door.donau-uni.ac.at o:5684 DOI 10.48341/g31s-vq79-04 What are good quality data on a phenomenon that is hard to measure? Key points • Irregular migration is difficult to measure, and the data that exist are often limited, inconsistent, or outdated. This chapter introduces a practical framework to help users assess the quality and credibility of such data, rather than taking estimates at face value. • It distinguishes between key data types—stocks vs flows, estimates vs indicators—and highlights how conceptual ambiguity, observational gaps, and poor documentation can undermine how irregular migration data are interpreted and used. • When applied to over 250 estimates across 14 countries, the framework reveals significant variation in quality. While some countries produce relatively robust and transparent figures, many rely on outdated, methodically weak or poorly documented estimates. Still, pockets of good practice exist across North America and Europe, which can be built on. • The chapter argues that responsible use of irregular migration data depends not only on improving data systems, but also on the ability of users to critically assess what data mean, how they were produced, and whether they are fit for purpose. eng Data quality Irregular migration measurement uncertainty indicators estimates critical data assessment FAIR data principles 2025-09-30T07:32:03.736Z LOMv1.0 Author BEGIN:VCARD VERSION:3.0 N:Kierans;Denis; FN:Denis Kierans X-ORCID:https://orcid.org/0000-0003-2354-7861 END:VCARD BEGIN:VCARD VERSION:3.0 N:Siruno;Lalaine; FN:Lalaine Siruno X-ORCID:https://orcid.org/0000-0002-4404-529X END:VCARD https://w3id.org/kim/hcrt/scheme https://w3id.org/kim/hcrt/text Textdokument Text LOMv1.0 yes https://creativecommons.org/licenses/by-nc/4.0 application/pdf 1343128 https://door.donau-uni.ac.at/o:5684 https://door.donau-uni.ac.at/api/object/o:5684/thumbnail LOMv1.0 discipline