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Research Methods — Module 02

Asking Good Research Questions

From vague topic to focused, testable question — the skill everything else depends on

Before You Start

Prerequisites: Module 1 (What is Research?).

Estimated time: ~45 minutes including the exercise.

What you need: Pen and paper or a Google Doc. A topic you are genuinely curious about helps.

By the end of this module you will be able to apply the FINER criteria, write a testable research question, state a hypothesis, and identify variables.

The Big Idea

A good research question is focused, researchable, and meaningful. Vague questions produce vague answers — and vague answers help no one. The most important skill in research is not running statistics or collecting data. It is knowing exactly what you are trying to find out.

Deep Dive

The FINER Criteria

A research question should pass the FINER test before you invest time in designing a study. FINER stands for:

Feasible

Can you actually do it with the time, budget, and access you have?

Interesting

Does it matter to you and to your field? Will you stay motivated?

Novel

Does it add something new, or fill a gap in the existing literature?

Ethical

Can it be done without harming participants or violating their rights?

Relevant

Does it contribute to knowledge that matters to the field or society?

Narrowing a Topic into a Question

Most students start with a topic that is far too broad. The narrowing process is iterative — you keep asking "but more specifically, what about it?"

Too broad: "Does social media affect mental health?"

Getting closer: "Is Instagram use linked to anxiety in young people?"

Research-ready:"Is there a relationship between daily Instagram use (hours per day) and self-reported anxiety scores (GAD-7) among college students aged 18–24 at a community college?"

Notice what the good question does:

Specifies the population (college students aged 18–24)
Specifies the measurement (hours per day; GAD-7 anxiety scale)
Specifies the relationship being investigated
Is testable — you could actually collect this data

Research Questions vs. Hypotheses

A research question opens the inquiry. A hypothesis is your specific, testable prediction about the answer.

Type	Format	Example
Research Question	"What is the relationship between X and Y?" or "Does X affect Y?"	Is there a relationship between daily Instagram use and anxiety in college students?
Alternative Hypothesis (H₁)	A specific, directional or non-directional prediction	H₁: Students who use Instagram more than 3 hours per day will report significantly higher anxiety scores than those who use it less than 1 hour per day.
Null Hypothesis (H₀)	The "no effect / no relationship" claim you are trying to disprove	H₀: There is no significant difference in anxiety scores between students with high and low Instagram use.

Why do we need the null hypothesis? Statistics can never "prove" something true. Instead, we test whether our data gives us enough evidence to reject the null hypothesis. If we reject it, we have support for our alternative. If we cannot reject it, we do not have enough evidence — but that does not mean the null is proven true.

Directional vs. Non-Directional Hypotheses

Type	What it predicts	Example
Directional	Predicts which direction the effect goes (more/less, higher/lower)	More Instagram use will be associated with higher anxiety.
Non-directional	Predicts there will be a difference but does not specify direction	Instagram use will be associated with anxiety scores differently than low use.

Use a directional hypothesis when existing theory or prior research clearly predicts a specific direction. Use non-directional when it is genuinely unclear which way the effect will go.

Variables: The Building Blocks of Research Questions

Every research question involves variables — things that can change or vary. Identifying them clearly prevents confusion later.

Independent Variable (IV) — the variable you think causes or influences the outcome. What you manipulate or compare.
Example: Hours of Instagram use per day

Dependent Variable (DV) — the outcome you are measuring. What you think is affected.
Example: Anxiety score on the GAD-7

Control Variables — things you hold constant to prevent them from influencing your results.
Example: Age range (18–24 only), college setting only

Confounding Variables — things that vary along with your IV and could be the real cause of changes in the DV — the sneaky troublemakers in research.
Example: Sleep quality — people who use Instagram more may also sleep less, and poor sleep causes anxiety independently.

Real Example: Narrowing Three Vague Topics

Topic 1: Sleep

Broad: "How does sleep affect students?"
Narrower: "Does sleep affect college students' academic performance?"
Research-ready: "Is there a significant relationship between average nightly sleep duration (hours) and cumulative GPA among full-time community college students in their first semester?"
IV: Nightly sleep duration | DV: Cumulative GPA

Topic 2: Grades

Broad: "Why do some students get better grades?"
Narrower: "Do study habits affect grades?"
Research-ready: "Do students who use active recall strategies (flashcards, self-testing) score significantly higher on midterm exams than students who re-read notes, in introductory psychology courses?"
IV: Study method (active recall vs. re-reading) | DV: Midterm exam score

Topic 3: Exercise

Broad: "Does exercise improve mental health?"
Narrower: "Does aerobic exercise reduce depression?"
Research-ready: "Does a 12-week program of 30-minute aerobic exercise sessions (3x/week) significantly reduce PHQ-9 depression scores in adults aged 25–40 diagnosed with mild-to-moderate depression?"
IV: Exercise program (present vs. absent) | DV: PHQ-9 depression score

Your Turn

What you need: Pen and paper or a Google Doc. About 15 minutes.

Pick a topic you genuinely care about. (It can be related to your major, your life, or something you keep reading about in the news.)
Write your broad question. Example: "Does coffee affect studying?"
Narrow it down three times by adding specificity. Each version should be more focused than the last.
For your final, most focused question: identify the independent variable, the dependent variable, and at least one possible confounding variable.
Bonus: Write a directional hypothesis (H₁) and a null hypothesis (H₀) for your question.

Keep this question — you will use it in later modules when you design your study and plan your data collection.

Brain Break — 2 Minutes

Think about something you "know" to be true.

Something you were told growing up, or that "everyone knows." Now ask: What would the research question be that could test this belief? What would the independent and dependent variables be?

Examples: "Reading before bed helps you sleep better." "Breakfast is the most important meal of the day." "You use only 10% of your brain."

Key Takeaways

A good research question is focused, researchable, and meaningful. Apply FINER: Feasible, Interesting, Novel, Ethical, Relevant.
Narrow broad topics by adding specificity: who, what, where, how measured, which population.
A hypothesis is a specific, testable prediction. The null hypothesis (H₀) claims no effect; the alternative hypothesis (H₁)predicts a difference or relationship.
Every research question involves variables: independent (cause), dependent (effect), control, and confounding.
Identifying confounding variables early is the mark of rigorous thinking — they are the most common reason studies mislead us.

Module 2 Complete!

You now have the most foundational skill in research. In Module 3, you will learn how to design a study that can actually answer your question.

Continue to Module 3: Research Design →