Correlated-Groups and Single-Subject Designs Graziano and Raulin Research Methods: Chapter 11 This multimedia product and its contents are protected under copyright law. The following are prohibited by law: (1) Any public performance or display, including transmission of any image over a network; (2) Preparation of any derivative work, including the extraction, in whole or in part, of any images; (3) Any rental, lease, or lending of the program. Copyright © Allyn & Bacon (2007) Graziano & Raulin (1997)

Correlated-Groups Designs Introduces a correlation between groups in the way groups are formed Within-subjects design: Same participants in each group Matched-groups design Uses matched random assignment More sensitive than independent-groups designs Copyright © Allyn & Bacon (2007)

Within-Subjects Designs All participants are exposed to all experimental conditions Need to control for sequence effects The experience with one condition affecting performance in subsequent conditions Controlled by varying the order of presentation (counterbalancing) Copyright © Allyn & Bacon (2007)

Target Search Study Hypothetical Study Within-subjects design Six participants; order counterbalanced Three conditions 10 distracters 15 distracters 20 distracters Design on next slide Data on the following slide Copyright © Allyn & Bacon (2007)

Within-Subjects Design Example Copyright © Allyn & Bacon (2007)

Target Search Study Conditions A (10) B (15) C (20) Participant Order 18.33 22.39 24.97 2 ACB 15.96 20.72 21.79 3 BAC 19.02 22.78 25.46 4 BCA 25.36 27.48 27.91 5 CAB 19.52 24.64 26.75 6 CBA 23.27 24.96 25.49 Mean Scores 20.24 23.83 25.40 Copyright © Allyn & Bacon (2007)

Graph of Results The graph shows how the mean search time increases as the number of distracter items increases Copyright © Allyn & Bacon (2007)

Statistical Analysis Appropriate Statistical Analyses Correlated t-test (for 2 groups only) Repeated measures ANOVA Order data so that each line represents one participant and each column represents one condition Note that the columns represent conditions, NOT the order of testing Copyright © Allyn & Bacon (2007)

ANOVA Summary Table Source df SS MS F p Between 2 83.69 41.85 32.25 <0.01 Subjects 5 95.85 19.17 Error 10 12.97 1.30 Copyright © Allyn & Bacon (2007)

Within-Subjects Strengths More sensitive to small group differences The variability due to individual differences is statistically eliminated Fewer participants are needed Each participant appears in each condition Instructions may take less time Participants were already instructed on the task in previous conditions Copyright © Allyn & Bacon (2007)

Within-Subjects Weaknesses Because participants experience all conditions, they may figure out the hypothesis (potential subject effects) Major issue is sequence effects Practice and carry-over effects Controlled by varying the order of presentation Counterbalancing Random order of presentation Latin square design Copyright © Allyn & Bacon (2007)

Matched-Subjects Designs Introduces correlation through matched random assignment Should match on “relevant” variables Variables that affect the dependent variable Variables that show considerable natural variation in the population sampled Copyright © Allyn & Bacon (2007)

Matching Participants Match participants in sets Set size is equal to the number of conditions Matching gets more difficult as: The number of matching variables increases Matching is done on continuous variables The number of conditions increase Once sets are matched, randomly assign participants in the set to the conditions Copyright © Allyn & Bacon (2007)

Statistical Analysis Analyze as if it were a within-subjects study Data from matched participants are organized as if the data came from a single participant Act as if the number of participants was equal to the actual number of participants divided by the number of conditions (e.g., for 40 participants and 4 conditions, tell the program that you had 10 participants and 4 conditions in a within-subjects design) Copyright © Allyn & Bacon (2007)

Strengths and Weaknesses Increased sensitivity to group differences No sequence effects Weaknesses Extra work of matching participants Participants without appropriate matches cannot be used in the study Copyright © Allyn & Bacon (2007)

Single-Subject Designs Extensions of within-subjects designs Single participant tested under all conditions, with the researcher actively manipulating the independent variable Variation on time-series designs, with repeated measurement of the dependent variable Copyright © Allyn & Bacon (2007)

History of these Designs Intensive studies of individuals was common before R. A. Fisher Fisher’s development of ANOVA changed the focus of psychology to comparing groups of individuals Skinner was one of the few psychologists who advocated the intensive study of individuals Copyright © Allyn & Bacon (2007)

Logic of these Designs Includes independent variable manipulation Expect dependent variable response Note that the response must occur shortly after the manipulation unless there is a theoretical reason to expect a delay Multiple measures before and after the manipulation to identify normal variation and rule out regression to the mean Copyright © Allyn & Bacon (2007)

Single-Subject Designs Basic Single-Subject design includes Baseline period A treatment phase A post-treatment evaluation period Copyright © Allyn & Bacon (2007)

Types of Designs ABA Reversal Design Multiple Baseline Design Single-Subject, Randomized, Time-Series Design Copyright © Allyn & Bacon (2007)

Reversal Design LOGIC PROCEDURES Apply, then remove, independent variable manipulation If change occurs at both application and removal, it suggests a causal connection PROCEDURES Baseline measures (Condition A) Treatment application (Condition B) Return to baseline (Condition A again) Additional reversals can be included Copyright © Allyn & Bacon (2007)

Reversal Design Example Reversal conditions Condition A: attending to self-stimulation Condition B: withdrawing attention when self-stimulation occurs Hypothetical results (next slide) suggests that attention does influence self-stimulation Ethics requires return to Condition B Copyright © Allyn & Bacon (2007)

Hypothetical Results Copyright © Allyn & Bacon (2007)

Published Example DeLeon et al. (1997) Copyright © Allyn & Bacon (2007)

Multiple Baseline Design LOGIC Show the effect of the independent variable on several dependent variables Use when Reversals are undesirable Behavioral changes would not reverse PROCEDURES Baseline Manipulation focused on first dependent variable Manipulation focused on second dependent variable …and so on Copyright © Allyn & Bacon (2007)

Multiple Baseline Design Copyright © Allyn & Bacon (2007)

Variations on Multiple Baseline Design Across Behaviors Testing effects on different behaviors Across Individuals Testing effects on different people Across Settings and Time Testing effects in different settings or at different times Copyright © Allyn & Bacon (2007)

Single-Subject, Randomized Time-Series Design LOGIC Repeated measures of the dependent variable interrupted by a randomly placed intervention If change occurs at the point of intervention, it suggests a causal connection PROCEDURES Select minimum baseline and follow-up periods Randomly select the point of intervention Compare pattern of scores before and after the intervention Copyright © Allyn & Bacon (2007)

Single-Subject, Randomized Time-Series Design Copyright © Allyn & Bacon (2007)

Replicating Single-Subject Experiments Direct replication Repeating study on the same target behavior Systematic replication Evaluate procedures across subjects, settings, and/or target behaviors Clinical replication Combining procedures into a treatment “package” Copyright © Allyn & Bacon (2007)

Summary Can introduce a correlation in two ways Within-subjects and matched-subjects designs These designs are more sensitive to small differences between groups The costs for the greater sensitivity are: Sequence effects (within-subjects design) Matching difficulties (matched-subjects design) Single-subject designs allow the experimental manipulation of variables Copyright © Allyn & Bacon (2007)