Quantitative methodological approach are several research designs
Internal vs. External Validity and Research Design
Internal Validity and External Validity are two crucial concepts in research design, particularly quantitative research. They assess different aspects of a study's trustworthiness:
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Internal Validity: Refers to the degree of confidence that the observed relationship between variables is causal. In other words, it measures whether the independent variable truly caused the change in the dependent variable, and not some other factor. Threats to internal validity can lead to misleading conclusions about cause and effect.
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External Validity: Refers to the generalizability of the study's findings. It indicates whether the results can be applied to a broader population or setting beyond the specific participants and context of the study.
Here are two examples of common internal validity threats:
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History: Events that occur between the pretest and posttest (in studies with this design) that could influence the dependent variable. For example, if studying the effectiveness of a new exercise program, participants in both groups might coincidentally join a gym during the study period, affecting their overall fitness.
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Selection Bias: Systematic differences between the groups being compared, which could explain the observed results. For example, if studying the effects of meditation on anxiety, participants who volunteer for the meditation group might be inherently less anxious than those assigned to the control group.
No single research design can completely eliminate all threats to internal validity. However, some designs offer greater control than others.
The design that comes closest to controlling for all internal validity threats is the Randomized Controlled Trial (RCT). Here's how RCTs address the threats mentioned above:
- History: RCTs randomly assign participants to control and experimental groups. This randomization helps ensure that any events occurring during the study are likely to affect both groups equally, minimizing their impact on the observed results.
- Selection Bias: Random assignment helps to control for pre-existing differences between groups. Since participants are randomly assigned, any systematic biases are less likely to influence the outcome.
It's important to note that even RCTs have limitations. For example, they might not be feasible in all situations due to ethical considerations or practical constraints. However, they represent the gold standard for establishing causal relationships and offer the strongest internal validity among quantitative research designs.