single designs

SINGLE-N DESIGNS

Inferring Causality

  • Covariation

    • Definition: The relationship between changes in independent and dependent variables.

  • Temporal precedence

    • Definition: The independent variable occurs in time before the dependent variable does.

  • Eliminate spuriousness

    • Definition: Changes in the dependent variable are only due to the independent variable.

    • Indicates that random variables affect all groups equally.

  • Inferential statistics

    • Purpose: Estimate the effect of random variables on the outcome.

Single-n Design

  • Case study

    • Characteristics:

    • No random assignment.

    • No inferential statistics utilized.

  • Essential elements:

    • Covariation

    • Temporal precedence

    • Elimination of spuriousness

    • Keep non-manipulation factors constant during the study.

A-B Design

  • Overview

    • Described as the most basic single-n design.

  • Stable measure of dependent variable

    • Requirement: Presence of a stable baseline to compare against.

  • Post-treatment dependent measure

    • Structure: A (manipulation) → B (dependent variable).

Battling Spuriousness

  • Methods to eliminate random error

    • Keep non-treatment variables constant to minimize interference with results.

  • Potential sources of error

    • Between-subjects variability

    • Not typically an issue in single-n designs.

    • Within-subjects variability

    • Stable baseline and a controlled, stable environment are crucial for consistency.

Baseline Stability

  • Key components

    • Dependent Variable (DV) manipulation must occur under stable conditions to ensure accurate results.

A-B Design Limitations

  • Stable baseline requirement

    • The design requires a control of all factors contributing to random error which can be challenging to identify.

  • Within-subject design effects

    • Common effects impacting results may include:

    • Practice effect: Improvement or changes due to repeated performance.

    • Fatigue effect: Diminished performance due to tiredness over time.

    • Sensitization effect: Changed responses due to increased awareness of the manipulations.

  • Maturation effects

    • Natural biological or psychological changes over time impacting the dependent variable during the study.

  • Testing effects

    • Influences on subjects due to the act of being tested.

A-B-A Design

  • Reversal design

    • Structure: Baseline (A) → Treatment (B) → Baseline (A).

  • Treatment effect

    • Returning to a baseline state after removing the manipulation indicates an established effect of treatment on DV.

A-B-A Design Limitations

  • Cyclical maturation

    • Proposed A-B-A-B-A design addresses continuous changes but complicates results.

  • Carryover effects

    • Effects of the manipulation may linger post-removal impacting the baseline conditions (e.g., anxiety manipulation).

Multiple-Baseline Design

  • Implementation

    • Measure baselines for several key behaviors.

    • Important principle: Only the behavior targeted by manipulation should be affected, ensuring clarity of effect.

Validity

  • Types of Validity

    • Internal validity

    • Ensured by keeping non-manipulation variables constant across conditions.

    • Construct validity

    • The accuracy in measuring the constructs being studied.

    • External validity

    • Importance of designs that utilize homogenous populations and account for individual variability.

    • Emphasizes the necessity for replication to establish broader applicability.