1. Chapter 12 Single-Case Evaluation Designs
PowerPoint presentation developed by:
Sarah E. Bledsoe & Jennifer Manuel

2. Overview Introduction to Single-Case Evaluation Designs
Logic of Single-Case Designs
Single-Case Designs in Social Work Practice
Single-Case Designs as Part of Evidence-Based Practice
Measurement Issues
Data Gathering
Alternative Single-Case Designs
Data Analysis

3. Introduction to Single-Case Evaluation Designs
Single-Case Evaluation is a type of time-series design with individual cases or systems
By identifying stable trends through repeated measures internal validity is enhanced
A researcher is able to identify when change in the dependent variable occurs and whether or not that change coincides with change in the independent variable
A pattern of coincidence may be established that makes alternative explanations unlikely

4. Logic of Single-Case Designs
Control Phase:
Baseline (repeated measure of outcome) is obtained in a target population
Experimental Phase:
An intervention is introduced and repeated outcomes measures are continued
Control and Experimental Phase data are examined to identify coinciding shifts and trends in data to make inferences about the effectiveness of the intervention

5. Single-Case Designs in Social Work
Distinguishing feature is a sample size = 1
Chief limitation is dubious external validity
High internal validity makes single-case designs a useful tool for identifying promising interventions for testing in subsequent studies
Replication results in the accumulation of evidence to support generalizability
Advances scientific basis of an intervention
Useful in evaluating an agency or program

6. Use of Single-Case Designs as Part of Evidence-Based Practice
Rigorous method used in implementing the evidence-based practice process
Allows practitioner to monitor client progress
Obstacles:
Client crises may not allow practitioners to collect sufficient baseline data
Heavy case loads increase difficulty of collecting repeated measures
Peers and supervisors may not recognize the value of single-case research
Clients may resent extensive monitoring

7. Measurement Issues Operationally defining target population and goals
Choosing what to measure
The use of triangulation of measurement

8. Operationally Defining Target Populations and Goals
Identification of the specific indicators of the target problem
May necessitate the selection and observation of indirect indicators of the target problem (i.e., symptoms in the target problem of depression)
Negative and positive indicators can be observed
Goal should include the direction of change in indicators:
To increase positive indicators and/or decrease negative indicators

9. What to Measure
Operational indicators should occur frequently enough to be measured on a regular basis
Infrequent events are not useful outcomes in single-case designs

10. Triangulation
Use of more than one imperfect data-collection alternative in which each option is vulnerable to different potential sources of error
For example, instead of relying only on a client’s self-report of a particular target behavior, a significant other (teacher, cottage parent, and so on) is asked to monitor the behavior as well
Maximizes the chance that hypothesized changes in the dependent variable will be detected

11. Data Gathering Potential Data Sources Available records Interviews
Self-report scales
Direct observation of behavior

12. Who Should Measure? Practitioner Clients Significant others
Risk of observer bias
Clients
Risk of bias to please themselves, appear socially desirable, or avoid disappointing the practitioner
Significant others
Neither objectivity nor commitment to the study is guaranteed
Triangulation provides the opportunity for assessing measurement reliability

13. Sources of Data Available records: Self report scales:
May be useful in obtaining a retrospective baseline
Reliability of available records may be questionable
Self report scales:
Convenient, brief, ensure uniformity
Clients may lose interest in repeating scales
Social desirability bias

14. Reliability and Validity
Affected by differences in testing of instruments and use of instruments in single-case designs:
In validity and reliability studies:
respondent is anonymous with no special, ongoing relationship with the researcher
instrument is not completed more than 1-2 times
score has no bearing on whether respondent is benefiting from services
In single-case experiments:
respondent is not anonymous and has a special relationship with service provider
answers may become less valid with repetition
differences between baseline and intervention phase may be apparent

15. Direct Behavioral Observation
Time may be an obstacle for busy practitioners
Resources for an additional observer may be unavailable
Many target problems need to be observed outside of visits with the practitioner
Client observation (self-monitoring)
Vulnerable to measurement bias and research reactivity

16. Unobtrusive vs. Obtrusive Observation
Unobtrusive observation:
Participant does not notice observation and is less influenced to behave in socially desirable ways and ways that meet experimenter expectancies
Obtrusive observation:
Participant is keenly aware of being observed and may be predisposed to behave in socially desirable ways and ways that meet experimenter expectancies

17. Data Quantification Procedures
Direct observation:
Quantified in terms of frequency, duration, or magnitude
Interval recording
Spot-check recording

18. The Baseline Phase
Internal validity is enhanced when baseline has enough measurement points to show a stable trend and to establish the unlikelihood that extraneous events will coincide with the onset of intervention
5 to 10 measurements should be planned
Severity of client problem may interfere

19. Alternative Single-Case Designs
AB: The basic single-case design
ABAB: Withdrawal/reversal design
Multiple-baseline design
Multiple-component design

20. AB: The Basic Single-Case Design

21. ABAB: Withdrawal/Reversal Design

22. Multiple-Baseline Designs

23. Multiple-Component Designs

24. Data Analysis 3 questions should be asked:
Is there a visual pattern that depicts a series of coincidences in which the frequency, level or trend of the target problem changes only after the intervention is introduced or withdrawn?
What is the statistical probability that the data observed during the intervention phase(s) are part of the normal, chance fluctuations in the target problem?
If change in the target problem is associated with the tested intervention, is the amount of change important from a substantive, or clinical, standpoint?