03 · What You Need to Know
Feasibility is the bridge between a valid design and an executable study
Methodological validity and practical feasibility are related but distinct. A design may be capable of answering the research question in principle while being impossible to implement with the available population, resources, expertise, infrastructure, or time.
NIH research-planning guidance explicitly advises investigators to evaluate whether available resources and the research environment can support the proposed work and to identify collaborators capable of filling gaps in expertise or resources. Feasibility is therefore not an embarrassing compromise added after the “real science” has been designed. It is part of determining what scientifically defensible research can actually be conducted.
Start by separating logical claims from feasibility assumptions
Consider the statement: “We will recruit 400 teachers from participating schools.”
The number 400 may be justified statistically. That establishes one part of the logic. It does not establish that 400 eligible teachers are accessible, that enough schools will participate, that teachers will respond, or that recruitment can occur within the available semester.
Research plans are full of sentences that quietly move from “we need” to “therefore we can.” Feasibility assessment examines the missing step.
Replace participant counts with a recruitment pathway
A target sample is not a recruitment plan. Work backward from the required completed sample.
If you need 300 completed participants, how many people are potentially eligible? How many can actually be approached? What proportion might consent? How many are likely to complete the procedure? How much attrition is plausible? How long does each stage take?
NCCIH guidance recommends evaluating population availability, participant burden, recruitment histories, competing studies, site resources, and local commitment while assessing study accrual feasibility. Although developed for clinical studies, these questions illustrate the broader difference between stating a target and demonstrating a plausible pathway to reach it.
Use accessible populations, not theoretical populations
The population of interest may contain millions of people while your recruitment pathway reaches only a few hundred. Conversely, an organization may have thousands of members while only a small subset satisfies the study's eligibility criteria.
A realistic plan is based on the population you can ethically and practically access through the proposed sampling and recruitment process. The distinction matters because theoretical population size does not solve local recruitment constraints.
Convert the timeline into actual work
“Data collection: three months” conceals the work required to produce three months of data.
Who recruits participants? How many can be processed each week? Does each interview require scheduling, conducting, transcribing, checking, and anonymizing? Does a laboratory sample need transport and processing? Can activities occur in parallel, or does one stage depend on completion of another?
A useful timeline models throughput, dependencies, and waiting time rather than merely assigning dates to headings.
This is why identifying tasks that commonly take longer than expected and building appropriate buffer into the research timeline are practical parts of feasibility assessment rather than project-management decoration.
Count waiting time even when nobody is actively working
Research timelines often underestimate external processes because researchers count labor rather than elapsed time.
An ethics application may take only several hours to prepare but then spend weeks in review. A data-use agreement may require little researcher effort while moving among offices. Equipment procurement can sit between approval, purchasing, delivery, installation, and training.
If the project cannot proceed until an external event occurs, the waiting period belongs in the research timeline. Planning around ethics approval, recruitment, data access, and other dependencies means treating these processes as part of the project rather than empty space between research tasks.
Check whether the team has the expertise the design assumes
A plan may require multilevel modeling, advanced qualitative analysis, psychometric validation, natural language processing, specialized laboratory methods, intervention development, or complex data linkage. Writing the method into the protocol does not create the expertise required to perform it.
Ask whether the necessary expertise exists within the team, can reasonably be learned within the project, or requires collaboration. NIH guidance specifically recommends identifying gaps in expertise and assembling collaborators who can strengthen and execute the research plan.
Check capacity, not merely availability
A resource can exist without having enough capacity for your study.
A laboratory may own the required equipment but process only 20 samples per week. A transcription service may exist but have a six-week turnaround. A collaborator may know the required method but have two hours per month available. A school may support the study but provide only one classroom each Friday.
Feasibility research has emphasized practical resource questions such as physical capacity, communication systems, time, equipment, software, staffing, institutional willingness, and the ability to handle failures or replacement needs.
Check whether the budget represents the actual workflow
A realistic budget follows activities. If every interview requires transcription, transcription belongs in the budget. If recruitment requires travel, travel belongs there. If secure data storage, software licenses, translation, equipment calibration, participant reimbursement, or specialist services are necessary, those costs cannot disappear because the project has a fixed funding ceiling.
When the true cost exceeds the available budget, the solution is usually to redesign the project, secure additional resources, or reduce scope. Pretending the activity will somehow cost less is not a methodological strategy.
Assess participant burden from the participant's perspective
Researchers naturally focus on what they need from participants. Feasibility also depends on what participation demands from the person providing it.
How long will the procedure really take? How many visits are required? Is travel involved? Are appointments compatible with work or school schedules? Are questions repetitive or sensitive? Does the intervention demand sustained adherence? Are follow-ups likely to feel worthwhile six months later?
NCCIH feasibility guidance explicitly recommends assessing and minimizing participant burden as part of recruitment planning.
Stress-test the study under ordinary failure, not catastrophe
You do not need to model every disaster imaginable. Instead, ask what happens when ordinary research problems occur.
What if recruitment is 25% slower than expected? A collaborator becomes unavailable for one month? Ethics review requires revisions? Ten percent of participants miss follow-up? One site withdraws? Equipment is unavailable for two weeks? Data cleaning takes twice as long?
A plan does not need to survive every scenario unchanged. It should, however, reveal where modest deviations cause the entire project to collapse.
Identify single points of failure
A single point of failure is a dependency whose loss stops the project because there is no practical alternative.
Examples include one unconfirmed recruitment site, one person with unique technical expertise, one instrument that cannot be replaced, one dataset whose access has not been established, or one narrow collection window that cannot move.
These dependencies deserve priority in feasibility verification with the people who control them. You may not be able to eliminate every single point of failure, but you should know where they are.
Distinguish “possible” from “probable enough to plan around”
Many research activities are technically possible. A school could recruit 200 students. A collaborator could finish the analysis in a week. Ethics approval could arrive quickly. Every participant could complete follow-up.
Feasibility planning should not be based on whether a favorable scenario is possible. It should be based on assumptions credible enough to support a real commitment of time, participants, and resources.
Logically coherent
If all stated assumptions hold, the proposed design could answer the research question.
Operationally realistic
The assumptions themselves are sufficiently credible, verified, resourced, and robust to ordinary implementation problems.
Some uncertainty requires empirical feasibility work
Not every feasibility question can be settled by checking records or speaking with collaborators. Investigators may lack reliable information about recruitment, retention, intervention delivery, acceptability, data collection, or other processes needed for a larger study.
NCCIH describes feasibility studies as a way to address such gaps by testing key aspects of an intervention and study design before a larger trial. Broader methodological work similarly treats pilot and feasibility studies as approaches for reducing uncertainties about future research.
If the important uncertainty can only be answered by trying the procedure, consider whether you should pilot the relevant part of the plan before committing to the full study.
Watch Out
A plan is not realistic merely because every task is technically possible. Feasibility depends on whether the complete sequence can be performed at the required scale, within the same project, by the available people, under the actual constraints, and within the available time and resources.
04 · A Practical Example
A perfectly coherent dissertation plan meets a calendar
Hypothetical Example
A doctoral researcher plans 60 qualitative interviews
The researcher has a clear question, appropriate qualitative methodology, a defensible sampling strategy, and a six-month period available for recruitment, interviewing, transcription, analysis, and thesis writing.
On paper Sixty interviews appear achievable. The researcher estimates one hour per interview and therefore imagines roughly 60 hours of data collection.
Convert interviews into workflow
Each interview also requires recruitment, eligibility checking, consent, scheduling, preparation, file handling, field notes, transcription or transcript checking, anonymization, coding, and analysis.
Check access
The partner organization estimates that only 90 potentially eligible people can realistically be approached during the recruitment period. Sixty completed interviews would therefore require an unusually high participation rate.
Check analytical workload
Preliminary coding of several practice transcripts shows that analysis requires substantially more time than the original timeline allocated.
Stress-test the deadline
Even a modest delay in recruitment pushes transcription and analysis into the period reserved for writing.
Revise
The researcher reduces the planned scope and aligns the sampling strategy with the methodological purpose rather than preserving an arbitrary interview count. The resulting study is smaller but executable and still capable of answering the refined question.
The original plan was not illogical. Its weakness was that it treated interviews as one-hour events instead of multi-stage research objects with a remarkable ability to consume calendars.
