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Research Questions & Hypotheses

Everything downstream — design, sampling, analysis — inherits its quality from the question. Here is how to write one worth answering.

Methodology step

Why the question comes first

A research question is a precise, answerable interrogative about a defined phenomenon in a defined population. It is narrower than a topic (“employee engagement” is a topic; “Is perceived supervisor support associated with engagement among frontline retail staff?” is a question) and it is chosen before the design, because the question dictates the design — not the other way round. Researchers who start from a method (“I want to run SEM”) or a dataset usually end up answering a question nobody asked.

A useful discipline: write the question, then ask what evidence would answer it. If you can describe the table or quote that would settle the matter, the question is answerable. If not, keep sharpening.

The FINER criteria

A classic screen for evaluating a candidate question — it should be:

F — Feasible	Adequate access to participants and data, adequate time, money and skills, and a scope one study can actually cover. A brilliant question you cannot execute is a bad question for this study.
I — Interesting	Genuinely intriguing to you (you will live with it for years) and to an identifiable audience of scholars or practitioners.
N — Novel	Extends, contradicts or contextualizes prior work — replication in a new context counts; restating a settled finding does not.
E — Ethical	Answerable without unacceptable risk, deception or intrusion; capable of clearing institutional review.
R — Relevant	The answer matters — to theory, to practice or to policy — and someone could act differently because of it.

PICO-style structuring

Borrowed from evidence-based medicine and easily adapted to management research, PICO forces every component of a comparative question into the open:

P — Population	Who exactly? “Frontline employees in Indian quick-service restaurants with 6+ months tenure”, not “employees”.
I — Intervention / Interest (the X)	The exposure, intervention or predictor: structured onboarding; algorithmic scheduling; influencer endorsement.
C — Comparison	Compared with what? Standard onboarding; manager-made schedules; celebrity endorsement; or simply “lower levels of X” in correlational work.
O — Outcome (the Y)	The measurable result: 6-month retention; schedule satisfaction; purchase intention.

PICO example (experimental): Among new sales hires (P), does gamified product training (I), compared with slide-based training (C), improve 90-day quota attainment (O)?
PICO-ish example (correlational): Among mid-career managers (P), is perceived career plateau (I, as a measured predictor) associated with lower innovative work behaviour (O), relative to managers not perceiving a plateau (C)?
Descriptive and qualitative questions drop the comparison: What proportion of P exhibit O? How do P experience I?

From question to hypothesis

A hypothesis is a testable, falsifiable statement of the expected answer, derived from theory or prior evidence — it belongs in quantitative confirmatory studies. Qualitative and exploratory studies keep open questions (or state tentative propositions) instead; forcing hypotheses onto them is a category error.

Write hypotheses so that a specific statistical result could contradict them. “Training affects performance” is untestably vague; “New hires receiving gamified training will show higher 90-day quota attainment than those receiving slide-based training” names the variables, the population and the direction.

Null hypothesis (H₀)	The no-effect default the statistical test evaluates: no difference between groups, zero correlation. You never “prove” H₀ — you either reject it or fail to reject it.
Alternative hypothesis (H₁)	The researcher's claim: a difference or relationship exists. This is what your theory motivates and your paper states as H1, H2, …
Directional (one-tailed)	Predicts the direction: “X is positively related to Y”. Requires theoretical justification stated in advance — never chosen after seeing the data.
Non-directional (two-tailed)	Predicts a difference or relationship without direction — the honest default when theory is silent about direction.

Good vs weak questions — worked contrasts

Weak: “Is social media good for marketing?” → Better: “Among D2C apparel brands, is influencer-generated content associated with higher add-to-cart rates than brand-generated content?”
Weak: “Why is attrition high?” → Better (correlational): “Are perceived career-growth opportunities associated with turnover intention among 2–5-year-tenure software engineers, controlling for pay satisfaction?” — or Better (qualitative): “How do departing engineers narrate their decision to leave?”
Weak: “Does leadership impact performance?” → Better: “Does weekly one-to-one coaching by first-line supervisors, versus monthly reviews, change service-quality scores in call-center teams?”

Common pitfalls

Questions that embed their own answer (“Why does bad management cause attrition?” assumes both the cause and its badness)
Double-barrelled questions that hide two studies in one sentence
Hypotheses written after seeing the results (HARKing) and presented as a priori
Directional hypotheses adopted for the smaller p-value rather than from theory
Vague populations (“employees”, “consumers”) that make sampling and generalization claims unanswerable
Confusing research questions with survey questions — the former structure the study; the latter are instrument items

Checklist before you proceed

The question names a specific population, the key constructs, and (if comparative) the comparison
It passes all five FINER criteria — feasibility checked against real access, time and skills
Its type (descriptive / relational / causal / meaning-oriented) is explicit, because that choice selects the design
Quantitative confirmatory studies: each hypothesis is falsifiable, directional only when theory justifies it, and written down before data collection
You can sketch the exact table, coefficient or theme that would answer it

Correlational Researchrelational questions and the models that test them Experimental Researchcausal questions and what they demand of the design Qualitative Researchmeaning-oriented questions that need open inquiry, not hypotheses Variables & Measurementturn the constructs in your question into measurable variables

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