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Discuss the threats of internal validity in an experimental research design?
joyline yegon
more than 4 weeks ago
Discuss the threats of internal validity in an experimental research design?
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more than 4 weeks ago
Internal validity refers to the extent to which a study accurately measures the relationship between the independent variable and the dependent variable. Threats to internal validity are factors that can affect the accuracy of this relationship, leading to potential confounding variables and inaccurate conclusions. Some common threats to internal validity in experimental research designs include:
1. History: Events that occur during the course of the study that could affect the outcome. For example, if a study is conducted over a long period of time, external events or changes in the environment could impact the results.
2. Maturation: Changes in participants over time that are not due to the experimental manipulation. For example, participants may naturally improve or decline in performance over time, which could affect the results of the study.
3. Testing effects: The act of being tested or measured multiple times can influence participants' responses. For example, participants may become more familiar with the task or more fatigued as they complete multiple trials, leading to changes in their performance.
4. Instrumentation: Changes in the way the dependent variable is measured over time. For example, if different raters are used to assess participants' performance at different time points, this could introduce bias and affect the results.
5. Selection bias: Differences in the characteristics of participants in different groups that are not due to the experimental manipulation. For example, if participants in one group are more motivated or have higher baseline levels of the dependent variable, this could confound the results.
6. Attrition: Participants dropping out of the study before it is completed. If participants who drop out are systematically different from those who remain in the study, this could bias the results.
7. Regression to the mean: Extreme scores on a measure are likely to be less extreme on a subsequent measurement. This can lead to the appearance of an effect when none actually exists.
To minimize these threats to internal validity, researchers can use random assignment, control groups, counterbalancing, and other experimental design techniques. Additionally, researchers should carefully monitor and document any potential sources of bias or confounding variables throughout the study.
1. History: Events that occur during the course of the study that could affect the outcome. For example, if a study is conducted over a long period of time, external events or changes in the environment could impact the results.
2. Maturation: Changes in participants over time that are not due to the experimental manipulation. For example, participants may naturally improve or decline in performance over time, which could affect the results of the study.
3. Testing effects: The act of being tested or measured multiple times can influence participants' responses. For example, participants may become more familiar with the task or more fatigued as they complete multiple trials, leading to changes in their performance.
4. Instrumentation: Changes in the way the dependent variable is measured over time. For example, if different raters are used to assess participants' performance at different time points, this could introduce bias and affect the results.
5. Selection bias: Differences in the characteristics of participants in different groups that are not due to the experimental manipulation. For example, if participants in one group are more motivated or have higher baseline levels of the dependent variable, this could confound the results.
6. Attrition: Participants dropping out of the study before it is completed. If participants who drop out are systematically different from those who remain in the study, this could bias the results.
7. Regression to the mean: Extreme scores on a measure are likely to be less extreme on a subsequent measurement. This can lead to the appearance of an effect when none actually exists.
To minimize these threats to internal validity, researchers can use random assignment, control groups, counterbalancing, and other experimental design techniques. Additionally, researchers should carefully monitor and document any potential sources of bias or confounding variables throughout the study.
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