measuresduplicates

Measuringduplicates, as a term coined for this article, refers to instances where the same measure appears more than once in a dataset or where multiple measures map to the same underlying concept. It can describe duplicate observations of a measurement for a single unit of analysis or redundant entries in a catalog of measures used for analysis and reporting.

In data collection, measuresduplicates can arise from integrating data from multiple sources, recording the same concept under different identifiers, or insufficient data governance. In metadata repositories and measure catalogs, measuresduplicates occur when distinct identifiers point to the same variable or when different measures assess the same construct, leading to fragmentation and inconsistency.

The presence of measuresduplicates can distort analyses by inflating sample sizes, biasing summary statistics, and complicating modeling efforts. They also hinder data governance, reproducibility, and cross-study comparisons, reducing the reliability of insights drawn from the data.

Detection and remediation involve data cleaning and governance processes. Practical steps include standardizing measure names and units, establishing canonical identifiers, and applying deduplication rules. Techniques include similarity matching on names, descriptions, and metadata; cross-referencing data dictionaries; and consulting domain experts. Once identified, duplicates can be merged under a single authoritative measure, or retired with a clear mapping to a preferred identifier. Documentation of changes and updates to downstream datasets and analyses is essential to maintain traceability and reproducibility.