Questions and Answers
What is generalisation?
Does the IV represent the concept we intend?
A measure is externally valid if what?
It truly measures the hypothetical construct intended.
An experiment is externally valid if it is similar to what?
Phenomena in the real world.
High external validity means what negative thing?
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What are the two types of external validity?
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Explain population validity.
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Explain ecological validity.
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What are the threats to external validity?
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What are reactive effects of testing?
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When do reactive effects of experiment arrangements occur?
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Give examples of reactive effects.
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What are Hawthorn effects?
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What are novelty effects?
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What are experimenter effects?
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What are Rosenthal effects?
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What is selection-treatment interaction?
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What is multiple-treatment interference?
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How can external validity be improved?
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What is replication?
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What is replication with extension?
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What are threats to statistical validity?
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What are possible causes of type I errors?
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What is a type II error?
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Study Notes
Generalization and External Validity
- Generalization refers to the extent to which findings from an experiment can be applied to broader contexts or groups.
- A measure is considered externally valid if it accurately represents the hypothetical construct it intends to measure.
External Validity in Experimental Design
- An experiment is externally valid if it closely resembles phenomena observed in real-world situations.
- High external validity, often seen in field experiments, can lead to a lack of control over confounding variables.
Types of External Validity
- Population validity indicates how well results can be generalized from the experimental sample to the overall population.
- Ecological validity assesses the generalization of results from the specific environmental conditions of the experiment to other settings.
Threats to External Validity
- Four key threats compromise confidence in generalizing study results:
- Reactive effects of testing
- Reactive effects of experimental settings
- Selection-treatment interaction
- Multiple-treatment interference
Reactive Effects of Testing
- These effects occur when a pre-test influences respondents' sensitivity toward the treatment, skewing true results.
Experimental Arrangements and Their Effects
- Reactive effects from experimental arrangements arise when the study conditions compromise the replicability of results in non-experimental environments.
Examples of Reactive Effects
- Examples include:
- Hawthorn Effects: Behavior changes due to awareness of being observed during studies (e.g., changes in productivity based on observation).
- Novelty Effects: Increased motivation and performance due to the novelty of the experimental task.
- Experimenter Effects: Influences from experimenters that shape participants' responses towards hypotheses.
- Rosenthal Effects: Self-fulfilling prophecies where participants adjust behavior to align with expectations.
Selection-Treatment Interaction
- This interaction occurs when certain characteristics of study participants, chosen without random sampling, affect treatment outcomes (e.g., past experiences or personality traits).
Multiple-Treatment Interference
- Arises when participants receive multiple treatments, potentially influencing subsequent responses due to former exposures (includes sequence and carry-over effects).
Improving External Validity
- External validity can be enhanced through:
- Replication: Conducting additional studies in the same manner as the original.
- Replication with extension: Replicating findings in different settings, using different participants, or under varied conditions.
Statistical Validity Issues
- Threats to statistical validity include type I errors, which occur when the null hypothesis is incorrectly rejected (false positives).
- Type I errors can result from practices like extensive "fishing" (conducting multiple tests on the same dataset).
Type II Errors
- A type II error is the failure to reject the null hypothesis when it is actually false, indicating missed findings in research.
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Description
This quiz delves into the concept of external validity in experimental design, focusing on its significance, types, and threats. Understand how generalization and ecological factors play a crucial role in assessing the applicability of research findings across different contexts and populations.
