How can bias be reduced in evaluation?

The option that emphasizes employing random sampling, blinding, or using mixed-methods approaches effectively addresses the need to reduce bias in evaluation. Random sampling is crucial because it ensures that every individual in the population has an equal chance of being selected, which enhances the representativeness of the sample. This reduces the likelihood of selection bias, leading to more generalizable findings.

Blinding helps prevent bias by ensuring that both participants and evaluators are unaware of certain critical aspects of the study, such as group assignments. For instance, in a clinical trial, neither the participants nor the researchers know who is receiving the treatment versus a placebo, which mitigates the influence of expectations on outcomes.

Incorporating a mixed-methods approach, which combines both qualitative and quantitative data, allows for a comprehensive understanding of the evaluation context and can illuminate perspectives that numbers alone may miss. This holistic approach aids in triangulating data, thus strengthening the validity of the findings and reducing interpretative bias. Overall, these methods create a more robust framework for evaluation, minimizing the potential for bias significantly.