Which process improves the reliability of data collected in research?

The process that improves the reliability of data collected in research is editing. Editing involves reviewing and correcting data entries to ensure accuracy and consistency. This process can help identify and rectify errors, such as typos, missing values, or inconsistencies in coding. By enhancing the quality of the data, editing directly contributes to the reliability of the findings drawn from that data. Reliable data is crucial for decision-making since it ensures that the conclusions drawn reflect true observations rather than artifacts caused by data collection errors.

In contrast, the other processes listed do not serve the primary purpose of enhancing data reliability. Aliasing typically refers to issues in signal processing and pattern recognition where multiple signals may be indistinguishable, which does not apply here. Stemming is a textual analysis process related to linguistics and natural language processing, where words are reduced to their root form and is not relevant to data reliability. Excluding data may remove anomalies but can potentially lead to biased results, thereby undermining the reliability of the remaining data. Thus, editing stands out as the pivotal process for ensuring high data reliability in research contexts.