Abstract
This chapter provides an overview of data linkage for exploiting and combining information about the same entities across data sources. Data linkage can be deterministic (exact), where each matching variable needs to agree exactly to determine a correct match, or probabilistic, where users allow for errors in the matching variables and assign a probability of a correct match. Through classic decision theory, the chapter determines the set of matches (and non-matches) and provides a linked dataset for further analysis. The chapter also describes some recent advances in record linkage and concludes with some initial research on compensating for linkage errors in the analysis of linked data.
| Original language | English |
|---|---|
| Title of host publication | Data Driven Policy Impact Evaluation: How Microdata is Transforming Policy Design |
| Editors | N Crato, P Paruolo |
| Publisher | Springer Nature |
| Chapter | 4 |
| Pages | 47-65 |
| Number of pages | 38 |
| ISBN (Electronic) | 9783319784618 |
| DOIs | |
| Publication status | Published - 2018 |