McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. Using Single-Subject Designs

2 Baseline design Observations made without treatment (baseline phase) and with treatment (intervention phase) Dynamic design Used to establish how behavior differs from one level of an independent variable to another Discrete trials design Behavior measured over a series of discrete trials must be averaged to provide relatively stable indices of behavior under the various treatment conditions

3 1.In a baseline design, individual subjects are observed under each of several phases Multiple responses are recorded in a phase before the next phase begins 2. Extensive observations are made during the baseline phase to establish a behavioral baseline against which any changes due to the independent variable are compared A behavioral baseline will also be established during the intervention phase In an ABAB design the base line and intervention phases are repeated

4 3.Each subject is observed under all phases, with each treatment phase repeated at least twice This repetition, or intrasubject replication, establishes the reliability of the findings 4. Subjects usually remain in each phase until a stability criterion is met 5.Multiple subjects may be included in the experiment This intersubject replication helps establish the generality of the findings across subjects

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6 Choosing a stability criterion Guarantees that a subject will stay in a phase until the baseline no longer shows systematic changes Criterion should not be too strict or too loose Removes “transitional” behaviors from analysis In some research situations you may want to use fixed times for phases rather than a stability criterion Dealing with uncontrolled variability Sources of uncontrolled variability must be identified Once identified the uncontrolled variability is controlled

7 Determining the generality of findings Intersubject replication is used to establish generality across subjects May not always succeed Failure of intersubject replication may reveal interesting effects Generality across situations requires replication, which can be of two types Systematic replications: Replications that incorporate variations on the original experiment Direct replication: Exact replication of an experiment

8 Drifting Baselines A baseline that does not stabilize but continues to show systematic variations (drift) A baseline may drift systematically upward or downward Drift can be taken into account, and effects of variables can be determined Unrecoverable Baselines Behavior cannot be returned to original baseline after a treatment (carryover effects) A baseline may be partially recoverable

9 Unequal Baselines Between Subjects Baselines for different subjects level off at very different values Steps may be taken to equalize baselines across subjects Inappropriate Baseline Levels Baseline levels that are too high or low may mask effects of a treatment (similar to range effects) Baseline levels will have to be adjusted so that they are at appropriate levels

10 Single-Factor Design Basic ABAB design can be extended to accommodate multiple levels of an independent variable Example: ABACBC design This design poses problems of counterbalancing to deal with carryover effects Multifactor Designs More than one independent variable included Can assess main effects and interactions

11 Multiple Baseline Design Used when a treatment produces an irreversible change in behavior Simultaneously use multiple behaviors, each with its own baseline Multiple behaviors must be independent from each other

12 Design includes a constantly changing independent variable Used to measure behavior dynamics-regular patterns of behavior that change over time Very similar to the baseline design The design is used infrequently but may become more popular with increased interest in behavior dynamics

13 1.Individual subjects receive each treatment condition of the experiment dozens (perhaps hundreds) of times. Each exposure to a treatment or trial, produces one data point for each dependent variable measured. 2.Extraneous variables that might introduce unwanted variability in the dependent variable are tightly controlled

14 3.If feasible, the order of presenting the treatments is randomized or counterbalanced to control order effects 4.The behavior of individual subjects undergoing the same treatment may be compared to provide intersubject replication

15 Inferential statistics have typically not been applied to data from single-subject research Some argue that inferential statistics should be used when variability cannot be controlled Inferential statistics for group designs cannot be used for single-subject data Multiple observations taken within a treatment to provide an estimate of uncontrolled error variance and compared to between-groups variability Serial dependency limits this approach

16 Focus on tightly controlling error variance Focus on individual behavior makes identifying and controlling sources of error variance easy Focus on individual behavior may reveal subtle effects of an independent variable lost with a group approach Causal relationships can be established with very few subjects

17 Making multiple observations is time- consuming and can be tedious The single-subject approach is not appropriate for all research questions (e.g., jury decision making) Results may be of limited generality All variables that can cause error variance cannot be identified and controlled