*5.3. Data Analysis*

We converted both final selections to Atlas.ti, a computer program for data analysis in qualitative research that helps to structure the process of coding and analysis. This means that we have two Atlas.ti files (one for political discourse and one for media discourse) in which all our data (respectively 137 and 84 documents) have been stored and coded manually. In our endnotes, we refer to document numbers (e.g., D1, 2, 3), which are based on our documentation system in the database. By adding a P or an M we distinguished between the political discourse and the media discourse. DP1, for example, refers to document 1 on political discourse in Atlas.ti, whereas DM1 refers to document 1 on media discourse. In the Appendices A and B we provide an overview of the various documents, so that they can be traced back to their original source.

Our codes consist of a combination of the above search terms (migration, border, asylum, etc.) and any variation of COVID-19. Using these codes as a guideline, we were able to quickly identify paragraphs and sentences in our data that were discussing both migration and COVID-19. After that, each set of codes was manually reviewed to see if a link was made between COVID-19 and migration, and if so, what the nature of this link was. This means that we looked at specific sentences as well as at short paragraphs (usually about five sentences long). Also, sentences and paragraphs are always viewed in the context of the entire source; after all, with many codings, it was necessary to read both backwards and forwards to understand the nature of any particular link between COVID-19 and migration. For example, an isolated sentence or paragraph often does not provide a definitive answer about how a statement is intended and/or how it will be received by a possible audience.

Furthermore, we assessed each alleged link between COVID-19 and migration for proximity, proportionality, the perceived (implicit and/or explicit) underlying problematization, and any unspoken assumptions. This process was guided by questions like 'how is migration / how are [various groups of] migrants being problematized in the light of COVID-19?', 'how are [various groups of] migrants being framed?', 'what are the underlying assumptions?', 'are the linked entities sufficiently related to one another for them to be connected like this?', 'is the link logically consistent?', and 'are proposed countermeasures proportionate and proximate to the harm they seek to address?'. On the basis of our answers to these guiding questions, we were able to group together paragraphs and sentences into categories like 'Governing migration through COVID-19- and 'Governing migration and COVID-19- .
