Types of Research Questions With Worked Examples

One example of this is a proposal that asks 'how does exercise affect mental health'. This seems to be asking one question, but in fact is asking to become one of several different studies. The sentence doesn't say which. Knowing the type of research question you are answering can help keep your project from drifting between them halfway through data collection. It also helps to recognize the types of research questions hiding inside a vague draft, before choosing one.

Six types cover almost every research question worth asking: descriptive, exploratory, comparative, relational, causal, and evaluative. Each one is really a commitment to a different method before a single source is read, since a descriptive question needs only a careful count and a causal one needs something closer to an experiment. Picking the type on purpose, rather than by whichever verb sounded natural, is the difference between a question you can actually answer and one that just sounds like research.

The Six Types of Research Questions

Each type below addresses a different kind of curiosity. Each worked example is as it would be stated in a proposal (not simplified for the table). The method column can be read quickly. This is what your phrasing commits you to before you've written anything else.

Type	Worked example	What it commits you to
Descriptive	What proportion of first-year nursing students report burnout symptoms by the end of their first clinical placement?	A structured survey or count. No comparison group and no test of association required.
Exploratory	How do first-generation college students describe asking faculty for help?	Interviews or open-ended data, coded for themes. Not a question a p-value can answer.
Comparative	Do first-generation and continuing-generation students differ in weekly hours of paid work?	Two or more defined groups, measured the same way and compared directly.
Relational	Is the number of hours students work for pay related to their GPA?	One group, two measured variables, and a correlation or regression, no groups to compare.
Causal	Does switching from lecture-based to active-learning instruction raise exam scores in introductory biology?	An experiment or quasi-experiment with random or matched assignment. A simple correlation cannot answer it on its own.
Evaluative	Did the university's peer-mentoring program improve first-year retention?	A before-and-after or matched-comparison design against a stated goal, not a single measurement after the fact.

Two pairs on that list get confused constantly, comparative with relational and causal with almost everything else, so those four earn their own space below rather than a single line each.

Descriptive and Exploratory Questions Do Not Compare Anything

A descriptive question asks what is there, and it is satisfied by an accurate count or a clear picture, nothing more. 'What proportion of first-year nursing students report burnout symptoms by the end of their first placement' needs a survey and a sample large enough to trust the percentage, and it stops there. No second group, no test of difference, no claim about cause.

An exploratory question asks what is going on, usually when too little is known to guess at variables yet. 'How do first-generation college students describe asking faculty for help' commits you to interviews or open-ended written responses, coded afterward for recurring themes, because the whole point is discovering categories you did not already have. Treating an exploratory question like a descriptive one, by forcing survey response options onto a topic nobody has mapped yet, is a common way students throw away the most useful part of the answer.

Comparative and Relational Questions Are the Two Everyone Confuses

A comparative question compares two or more well-defined groups and asks if there're differences between them. 'Do first-generation and continuing-generation students differ in weekly hours of paid work' needs both groups measured the same way and a test built to compare group averages, a t-test or its equivalent. There's no group in a relational question, which is exactly where the confusion starts.

A relational question stays inside one group and asks whether two measured things move together. 'Is the number of hours students work for pay related to their GPA' never splits anyone into groups; it correlates two variables measured on everybody in the sample. Asking a relational question and then designing a comparative study around it is one of the most common structural errors a supervisor catches on a first read.

Causal and Evaluative Questions Ask for the Most Evidence

A causal question claims that one thing changes another, and it is the one type a correlation cannot answer on its own. 'Does switching from lecture-based to active-learning instruction raise exam scores in introductory biology' needs sections assigned at random or matched closely on prior performance, because without that, a score difference could just as easily come from which students happened to choose which section. Verbs like 'affect,' 'increase,' and 'improve' quietly promise a causal design, which is why they are worth double-checking against what you actually built.

An evaluative question asks whether something worked against a stated goal, which sits close to causal reasoning but is framed around a program rather than a variable. 'Did the university's peer-mentoring program improve first-year retention' needs a before-and-after comparison, or a matched group of students who did not take part, measured against the retention rate the program was actually meant to move. A single semester's retention number, with nothing to compare it to, answers a different and much weaker question.

Why the Type You Pick Is a Methodological Choice

'Does remote work affect employees' is a draft with no type at all, which is exactly the problem: as written, it could become any of three different studies. The same starting topic can be genuinely phrased as three different types, and each phrasing commits a different study before a single participant is recruited. 'How much do employees work from home' is descriptive. 'Do employees at companies with formal remote-work policies work from home more than employees at companies without one' is comparative. 'Does the number of remote workdays reduce reported burnout' is causal, and only that last version needs an experiment, or a design strong enough to rule out other explanations.

No one type outranks the others; a well-run descriptive study beats a badly designed causal one every time. What matters is that the type on the page matches the design in the methods section, which is worth checking before a proposal goes anywhere. The step of turning a chosen type into a fully answerable sentence, tightened word by word, is covered in how to plan a research paper, which walks the whole planning sequence from a raw topic to a finished outline.

Testing a candidate phrasing against a research question generator before committing to a type is faster than realizing four sources into your reading that the study you built does not match the question you meant to ask.

Which type you need depends on whether the study wants a question or a hypothesis, and that distinction is drawn separately, along with how to write null and alternative hypotheses.

Once the type is settled and the wording is tight, drafting is its own separate stage. An AI humanizer built for academic writing is worth knowing about for that later point, once a draft exists and reads stiffer than intended, not before.