Difficulty: Medium

Average Score: 69%

Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set?

Rationale

The data must be relevant and the data elements must be unique.

Relevance ensures that the data pertains to the research questions being investigated, while uniqueness prevents duplication and ambiguity in data analysis, both of which are critical for maintaining data quality and integrity.

A (The data must be relevant.) CORRECT

Relevance is essential because data that does not pertain to the specific research question can lead to misleading conclusions. Ensuring that the data is relevant helps maintain the focus of the analysis and increases the likelihood of deriving valid insights.

B (The age of the data does not matter if the data are complete.) YOUR ANSWER

While completeness is important, the age of the data can significantly impact its relevance and applicability. Outdated data may not accurately represent current conditions or trends, potentially skewing analysis results. Thus, the age of the data cannot be overlooked in assessing quality.

C (The data cannot contain outliers.) YOUR ANSWER

While outliers can affect data analysis, their presence does not inherently determine data quality. In some cases, outliers can provide valuable insights or indicate important phenomena. Therefore, the absence of outliers is not a necessary characteristic for ensuring data quality.

D (The data elements must be unique.) CORRECT

Uniqueness is crucial to prevent duplicates that can distort analysis results. When data elements are unique, they enhance the reliability of the findings and ensure that each observation contributes distinctly to the analysis, thereby improving overall data quality.

Conclusion

For a researcher aiming to ensure data quality, considering both the relevance of the data and the uniqueness of its elements is vital. These characteristics help ensure that the analysis is based on pertinent and distinct data points, thus fostering accurate and reliable research outcomes. Neglecting factors like data age or the presence of outliers could compromise the integrity of the analysis.

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