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Single-Case Research: Standards for Design and Analysis Thomas R. Kratochwill University of Wisconsin-Madison. The SCD Standards Panel. Tom Kratochwill, Chair John Hitchcock Rob Horner Sam Odom David Rindskopf Will Shadish Joel Levin, Consultant. Three Defining Features of a SCD.

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Single-Case Research: Standards forDesign and AnalysisThomas R. KratochwillUniversity of Wisconsin-Madison


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The SCD Standards Panel

Tom Kratochwill, Chair

John Hitchcock

Rob Horner

Sam Odom

David Rindskopf

Will Shadish

Joel Levin, Consultant


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Three Defining Features of a SCD

An individual “case” is the unit of intervention administration and data analysis. A case may be a single participant or a cluster of participants (e.g., a classroom or community).

Within the design, the case provides its own control for purposes of comparison. For example, the case’s series of outcome variables prior to the intervention is compared with the series of outcome variables during (and after) the intervention.

The outcome variable is measured repeatedly within and across different conditions or levels of the independent variable. These different conditions are referred to as “phases” (e.g., baseline phase, intervention phase).




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SCD Standards are designed to address threats to Internal Validity

Ambiguous Temporal Precedence

Selection

History

Maturation

Testing

Instrumentation

Additive and Interactive Effects of Threats


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Types of questions a SCD might answer: Validity

Overarching: Which intervention is effective for this case?

Is this intervention more effective than the current “baseline” or “treatment” as “usual” condition? (e.g., does Intervention A reduce problem behavior for this case?)

Does adding B to Intervention A further reduce problem behavior for this case?

Is Intervention B or Intervention C more effective in reducing problem behavior for this case?


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Design and Evidence Standards Structure Validity

Evaluate the Design

Meets Evidence Standards

Meets Evidence Standards with Reservations

Does Not Meet Evidence Standards

Conduct Visual Analysis for Each Outcome Variable

Strong Evidence

Moderate Evidence

No Evidence

Effect-Size Estimation


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Criteria for Single-Case Designs that Meet Evidence Standards

Independent variable must be systematically manipulated

The outcome variable must be measured systematically

The study must include at least three attempts to demonstrate an intervention effect (replication)

The phase should typically include a minimum of five data points


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Independent Variable Must be Systematically Manipulated Standards

The researcher determines when and how the independent variable conditions change


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The Outcome Variable Must be Measured Systematically Standards

Measurement occurs over time

Inter-observer agreement is reported

Inter-observer agreement must be assessed on each outcome variable in every phase and there should be measurement for at least 20% of the sessions distributed across all conditions of the study


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The Study Must Include at Least Three Attempts to Demonstrate an Intervention Effect

Designs that generally meet this standard include:

ABAB Design

Multiple Baseline Design

Alternating Intervention Design

Designs not meeting this standard include:

AB Design

ABA Design

BAB Design


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Importance of Replication Demonstrate an Intervention Effect

ABAB Design

Source: Horner & Spaulding, in press


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Multiple Baseline Demonstrate an Intervention Effect

Design

Source: Horner & Spaulding, in press


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The Phase Should Typically Include a Minimum of Five Data Points

Exceptions: If an ABAB or Multiple Baseline Design study has fewer than three or four data points in any one phase used to demonstrate an effect, the study may Meet Evidence Standards with Reservations


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Further Exceptions to the Five Data Points Criterion Points

Alternating Treatment Design

Randomized Designs

Brief Functional Assessment


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Visual Analysis of Single-Case Designs Points

Evidence Standards Met Through Visual Analysis of Single-Case Research Data Displays

WWC Reviewers Trained in Visual Analysis of Data in Single-Case Design


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Single-Case Design Visual Analysis: Training Goals Points

  • Define Six Variables used in visual analysis, and build fluency in applying those variables with attention to both main and interaction effects.

  • Provide a Four-Step Framework for analysis of single-case designs

    • Visual analysis

    • Statistical analysis

  • Apply visual analysis to ABAB, Multiple Baseline Designs, and AlternatingTreatment designs.


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    Visual Analysis within PointsSingle-case design

    • Documenting Experimental Control

      • Three demonstrations of an “effect” at three different points in time.

        • A “basic effect” is a change in the dependent variable when the independent variable is actively manipulated.

      • To assess an “effect” Visual Analysis includes simultaneous assessment of:

        • Level, Trend, Variability, Immediacy of Effect, Overlap across Adjacent Phases, Consistency of Data Pattern in Similar Phases.

          • (Parsonson & Baer, 1978, 1992; Kratochwill & Levin, 1992)


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    Visual Analysis Points

    • Interpreting experimental control always involves assessment of data from the whole study(all phases), not just assessment of two adjacent phases.

      • Assessment of a “basic effect”is done with adjacent phases.

      • Assessment of experimental control, however, requires evaluation of all data in all phases.


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    Four Steps/ Six Variables Points

    • Do Baseline data document a predictable pattern?

    • Do data within each phase allow documentation of a predictable pattern?

    • Do data between phases document basic effects?

    • Do data across phases document experimental control?

    • Level

    • Trend

    • Variability

    • Overlap

    • Immediacy of effect

    • Consistency across similar phases

    Four Steps in Analysis

    Six Variables for Consideration


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    Multiple Baseline Design: PointsA 7th consideration

    • Level

    • Trend

    • Variability

    • Overlap

    • Immediacy of Effect

    • Consistency across similar phases

    • Stability in non-intervened series when effect demonstrated in one series


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    Alternating Treatment/ PointsMulti-element designs

    • Magnitude of separation

      • Greater the difference between two conditions, larger the demonstration of a functional relation

  • Consistency of separation

    • Greater consistency of separation between two conditions (no overlap) larger the demonstration of a functional relation

  • Number of data points used to establish separation

    • The more points documenting separation to larger the demonstration of a functional relation.


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    Visual Analysis: Traditions of Relying on Visual Inspection of Single-Case Design Data

    • The Tradition of Applied Behavior Analysis

    • Lack of Consensus Surrounding the Statistical Analysis of Single-Case Research Design

    • Use of Visual Analysis in Single-Case Design in Practice Settings


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    Methods to Improve Visual Analysis of Single-Case Data of Single-Case Design Data

    • Structured Training in Visual Analysis (e.g., compare visual analysis of novices to experts)

    • Use Visual Analysis Protocol that Includes a Component Analysis and Judgmental Aids (e.g., Tawney & Gast, 1984)

    • Use Visual Analysis Criteria (e.g., Dual Criterion Method and Conservative Dual Criterion Method; Fisher, Kelly & Lomas, 2003; Swaboda, Kratochwill, & Levin, 2009)


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    Methods to Improve Visual Analysis of Single-Case Data (Cont.)

    • Use of Randomization in Design [Response-Guided versus Non-Response-Guided Experimentation (e.g., Ferron & Jones, 2006; Todman & Dugard, 1999, 2001)]

    • Blind Visual Analysis Procedures from a “Data Analyst” (Ferron & Jones, 2006)

    • Use Both Visual and Statistical Analysis (e.g., Borckhardt, Nash, Murphy, Moore, Shaw, & Oneil, 2008; Brossart, Parker, Olson, & Mahadevan, 2006; Ferron & Jones, 2006;..among others)


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    Issues the Panel did NOT Address or Obtain Closure (Cont.)

    • Randomization applied to Single-Case Design Structure

    • Statistical Analysis of Single-Case Design to Determine Statistical Significance (e.g., randomization tests, time-series analysis, HLM )

    • Single-Case Design Effect Size Determination

    • Meta-Analysis (Single-Case Design Studies or Combined with Group Design Research)


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    Acknowledgements (Cont.)

    • Special appreciation to Rob Horner for his contributions to the visual analysis training slides.


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    Contact Information (Cont.)

    Thomas R. Kratochwill, PhD

    Educational and Psychological Training Center

    1025 West Johnson Street

    University of Wisconsin-Madison

    Madison, Wisconsin 53706

    E-Mail: [email protected]


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