Quantitative Research Question Examples by Study Design

A quantitative research question has to do more than name a topic — its wording has to match the study design that will actually answer it. A question phrased as a causal claim (“does X increase Y?”) cannot honestly be answered by a design that never manipulates X, and a question phrased as a simple description (“what proportion of X?”) wastes the power of a design built to test a relationship. This guide shows what a well-formed quantitative research question looks like at each of the four major design families — descriptive, correlational, quasi-experimental, and experimental (RCT) — using illustrative example questions, not case studies drawn from any real, specific published study.

For the process of narrowing a broad topic into a question and evaluating it against the FINER criteria, see CASRAI’s guide How to Write a Research Question: From Broad Topic to FINER Criteria. This page assumes that step is done and focuses specifically on how the same underlying topic gets rewritten differently depending on which quantitative design will answer it.

Why study design determines a question’s wording

Every quantitative research question implicitly commits to a design the moment it names a relationship between variables. Four elements recur across all four design families, but which ones are present — and how they’re worded — differs by design:

• Population — who or what is being studied (see Defining the Population).
• Variable(s) — what is being measured, and whether the design treats a variable as independent (manipulated or grouping) or dependent (outcome) — see Independent vs. Dependent Variable.
• Relationship type — whether the question asks about a single characteristic, an association, a group difference, or a causal effect.
• Operational definition — how an abstract concept (e.g., “engagement,” “adherence,” “burnout”) is measured concretely enough to answer the question — see Operational Definition.

The table below shows how the same broad topic — student use of a mobile study app — gets rewritten as four structurally different, equally legitimate quantitative research questions depending on the design chosen to answer it.

Descriptive research question examples

A descriptive design measures and reports a variable, or the distribution of a variable, in a defined population — without testing a relationship between two variables or comparing groups. Descriptive quantitative questions typically use “what,” “how many,” “how often,” or “what proportion” framing, and never imply cause, effect, or association.

• What percentage of clinical trial protocols registered with a national registry report their primary outcome results within 12 months of trial completion?
• What is the average number of co-authors per publication in a given discipline over a five-year period?
• How frequently do early-career researchers report using preprint servers before formal journal submission?

Common pitfall: adding comparison or causal language (“compared with,” “leads to,” “predicts”) to a descriptive question overstates what a purely descriptive design can support — that wording belongs to one of the three design families below.

Correlational research question examples

A correlational design measures two or more variables as they naturally occur, without manipulating anything, and tests whether they are statistically associated. Correlational questions use “association,” “relationship,” “correlated with,” or “predict” language — but not causal verbs like “cause,” “increase,” or “improve,” because a correlational design alone cannot establish causation, even when a strong association is found.

• Is there a relationship between the number of data-sharing statements a journal requires and the average citation count of articles it publishes?
• Does self-reported research-integrity training hours correlate with faculty confidence in identifying research misconduct?
• Is grant application word count associated with reviewer-assigned score in a given funding program?

Common pitfall: reporting a correlational finding using causal language in the write-up (e.g., “training improves confidence”) when the question and design only support association — this is a frequent, avoidable mismatch between what a study asks and what it can honestly conclude.

Quasi-experimental research question examples

A quasi-experimental design compares groups or conditions — like an experiment — but without random assignment to those groups; group membership is determined by a pre-existing or naturally occurring factor (e.g., which institution a participant belongs to, which policy was already in place, or a self-selected choice). This design supports stronger causal inference than a correlational study but weaker inference than a true experiment, because of possible confounding from whatever determined group membership. Questions typically use comparative language (“higher/lower than,” “differ between”) applied to intact or pre-existing groups.

• Do institutions that adopted a structured data management plan (DMP) requirement before grant approval show different data-deposit rates than institutions without that requirement?
• Following the introduction of a mandatory CRediT contributorship statement at a set of journals, did the proportion of authorship disputes reported to editors change compared with the two years prior?
• Do research groups that use electronic lab notebooks show different rates of data-availability compliance than groups using paper notebooks?

Common pitfall: describing a quasi-experimental finding as if random assignment ruled out confounding — a quasi-experimental question and its eventual write-up should name the comparison groups as pre-existing, and the discussion section should address plausible alternative explanations that randomization would otherwise have controlled for.

Experimental / RCT research question examples

A true experimental design — most rigorously, a randomized controlled trial (RCT) — randomly assigns participants to conditions and manipulates the independent variable directly, which is what allows a causal claim. In clinical and health research, this design family is where the Randomized Controlled Trial (RCT) definition and the PICOT structure (Population, Intervention, Comparison, Outcome, Time) from CASRAI’s FINER/PICOT guide apply most directly. Questions use explicit causal or effect language (“does X cause/increase/reduce Y compared with Z”) because random assignment is what earns that language.

• Among early-career researchers randomly assigned to a structured mentorship program versus standard onboarding, does the mentorship program increase the rate of first-author publication within two years?
• In a randomized trial comparing a structured data management plan (DMP) template against an unstructured template, does the structured template reduce time-to-completion for a compliant DMP?
• Among patients randomly assigned to a reminder-based medication adherence intervention versus usual care, does the intervention increase adherence rate at 90 days compared with usual care?

Common pitfall: labeling a study “experimental” or “RCT” in the question when assignment to groups was not actually random — this is the same category error as the quasi-experimental pitfall above, just in the other direction, and it’s one of the more common design-labeling errors reviewers flag.

A quick self-check before finalizing a quantitative research question

1. Does the verb in the question (“differ,” “associate,” “predict,” “increase,” “cause”) actually match what the design can support? A design without random assignment cannot honestly support a causal verb.
2. Is every variable operationally defined clearly enough that another researcher could measure it the same way? See Operational Definition.
3. Is the population named specifically enough to bound the study? See Defining the Population.
4. For a comparison or group-difference question, is it clear whether group membership was randomly assigned (experimental) or pre-existing (quasi-experimental)? Naming this correctly in the question itself avoids a mismatch that shows up later at the analysis or write-up stage — see Experimental Design.
5. Does the planned analysis match the question? A descriptive question calls for descriptive statistics; a correlational, quasi-experimental, or experimental question calls for inferential statistics that test a relationship or group difference.

Frequently asked questions

What’s the difference between a correlational and a quasi-experimental research question?

A correlational question measures two variables as they naturally occur and asks whether they’re associated, with no groups being compared. A quasi-experimental question compares two or more pre-existing groups (not randomly assigned) on an outcome. Both fall short of a true experiment because neither involves random assignment, but a quasi-experimental design is structured around a specific comparison in a way a purely correlational design is not.

Can a quantitative research question use causal language without random assignment?

Generally, no — causal verbs (“causes,” “increases,” “reduces,” “improves”) should be reserved for designs with random assignment to conditions, because only randomization credibly rules out that some other, unmeasured factor produced the observed difference. Correlational and quasi-experimental questions should use association or comparison language (“is associated with,” “differs from”) instead.

How specific should the population be in a quantitative research question?

Specific enough that another researcher could replicate the sampling frame from the question alone — a vague population (“adults,” “researchers”) is one of the most common reasons a quantitative research question fails the Feasible and Relevant parts of the FINER criteria. See CASRAI’s Defining the Population and the FINER discussion in How to Write a Research Question.

Do these examples come from real published studies?

No. Every example question on this page is an illustrative composite, written to demonstrate correct structure for a design type. None is drawn from, or attributed to, a specific real study, institution, or researcher.

Related CASRAI resources

• How to Write a Research Question: From Broad Topic to FINER Criteria — the process for narrowing a topic and applying FINER/PICOT, upstream of this page.
• Experimental Design
• Independent vs. Dependent Variable
• Operational Definition
• Defining the Population
• Descriptive Statistics vs. Inferential Statistics
• Randomized Controlled Trial (RCT)
• Null Hypothesis vs. Alternative Hypothesis
• Scholarly Writing cluster overview