23. Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set? Choose 2 answers.
Answer: B,D
Data elements must be unique and the data must be relevant.
A researcher must ensure that the data elements in the analysis are unique and that the data is relevant to the research question to maintain data quality in a clean data set.
A) The data cannot contain outliers.
While outliers can impact the results of an analysis, their presence does not inherently disqualify a data set from being considered clean. It is important to evaluate the context of outliers, as they may carry significant information; therefore, this option is not a necessary characteristic regarding data quality.
B) The data elements must be unique.
Unique data elements ensure that each entry in the dataset represents a distinct observation, which is crucial for accurate analysis. Duplicate entries can skew results and lead to incorrect conclusions, making uniqueness an essential characteristic to ensure data quality.
C) The age of the data does not matter if the data are complete.
This option is incorrect as the age of the data is often critical in determining its relevance and applicability to current research questions. Outdated data can lead to misleading results, thus making it an important consideration in data quality.
D) The data must be relevant.
Relevance is a fundamental characteristic of data quality because data that does not pertain to the research question can result in irrelevant or invalid conclusions. Ensuring that the data is relevant directly impacts the integrity and usefulness of the analysis.
Conclusion
In summary, ensuring that data elements are unique and relevant is vital for maintaining data quality in research analysis. These characteristics help prevent misleading results and uphold the integrity of the research, while the other options fail to meet the necessary standards for a clean data set.