Questions and Answers
What does Internal Validity refer to?
What is an example of factors influencing Internal Validity?
Age of participants, ability of participants, conditions under which the study is conducted, type of materials used.
What does Internal Validity mean?
The observed differences in the dependent variable are directly related to the independent variable and not due to some other unintended variable.
What is a misleading correlation in research?
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Why is the correlation between height and math scores misleading?
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Systematic investigations of threats to internal validity receive ample attention.
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Which of the following is not a threat to Internal Validity?
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What is participant characteristics threat?
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What is the mortality threat in research?
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What are researchers trying to achieve regarding mortality threat?
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What is location threat?
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What is the best method for controlling location threats?
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What is an instrumentation threat?
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How can researchers control instrumentation threat?
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What is testing threat?
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What is history threat?
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What is maturation threat?
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What is regression threat?
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What are special education studies subject to?
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How does regression threat manifest in special education?
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What can handle regression threat?
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What is Implementation threat?
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How can Implementation threat occur?
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Study Notes
Internal Validity Overview
- Internal validity ensures relationships between variables are clear and not influenced by extraneous factors.
- Observed differences in dependent variables must be attributed directly to independent variables.
Examples of Internal Validity
- Factors influencing internal validity include participant age, ability, study conditions, and materials used.
- Correlation between height and math test scores in children may be misleading due to age-related development.
Misleading Results
- Correlation observed (0.80) between height and math scores may misrepresent true relationships influenced by participant age.
Threats to Internal Validity
- Common threats include:
- Participant characteristics and selection bias
- Participant mortality (dropout rates)
- Location where data is collected
- Instrumentation changes affecting measurement
- Characteristics and biases of data collectors
- Testing effects from pretesting
- Historical events impacting performance
- Maturation effects due to time
- Attitudinal differences among subjects
- Regression effects in extreme performances
- Implementation inconsistencies
Participant Characteristics
- Selection bias arises when participants differ in ways relevant to the study variables.
Mortality Threat
- Refers to the loss of participants during a study, which is challenging to manage.
- Researchers should investigate reasons for dropout to ensure they don’t skew results.
- Evidence should support that lost participants are comparable to those remaining on key characteristics.
Location Threat
- The data collection or intervention environments may introduce unintended variables.
- Consistent locations for data collection help mitigate this threat.
Instrumentation Threat
- Changes in the measuring instruments or scoring can lead to invalid results.
- Also noted as "instrument decay."
Managing Instrumentation Threat
- Schedule data collection to minimize variability.
- Standardize scoring procedures across all measures.
Data Collector Characteristics Threat
- Variations in data collectors' attributes (e.g., gender, age) can influence data quality.
- Consistency among data collectors can prevent bias.
Data Collector Bias Threat
- Collectors can inadvertently distort results based on personal biases.
- Training and deliberate ignorance about study hypotheses help minimize this risk.
Testing Threat
- Improvement observed from pretest to posttest might not solely be from the intervention.
- Pretesting can influence subsequent performance, revealing studied areas.
History Threat
- External events experienced by participants during the study may affect outcomes.
Maturation Threat
- Changes over time can lead to results being misattributed to interventions rather than natural growth.
Participant Attitude Threat
- Participant perceptions and treatment nuances may impact results beyond the intervention's effects.
Regression Threat
- Risk arises when studying extreme scores as they tend to revert towards the mean in subsequent testing.
Addressing Regression Threat
- Utilizing equivalent control or comparison groups can help control for natural score regression.
Implementation Threat
- Inconsistent application of the intervention can alter study outcomes.
- Bias from individuals implementing different aspects of the intervention may skew results.
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Description
Explore key concepts of internal validity through this flashcard quiz. Each card provides a definition and examples to clarify the significance of establishing clear relationships between variables. Perfect for psychology students and researchers alike.