Which of the following dimensions of data quality ensures values are within an expected range?

The dimension of data quality that ensures values are within an expected range is validity, which falls under the category of integrity/validity. This aspect focuses on whether data values conform to specific rules or business requirements, ensuring that they make sense in the context they are used.

For instance, a dataset containing age values should only have entries that are non-negative and typically within a realistic range for human ages, such as 0 to 120. If a data entry specifies an age of -5 or 150, it would fail the validity check, signaling that the data does not meet the necessary standards for correctness and reliability.

In contrast, other dimensions such as accuracy pertain to how closely data reflects the real-world scenario it represents, completeness examines whether all necessary data is present, and timeliness refers to how current the data is. Each dimension plays a crucial role in overall data quality, but it is validity that specifically addresses the adherence of data values to expected ranges and formats.