Single-Case Designs

AKA single-subject, within subject, intra-subject design Footnote on p. 163 Not because only one participant (although might sometimes) Because participant serves as his own control Each participant’s data are graphed and analyzed separately “Case” doesn’t mean case study Case study is usually a narrative report of intervention with a client and description of his behavior Sometimes data are provided, but no experimental control Single-case design strategies…

Baseline Collecting data on the target behavior without your IV May still have a treatment, but not your IV Sometimes the preferred item (used as reinforcer in tx) is delivered contingent upon on-task/attending behaviors Why collect baseline data? To compare it to and see effects of your intervention To determine the dimensions of the behavior: topography, duration, frequency, latency, magnitude Get data on common antecedents and consequences to help with designing an intervention for a problem behavior To get info on what the criteria for reinforcement should be

Notation: A = baseline B = IV #1 C = IV #2 D =

Cummings and Carr (2005)

How Long Do You Collect Data in Baseline? At least 3 data points to demonstrate a pattern of behavior is better (Gina Green says a minimum of 6!) Obtain a steady state (“get stability”) Little variability over time Steady state strategy: repeatedly exposing a participant to a given condition to eliminate or control extraneous influences on behavior Your goal is to show the natural characteristics of behavior so that the effect of treatment will be clear Stability is defined different ways – for a study you’re conducting, look at the lit in that area and use that Mastery criterion: x sessions at or above 90% correct Visual inspection: Certain # of data pts with no trend or trend in opp direction with little variability Statistical method: mean of last 3 data pts differs by less than x% from the mean of the preceding 3 data pts Collection of BL data will show any practice effects 4 Baseline Patterns…

Review… Trend Overall direction taken by the data path Trends can be increasing, decreasing, or no trend (flat) Draw a trend line with your eyes that represents the direction (up, down, flat) that leaves about half of data pts below it and half above it. Compare direction and how steep Level Value on y axis around which a set of data points converge Draw a straight horizontal line with your eyes that leaves approximately half of the data points above it and half below Variability Degree to which data points deviate from the overall trend

Stable Baseline No upward or downward trend Not much variability Allows you to clearly determine effects of your IV Any changes in trend, variability, or level that happen when you start your tx can be more reasonable attributed to the tx

Ascending Baseline Increasing trend Behavior was in the process of changing during baseline! If your goal was to increase behavior in tx… If you started treatment while behavior was increasing, could you tell if your tx had an effect? If your goal was to decrease behavior in tx… If you have to start treatment, you can – why?

Variable Baseline “Something” is having an effect on behavior If you start your tx, that “something” could be affecting behavior during your tx Try to figure out what’s producing the variability Control it If you can’t, demonstrate that variability is the natural state of the behavior – ex: stereotypy

Behavior is observed repeatedly across time  Shows that the sample of behavior being measured is representative of that student  Shows pattern of behavior over time  Why is it important to measure behavior repeatedly? Percentage of Intervals Repeated Measures BaselineTreatment

Prediction Anticipated outcome of future measurement Without the IV… Trend, level, and variability would be the same as it has been PREDICTION Baseline

Affirmation of the Consequent Logic used in single-case designs to demonstrate a functional relationship between IV and DV If the IV were not implemented, the behavior would not change When the IV is implemented, the behavior changes PREDICTION BaselineTreatment

Verification Demonstrating that prior baseline responding would have remain unchanged if the IV hadn’t be implemented Verifies your prediction Reduces the possibility that something besides your IV was responsible for the change in your DV! PREDICTION VERIFICATION PREDICTION Baseline Treatment

Replication Repeating IV manipulations conducted previously in the study and obtaining similar outcomes Same participant: Intrasubject direct replication Different participant in same study: Intersubject direct replication PREDICTION VERIFICATION PREDICTION REPLICATION PREDICTION Baseline Treatment

Types of Single-Case Designs A-B Reversal Alternating Treatments Multiple Baseline Changing Criterion

Effects of a new medication on outbursts Was the medication effective for Joe? “B” Design

A-B Design Baseline phase followed by a treatment phase An effect is demonstrated by showing that behavior changes from one phase to the next

A-B Design BaselineToken System Was the token system effective? Effects of a token system on number of correct math problems

A-B Design: Advantages and Disadvantages  Advantages: simple, brief, no reversal  Disadvantage - changes in behavior may be the result of something besides your treatment  No verification or replication to show that it was the IV that caused the change in the DV  Can’t demonstrate experimental control  Considered a “quasi-experimental design”  Main threats to internal validity  History  Maturation  Used clinically and with self-management projects

Watson and Sterling (2005)

A-B Design: Review Does the design allow us to see a change in DV (without regard to whether it was caused by the IV)? YES Does the design allow us to infer a functional relationship between IV and DV? NO What threats to internal validity (confounds) does the design control for? NONE