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.
The data elements must be unique and the data must be relevant.
For a data set to be considered clean and suitable for analysis, it is essential that the data elements are unique to avoid redundancy and that the data is relevant to the research questions being addressed. Uniqueness helps maintain the integrity of the data set, while relevance ensures that the analysis yields meaningful insights.
While outliers can indeed affect the quality of an analysis, their presence does not automatically disqualify a data set from being considered clean. In some cases, outliers may represent legitimate variations in the data that are important to the analysis. Therefore, the absence of outliers is not a definitive characteristic of data quality.
Each element in a clean data set should be unique to prevent duplication, which can skew analysis results. Unique data entries ensure that each observation contributes distinct information, allowing for accurate conclusions to be drawn from the data set.
The age of the data is a critical factor in assessing data quality. Even if a data set is complete, outdated information can lead to irrelevant or misleading conclusions. Thus, the statement that age does not matter is incorrect, as the timeliness of data is essential for relevancy.
Data relevance is crucial in ensuring that the information being analyzed directly pertains to the research questions. Irrelevant data can dilute the analysis and lead to inaccurate or unhelpful findings, making it necessary for research to focus on pertinent data points.
In summary, ensuring data quality involves confirming that the data elements are unique and relevant to the analysis being conducted. These characteristics are essential for maintaining the integrity and applicability of the data set. Other factors, such as the presence of outliers or the age of the data, can influence quality but do not serve as definitive criteria for a clean data set in the same way that uniqueness and relevance do.
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