How Do You Know Whether Your Objectives Can Actually Be Achieved With the Study You Designed?

Your Objective Sounds Good, but Can Your Study Actually Deliver It?

Consider this objective:

To determine the effect of generative AI use on undergraduate students' academic writing performance.

It is focused. The population is identifiable. The exposure and outcome appear reasonably clear. Yet none of that tells you whether the study you designed can actually accomplish it.

If your design is a one-time survey asking students how often they use generative AI and how good they think their writing is, you may be able to examine an association between self-reported AI use and self-reported writing perceptions. You have not automatically created evidence capable of establishing an effect on actual writing performance.

This is one of the most important tests of a research objective: not whether the sentence sounds researchable, but whether the design, evidence, and analysis can support what the objective promises.

Work Backward From the Evidence the Objective Requires

Every Objective Implies an Evidence Requirement

Start With What the Objective Requires You to Know

The most useful feasibility check begins with the objective rather than the method you happen to have available.

Ask:

If I were to claim that this objective had been addressed successfully, what evidence would I need?

For example:

Objective: To compare academic writing performance between students who receive an AI literacy intervention and students who receive standard instruction.

This objective requires evidence about at least two conditions or groups and a defensible measure of academic writing performance. Depending on the intended inference, it may also require appropriate allocation, baseline information, timing, sample size, and procedures for handling potential sources of bias.

The Philippine Council for Health Research and Development's research-methods guidance describes methodology as the “how” through which objectives are answered and emphasizes that study design provides the framework for collecting and analyzing data to attain those objectives. It recommends selecting a design according to its appropriateness for the objectives and then considering feasibility constraints such as ethics, time, personnel, resources, and access to the required population or data.

Match the Research Task to the Study Design

Different objectives require different forms of evidence.

The table is illustrative rather than prescriptive. Several designs may sometimes address the same objective, each with different strengths and limitations.

Check Whether You Are Measuring the Thing Named in the Objective

A surprisingly common mismatch occurs between the construct named in the objective and the variable actually measured.

Suppose your objective is:

To examine the relationship between generative AI use and academic writing performance.

But your questionnaire asks students to rate how much they believe generative AI improves their writing.

You have measured a perception of AI's influence, not necessarily academic writing performance.

The distinction matters. Researchers should not quietly substitute an available proxy for the construct promised in the objective without justification.

Check the Population and Data Source

An objective may also become unachievable because the study cannot access the people, records, documents, observations, or other evidence required.

If the objective concerns faculty implementation of institutional AI policy but your participants are exclusively students, the data source may not permit the intended conclusion.

Likewise, if the objective concerns university-wide prevalence but recruitment occurs through a small voluntary sample from one programme, the issue is not simply sample size. The relationship between the sampled participants and the population to which the objective refers also matters.

Check the Time Dimension

Words such as change, development, long-term, before and after, and trajectory imply a temporal structure.

Consider:

To examine how students' academic writing ability develops during four years of undergraduate study.

A one-time survey of one cohort cannot directly observe four years of within-person development. A cross-sectional comparison of different year levels might address a related question, but it does not automatically provide the same evidence as longitudinal observation.

The time structure of the design should therefore match the time structure of the objective.

Check the Intended Level of Inference

One of the most consequential mismatches involves causal language.

Terms such as effect, impact, causes, and sometimes influence may imply claims beyond simple association, depending on context.

SPIRIT guidance for randomized trials illustrates how tightly a precise research objective can be connected to study design, outcomes, comparison conditions, and statistical analysis. It notes that different questions, such as superiority, non-inferiority, or equivalence, can require different sample-size and analytical approaches.

The general lesson extends beyond trials: the strength of the verb should not exceed the inferential capacity of the design.

Check Whether the Analysis Can Answer the Objective

Having the right variables is not sufficient if the analytical strategy cannot address the question posed.

Suppose the objective is to compare changes in writing performance over several time points between two groups. A table containing only each group's final mean may omit the longitudinal structure that makes the objective distinctive.

Conversely, an objective may require only a descriptive estimate, while the proposed analysis includes elaborate modeling that does not contribute to the stated question.

Research objectives help guide protocol development, study design, analysis, and, in some quantitative designs, sample-size and power calculations. The analytical plan should therefore be traceable to the objective rather than added because a particular technique happens to be available.

Check Whether the Study Has Enough Information, Not Merely Enough Participants

Sample adequacy matters, but “enough participants” means different things across research methodologies.

In quantitative studies, considerations may include statistical precision, power, event frequency, model complexity, clustering, attrition, and the size of effects the study is designed to detect or estimate.

In qualitative research, adequacy is approached differently and may depend on the methodology, sampling logic, information provided by participants or cases, heterogeneity, analytic purpose, and other design-specific considerations.

The appropriate question is not simply whether the sample looks large. Ask whether the evidence base is adequate for the kind of conclusion promised by the objective.

Check Whether the Objective Is Practically Feasible

A design can be theoretically appropriate and still impossible to implement.

You may need access to participants you cannot recruit, proprietary records you cannot obtain, equipment you do not have, follow-up periods longer than the project allows, specialist analyses beyond available expertise, or a budget substantially larger than the study possesses.

Feasibility therefore includes practical and ethical constraints, not just methodological elegance.

This is one reason the appropriate number of research objectives cannot be decided independently of resources. Every additional objective creates another evidentiary commitment.

Ethical Feasibility Is Part of Research Feasibility

An objective is not achievable merely because researchers can imagine a design that would answer it. The necessary procedures must also be ethically permissible.

A design may expose participants to unjustified risk, require data that cannot lawfully or ethically be accessed, or involve recruitment procedures that are inappropriate for the population.

Ethical constraints can therefore require researchers to redesign the study, narrow the objective, or sometimes abandon the question.

Check the Objective Before Data Collection, Not Only Afterward

Objectives should be stress-tested during study planning.

If you discover after data collection that an essential variable was never measured, the problem is difficult to repair. If you discover beforehand that an objective requires a second measurement point, another participant group, or a different instrument, the design can still be revised.

This is why objective formulation and study design are iterative. You may draft an objective, discover that the necessary study is infeasible, revise the objective, and then reconsider the design again.

Current academic guidance similarly describes movement from question to aim and objectives as a process that may require returning to earlier stages as the research develops.

Sometimes the Correct Solution Is to Narrow the Objective

Suppose your available design is a cross-sectional survey and your original objective is:

To determine the long-term effect of generative AI use on students' academic writing development.

If a longitudinal or experimental design is not feasible, you have at least two options. Redesign the study so that it can support the intended question, or change the objective to one the available design can legitimately address.

For example:

To examine the association between students' self-reported frequency of generative AI use and academic writing self-efficacy.

That is a different question. It may be less ambitious, but it is preferable to retaining an impressive objective that the study cannot answer.

When narrowing, make sure the revised objective does not become so vague that its research purpose disappears.

Stress-Testing an Objective Against the Study Design

The revised objective is not merely a weaker version of the original. It describes what the available evidence can legitimately address.

This is the value of feasibility testing: it forces the objective and design to negotiate with each other before the conclusions do.

Common Mistakes When Checking Whether Objectives Are Achievable

Build an Objective-to-Evidence Map Before You Collect Data

Take every objective separately and work forward from the claim to the evidence required. This exercise often reveals missing measurements, inappropriate samples, unrealistic time frames, or objectives that have quietly become stronger than the design.

Then trace each objective all the way to the planned findings. You should be able to anticipate what kind of result or research output would address each objective, without predicting what that result will actually be.

Can Your Study Actually Achieve Each Objective?

Frequently Asked Questions About Achievable Research Objectives

Your Study Must Be Capable of Delivering What the Objective Promises

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