EE Feasibility Check: Sources, Data, and Scope (Before You Commit)
Deciding on an Extended Essay research question is one of those moments that can feel both thrilling and quietly terrifying. You want something original, interesting, and meaningful — but you also need something realistically achievable within the available time, resources, and ethical boundaries. This post walks you through a pragmatic feasibility check: how to test sources, anticipate data needs, and set a scope that lets you do excellent analysis rather than heroic overreach.
Why a feasibility check matters (and what it saves you)
Most EE students learn, sometimes the hard way, that a brilliantly worded question is only valuable if it can be answered with the resources at hand. A feasibility check prevents wasted months, manages supervisor expectations, and allows you to pivot early when a topic is promising in idea but problematic in practice. Think of it as a small, systematic pilot study you do with your head before you commit your calendar and energy to the full investigation.
What ‘feasibility’ really covers
Feasibility is not just about whether you can find sources. It includes several practical dimensions:
- Access: Can you obtain the primary and secondary sources you need?
- Data quality: Will the data be reliable and sufficient for meaningful analysis?
- Scope: Is the question narrow enough to investigate thoroughly yet broad enough to be substantial?
- Ethics and permissions: Do you need consent, school approval, or special clearance?
- Time and logistics: Is the timeline realistic given experiments, fieldwork, or archival requests?
- Supervision: Can your supervisor reasonably support the methods and topic?
Quick triage: a table to test your idea in five minutes
Use this simple table as a rapid diagnostic. Be honest — if several rows land in the amber/red zone, you should either adjust the question or plan a stronger mitigation.
| Aspect | Concrete test | How to pass the test |
|---|---|---|
| Source availability | Can you list 3 primary and 3 secondary sources you could access within weeks? | Yes = pass; No = look for alternatives or narrower question |
| Data sufficiency | Can you collect enough data to show a trend or pattern (surveys, experiments, archival samples)? | Quick pilot or sample check; if not, scale down the analysis or change method |
| Ethics/permissions | Are ethical approvals needed and obtainable in time? | If approval is lengthy, consider secondary-data approach or simulated data with justification |
| Supervisor fit | Does your supervisor have the subject knowledge or willingness to guide methodology? | If not, seek co-supervision or pick a topic closer to their expertise |
| Time vs complexity | Can the project be completed in the available months without sacrificing analysis? | Create a draft schedule and pilot a core task to test timing |
Sources: where to look and how to test availability
Start with a rough map of where the evidence for your question will come from. Sources typically fall into three buckets: primary (original data you collect), secondary (scholarly analysis, books, articles), and tertiary (reference material, datasets compiled by others).
- Primary sources: lab results, interviews, surveys, archival documents, field observations. These are gold because they give you control, but they require planning and often ethics clearance.
- Secondary sources: journal articles, books, dissertations. They help you situate your question and offer methods you can adapt.
- Tertiary sources: official datasets, government statistics, meta-analyses. These are great for comparative context.
How to test source availability in practice:
- Library check: identify 3 relevant books or 5 recent journal articles using your school library catalogue or online databases. If you can’t find that much, the topic may be too niche without access to a university library or inter-library loan.
- Archival check: contact archives or special collections early — many require appointment windows. Ask for digitized inventories; a quick reply (or lack of one) is diagnostic.
- Fieldwork check: if interviews or experiments are needed, draft a short script and approach 2–3 potential participants or labs to test responsiveness and permissions.
- Dataset check: search for public datasets or contact data owners for access. Confirm formats and documentation to ensure the data are analyzable.
Data: planning collection, reliability, and sample considerations
Data planning is where many projects succeed or falter. Your aim is to gather enough evidence to support a clear analysis without drowning yourself in complexity.
Practical tips for data planning:
- Define the minimum viable dataset: What is the smallest set of observations that would let you detect a pattern or test your hypothesis?
- Pilot early: run a mini-survey, a single lab trial, or pull a 100-row sample from a dataset. The pilot tells you whether your instrument works and how much time each datapoint takes.
- Consider reliability and validity: are your measurements reproducible? Will your survey questions produce clear, interpretable answers?
- Sample size: avoid rigid rules that sound like prescriptions. Instead, frame it as “enough to show a robust pattern.” For many EE experiments and surveys, a small but well-collected dataset with clear error analysis can be stronger than a large but sloppy one.
- Ethics and consent: plan consent forms, anonymization, and safe data storage now — approvals can take time.
Scope: narrowing without killing originality
Scope is the art of asking a question you can answer deeply. Too broad and you list facts; too narrow and you might not have enough to analyze. Use the following strategies:
- Turn broad topics into focused comparisons: rather than “How does X affect Y?” consider “How does X affect Y among Z population under condition A?”
- Set a clear dependent variable and limit confounders: fewer variables mean cleaner analysis.
- Use geographic or temporal limits wisely: a case study approach is often more manageable than a global comparison.
- Be pragmatic: if a method requires advanced equipment, either locate the equipment early or adapt the method to what’s available.
A practical step-by-step feasibility checklist
Before you write the proposal, run through this ordered checklist. Treat it like a mini-project plan that you can show your supervisor.
- Draft a concise working question (one sentence). If you can’t state it in a sentence, you probably don’t have focus yet.
- Create a 1-page source inventory listing 3 primary and 3 secondary sources you can access now or within weeks.
- Design a pilot: one experiment, a 10–15 question survey, or a quick archival pull.
- Estimate time: break the project into weeks and estimate hours for each stage (data collection, analysis, writing, revision).
- Identify ethical concerns and whether approvals are needed.
- Check supervisor fit and ask for their early read on feasibility.
- Decide: Green = proceed; Amber = revise and run a second pilot; Red = rethink the topic.
How to use your supervisor and support networks
Your supervisor is a resource for methodology, scope-checking, and reading drafts — but they are not your safety net for poor planning. Use short, focused meetings to test specific elements: show them your source list, your pilot data, or your timeline. If you need additional help with method design, consider targeted tutoring: a few sessions of 1-on-1 guidance can boost confidence on experimental design, data analysis, or structuring an argument. For example, Sparkl’s tutors can help with method selection and tailored study plans when you hit a methodological wall.
Narrative examples: applying the feasibility check
Examples help make this concrete. Below are three brief case studies showing how a feasibility check can transform a promising idea into an achievable project.
Case study 1 — Biology: Can soil pH explain growth differences in local tomato varieties?
Initial idea: Compare growth rates of two tomato varieties across different garden soils. Quick feasibility steps:
- Source check: Are seeds and soil types available? Yes — seeds ordered, soils sampled from three local gardens.
- Pilot: Plant 6 seedlings (3 per variety) to test growth measurement protocol — this reveals variability and time per measurement.
- Data planning: Decide on measurable dependent variables (height, leaf count, fruit mass) and schedule measurements weekly for a fixed number of weeks. Include simple controls (same pot size, watering regime).
- Ethics/logistics: No human subjects, but space and light must be consistent — check school lab or a controlled windowsill.
Verdict: Feasible if you can secure a controlled growing environment and follow through weekly measurements. If not, scale to one controlled variable (e.g., only soil pH) and use a smaller sample size with clear error analysis.
Case study 2 — History: How did local newspapers frame a specific community event?
Initial idea: Analyze newspaper representation of a historical community event. Quick feasibility steps:
- Source check: Contact the local archive and search digitized newspaper collections. If newspapers are inaccessible, consider oral histories or secondary accounts.
- Pilot: Pull 5 articles and perform a short content analysis to check if theme categories emerge reliably.
- Data planning: Define coding categories, inter-coder reliability (if working with a peer), and a time window for sampling articles.
- Ethics/logistics: Archives may charge; plan for lead time on requests.
Verdict: Feasible with archive access. If the archive is closed or expensive, pivot to accessible materials like public statements, council minutes, or digitized papers.
Case study 3 — Economics/Business: Do consumer preferences vary by price points in a campus market?
Initial idea: Survey shoppers to see how price influences choice. Quick feasibility steps:
- Source check: Is the campus market operational and are students willing to take a short survey? Pilot with 20 respondents to estimate response rate.
- Pilot: A short 8-question survey helps test question clarity and time to complete; it also estimates how many days of fieldwork you’ll need.
- Data planning: Decide analysis method (basic descriptive statistics or simple regression). Ensure variables are measurable and that you can collect demographic context ethically.
- Ethics/logistics: Obtain consent, ensure anonymity, and check whether you need permission from campus authorities.
Verdict: Feasible if permission is granted and response rate is acceptable. If response rate is low, consider incentivizing participation ethically or using pre-existing sales data if available.
Common pitfalls and practical fixes
- Pitfall: Waiting too long to check archive or equipment availability. Fix: Contact providers as soon as you have an idea; treat replies as data.
- Pitfall: Over-ambitious sample size. Fix: Aim for sound methods on a smaller scale and justify limits transparently in the write-up.
- Pitfall: Vague variables. Fix: Operationalize variables in a pilot (write precise measurement steps).
- Pitfall: Ignoring ethics. Fix: Draft consent language and anonymization plans on day one; supervisors appreciate foresight.
Decision template: Go / Revise / Rethink
Use this short decision template after your pilot and source checks:
| Criterion | Pass | Action if fail |
|---|---|---|
| Primary sources accessible | List of 3 reachable sources | Narrow question or use more secondary sources |
| Pilot produces usable data | Pilot yields analyzable output | Revise method or redefine variables |
| Supervisor supports approach | Clear sign-off or actionable feedback | Seek alternate supervisor or co-supervision |
| Ethics and permissions manageable | Approvals or clear path to approval | Switch to publicly available data or anonymized methods |
Tools and small supports that help
A few targeted resources can make your feasibility work smoother: small tutoring sessions for method design, a brief statistics consultation for analysis planning, or a template consent form to speed ethical approvals. If you want tailored, 1-on-1 help to design a pilot or map sources and timelines, short sessions with subject-specialist tutors can pay big dividends. For example, Sparkl’s personalized tutoring often focuses on tailored study plans, method refinement, and AI-driven insights to help turn pilot data into a robust plan.
How this fits with IA and TOK
Your EE feasibility work often intersects with Internal Assessments and Theory of Knowledge. IA experience in method design or data collection can be a rehearsal for EE methods. Use TOK to reflect on assumptions in your methodology: how do you know your measures really capture the phenomenon you care about? Being explicit about sources of knowledge and the limits of your methods strengthens both EE analysis and TOK reflections.
Final practical tips before you commit
- Document every check: note replies from archives, pilot results, and supervisor feedback — you’ll thank yourself when writing the methods and reflection sections.
- Keep a backup question or method: pivoting is easier with a prepared alternative.
- Be conservative in your timeline estimates and generous in buffer time for approvals and unexpected delays.
- Write the methods section early — explaining how you will collect and analyze data helps reveal feasibility problems before they become crises.
Conclusion
A careful feasibility check turns a hopeful idea into a manageable research plan: map your sources, pilot your method, assess time and permissions, and refine scope until the question becomes answerable with depth rather than breadth. That disciplined groundwork is what separates an aspirational topic from an Extended Essay that delivers clear, defensible analysis.
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