What Went Well

This week I made real progress moving the methodology from a rough outline into a properly structured section of the report. I was able to clearly set out our mixed-methods approach, explain how we're using pupil premium/FSM eligibility as a working definition of "disadvantaged," and describe each of our data sources — the RLT stakeholder survey, the school's Attendance Policy, the Pupil Premium Strategy Statement, and the national DfE performance data — in enough detail that someone outside the project could understand exactly what we did and why. I was also pleased with how well the literature review slotted into the methodology once I had it — being able to justify our analytical choices (for example, using PATA theory to interpret student trust scores, or Reay's work on the "invisibility" of class to justify a closer thematic read of school policy documents) made the section feel far more rigorous than a purely descriptive account of our data sources would have been.

Even Better If

Even better if I had built in more slack for setbacks around data access from the start. I lost time this week rebuilding a draft I thought was more complete than it turned out to be, and separately, I ran into practical limits trying to pull live figures from the DfE Compare School Performance site, which blocks automated access. Moreover our contact at the school has not provided us with the data we need, another significant data limitation. In hindsight, I should have flagged both of these risks earlier and built a buffer into my working schedule rather than treating data collection as a formality that would sort itself out alongside the writing.

Reflection

This week reminded me that methodology writing is as much about being honest regarding the limits of your data as it is about describing your method. Having to explicitly write out that our survey data isn't broken down by pupil premium status, and that we're still waiting on data we requested from the school, was uncomfortable at first — it felt like admitting a weakness in the project. But actually writing those limitations into the methodology made the whole section stronger and more credible, and forced me to think harder about what we can and can't claim from the data we do have. I also noticed how much the quality of my methodology depended on other people's work being ready (the literature review) and other people's cooperation (the school providing data) — this is a genuinely collaborative, dependent process, not something I can fully control by working harder on my own piece of it.

A Struggle

My biggest struggle this week was around incomplete or inaccessible data. Specifically: our survey dataset doesn't let us isolate the disadvantaged-pupil experience directly, we're still missing a full response from the school on the additional data we requested, and I couldn't independently verify the DfE attainment figures I wanted to reference because the site blocked automated retrieval. I found it genuinely frustrating to write a methodology that relies on data I don't fully have in hand yet, and I had to keep resisting the temptation to either overstate what our data shows or quietly skip over the gaps instead of naming them properly.

Character Trait to Improve Next Week

Patience with incomplete information. I noticed this week that my instinct when faced with a data gap is to want to solve it immediately — chase the school again, try another workaround for the DfE data — rather than sitting with the uncertainty and working methodically around it in the meantime. Next week I want to practise setting a clear limitations statement early, moving on with what I can control, and following up on outstanding requests on a schedule rather than reactively, so that gaps in the data don't stall the rest of my work.