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Once data is secured, researchers should review the raw data for completeness and accuracy. This involves looking for illegible or missing responses, as well as obvious misunderstandings or suspicious entries (e.g., a response of $$12$$ on a $$1$$-to-$$10$$ rating scale). Identifying these issues early is crucial for determining if a participant's data is usable for statistical analysis.

Checking Raw Data

During the data checking process, a researcher may find that a participant's responses are missing key information or are highly suspicious. In such cases, the researcher must decide whether to exclude that participant's data from the analysis. Excluded data should never be deleted or thrown away; instead, it should be set aside with detailed notes explaining the reason for exclusion, as this decision must be reported in the final research write-up.

Excluding Participant Data

A researcher has just finished collecting survey data from 200 participants who rated their stress levels on a 1-to-10 scale. Before running any statistical analyses, the researcher reviews the raw dataset and notices that one participant recorded a value of 15. What does this example best illustrate?

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