Project

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Underpinnings of Scientific Research

KEY CONTEXT: Why do research?

Experimentation is needed to examine specifically why change has occurred

Rule out extraneous factors that might explain the results.

This section will discuss:

The purpose of experimentation

The types of factors that needed to be ruled out

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Underpinnings of Scientific Research

Internal validity: the extent to which the experiment rules out alternative explanations of the results

Threats to internal validity;

History

Maturation

Instrumentation

Testing

Statistical regression

Diffusion of Treatment

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Underpinnings of Scientific Research

External Validity: the extent to which the results of an experiment can be generalized or extended beyond the conditions of the experiment

Threats to external validity:

Generality across-

Subjects

Responses or measures

Settings

Time

Behavior-change agents

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Underpinnings of Scientific Research

Threats to external validity:

Reactive experimental arrangements

Reactive assessment

Multiple treatment interference

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Underpinnings of Scientific Research

CONSTRUCT VALIDITY

What is the intervention and why did it produce the effect?

Interprets the relation between the intervention (independent variable) and the behavior change (dependent variable)

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Underpinnings of Scientific Research

Threats to Construct Validity

Attention and contact accorded to client

Special stimulus conditions, settings and contexts

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Underpinnings of Scientific Research

DATA EVALUATION VALIDITY: Aspects of the data that can interfere with drawing valid inferences

Excessive variability in the data

Unreliability of the measures

Trends in the data

Insufficient data

Mixed data patterns

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Underpinnings of Scientific Research

Excessive variability in the data

Uncontrolled and changing influences in setting

Unreliability in measurement

Inconsistent

Implementation of intervention

Inconsistency in performance

Differences among subjects

Cycles or abrupt changes on the individual or environment

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Underpinnings of Scientific Research

DATA EVALUATION VALIDITY: Aspects of the data that can interfere with drawing valid inferences

Unreliability of the measures

Trends in the data

Insufficient data

Mixed data patterns

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Underpinnings of Scientific Research

PRIORITIES AND TRADE-OFFS IN VALIDITY

Impossible to design a study that prevents all threats to validity

KEY ISSUES:

Highest priority is internal validity

Trade offs

Control of laboratory

Generalized significance in applied setting

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The following modules will discuss how different single subject designs address there threats

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Created by: Kathleen Dyer, CCC-SLP, BCBA-D, LABA Adjunct Faculty, ABA Programs