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How much reliable are the integrated 'live' data? A validation strategy proposal for the non-parametric micro
Riccardo D'Alberto1, Meri Raggi1
1Department of Statistical Sciences 'P. Fortunati', University of Bologna, Bologna (BO), Italy.
Journal of Applied Statistics
|June 16, 2022
Summary
Statistical Matching (SM) integrates data efficiently, but assessing its accuracy is challenging. This study introduces a validation strategy, showing that data integration quality depends more on variable variability than dataset size.
Area of Science:
- Statistics
- Data Science
- Official Statistics
Background:
- Integrating diverse data sources is crucial for efficiency in research and official statistics.
- Statistical Matching (SM) offers a non-parametric approach to merge datasets using observed information, potentially reducing costs and time.
- Current methods for assessing SM integration quality are not robust, and common practices like using the largest dataset as a donor may not be optimal.
Purpose of the Study:
- To propose and validate a new strategy for assessing the goodness of data integration using Statistical Matching.
- To investigate common practices in SM, such as the 'biggest dataset as donor' rule.
- To explore the performance of different SM techniques and distance functions in generating reliable synthetic datasets.
Main Methods:
- Developed a validation strategy leveraging pre- and post-integration variable relationships.
- Applied the strategy to evaluate the impact of dataset size ratios on integration quality.
- Compared various combinations of SM techniques and distance functions.
Main Results:
- The proposed validation strategy provides a robust assessment of data integration quality.
- The common practice of using the largest dataset as the donor is not always superior.
- Integration quality is more strongly influenced by the variability of matching variables than by the size ratio of the datasets.
Conclusions:
- A new validation strategy enhances the assessment of Statistical Matching.
- The effectiveness of SM is linked to matching variable variability, not solely dataset size.
- This research reframes best practices for data integration in statistical analysis.
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