Internal validity
Internal validity is a measure of the accuracy of a study's results, specifically whether the effects observed in the study are due to the manipulation of the independent variable and not some other factor. It is one of the most important properties of scientific studies.
Definition[edit | edit source]
Internal validity refers to the degree to which a study establishes a trustworthy cause-and-effect relationship between a treatment and an outcome. It also reflects that a given study makes it possible to eliminate alternative explanations for a finding. For example, if a study is conducted to determine whether a certain diet causes weight loss, the study has high internal validity if the researchers can confidently say that the weight loss was caused by the diet and not other factors like exercise or a concurrent medical treatment.
Factors affecting internal validity[edit | edit source]
Several factors can affect the internal validity of a study, including:
- Confounding variables: These are variables that the researcher failed to control, or eliminate, damaging the internal validity of an experiment.
- Selection bias: This occurs when participants are not randomly selected, and some participants are more likely to be selected for the study than others.
- History: This refers to the specific events occurring between the first and second measurements in addition to the experimental variables.
- Maturation: This refers to processes within the participants as a function of the passage of time.
Improving internal validity[edit | edit source]
Researchers can improve the internal validity of a study by:
- Randomization: This is the use of chance procedures in psychological experiments as it allows researchers to have full control over the variables in the study.
- Control groups: This allows researchers to compare the group receiving the treatment with another group not receiving the treatment.
- Blinding: This is used to prevent bias in research. Blinding can be applied to those conducting the research, the participants, or the statisticians analyzing the results.
See also[edit | edit source]
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