In quantitative research, what is control used to minimize or prevent?

Control in quantitative research is primarily used to minimize or prevent bias. Bias refers to any systematic error that can affect the validity of the research findings. It can arise from various sources, including selection bias, measurement bias, and observer bias. By implementing control measures, researchers aim to ensure that external variables do not unduly influence the outcomes of their study, thereby strengthening the reliability of the results.

Techniques such as randomization, blinding, and using control groups are common methods to exert control over potential confounding factors. These methods help create a balanced comparison between different groups in the study, ensuring that any observed effects can be attributed more confidently to the variables being studied rather than to other influences.

While duplication, homogeneity, and randomization are important concepts in research, they do not directly address the need to minimize bias in the same way that control does. For instance, duplication refers to the need for repeat studies to verify results, homogeneity relates to the similarities among study subjects, and randomization is actually a method used to achieve control rather than a separate concern. Therefore, the focus of control on minimizing bias makes it a crucial aspect of ensuring robust and credible quantitative research.