Bar Graph (Used for Plotting Discontinuous Data)
Frequency Plot versus Cumulative Record
a) Frequency plot: Each data-point value is graphed separately
b) Cumulative record: Each data point-value is added to the previous value
Characteristics of Data Evaluated via Graphical Analysis
Level: Mean (average) value of a set of data
Trend: Systematic change in level across measurements
Variability: Degree of fluctuation in a data set around its mean
Multiple Plot, more than one data set
Multiple plot, more than one X axis
Multiple plot, double Y axis
Bar graph
Frequency plot vs. cumulative record
Experimental Design
Terminology
1. Dependent variable (DV): Variable observed to determine the effects of an experimental manipulation (behavior)
2. Independent variable (IV): Variable manipulated by the experimenter (environmental event or treatment)
3. Confounding variable: Source of influence other than the IV that may produce changes in the DV
4. Experimental design: Rules for applying an IV so as to examine its effects on a DV
5. Functional relation: A relationship in which changes in one variable (DV) are demonstrated to be the result (a function) of changes in another variable (IV)
6. Single subject design: An experimental design in which a functional relationship can be demonstrated with the behavior of only one subject
7. Baseline: Condition in effect prior to introduction of the IV
8. A-B notation system: “A” denotes baseline; subsequent letters (“B”, “C,” etc.) denote different IVs
9. Replication: Duplication of earlier conditions in an experiment
A-B Design
Definition: Single introduction of at least one IV on at least one baseline
Advantage: Repeated measurement under Bl (A) and Rx (B) conditions allows examination of changes in level, trend, and variability
Limitations:
No replication
Therefore, does not rule out the influence of confounding variables
Therefore, no demonstration of a functional relation
Reversal Design
Definition: Introduction and subsequent removal of at least one IV on one BL
Variations: ABAB, ABA, BAB, ABAC, etc.
Advantage: Simple yet powerful demonstration of experimental control
Limitations:
Detrimental effects of reversal: Ethical considerations
Irreversibility: Failure to reproduce effect observed in a previous phase even though conditions are arranged identically
Sequence/Order effect: Influence of a previous manipulation on responding in a later condition (e.g., training ( contingencies)
Multiple Baseline Design
Definition: Sequential introduction of an IV across more than one BL
Variations: Across subjects, behaviors, settings
Advantage: Does not require reversal to show experimental control
Disadvantages:
Stability requirement more cumbersome than with a reversal design
Potential generalization across baselines (more likely with MBL across behaviors or settings)
Multielement Design (aka Alternating Treatments)
Definition: Rapid alternation of BL and IV conditions (or 2 or more IVs) on a single BL
Variations: BL vs. Rx, Rx1 vs. Rx2 (with or without BL), etc.
Advantages:
Does not require baseline (although preferred)
Accommodates trends and instability
Minimizes sequence effects (limited exposure to one condition)
Ideally suited to complex analyses (parametric, component, comparative)
Limitation: Multiple treatment interference
Changing Criterion Design
Definition: Introduction of one IV on a single BL in step-wise fashion, with steps corresponding to progressive changes in either response requirement or value of the IV
Advantage: No reversal and no additional baselines required to show experimental control
Disadvantages:
Requires control over both direction and level of change
Requires careful selection of criterion change
Potential generalization
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