Tutorial on Types of Research Questions: Qualitative, Quantitative, and Mixed Methods Approaches

In this tutorial, we'll explore qualitative, quantitative, and mixed methods research questions. We'll look at definitions, examples, and tips to help you choose the right approach for your research. We'll cover the exploratory nature of qualitative questions, the hypothesis-testing of quantitative designs, and how mixed methods combine both approaches.

Foundational Principles of Research Question Design

Research questions are our guides throughout a study. When creating these questions, we need to align them with our research worldview and methodology. Good research questions come from literature reviews that find knowledge gaps. Our questions should determine whether we use qualitative, quantitative, or mixed approaches.

The methodology we choose affects how we write our questions. Qualitative questions often start with how or what to explore processes and meanings. Quantitative questions use does, is, or to what extent to look at relationships between variables. Mixed methods questions combine these approaches to address both exploratory and confirmatory goals.

Qualitative Research Questions: Exploring Meanings and Experiences

Defining Characteristics and Purpose

Qualitative research questions focus on depth over breadth. We use them to understand complex social phenomena through non-numerical data. These questions often arise when we see contradictions between theories and real-world observations. For example: How do socioeconomic factors influence patients' perceptions of telehealth accessibility during public health crises? This question works well for qualitative research because it focuses on subjective experiences.

Formulation Strategies

When developing qualitative questions, we can follow this three-step process:

Choose a broad area: Pick a general topic like "workplace communication in hybrid teams."
Narrow it down: Focus on specific aspects, such as "conflict resolution strategies in tech-sector hybrid teams."
Refine the question: Create an open-ended question like How do mid-level managers in tech firms use communication tools to resolve department conflicts?

We should also let our questions evolve as we collect data. We might start with a broad question about student resilience, then refine it to What coping strategies do first-generation college students use when on academic probation? after initial interviews.

Good Qualitative Questions

When creating qualitative questions, we should:

Ask for stories rather than yes/no answers
Use terminology that participants understand
Allow for unexpected themes to emerge

For example: How do park designers think about 'equitable access' when planning green spaces in changing neighborhoods? This question lets designers explain what equity means to them in their own context.

Quantitative Research Questions: Measuring Relationships and Testing Hypotheses

Structural Attributes and Objectives

Quantitative research questions convert concepts into measurable variables to test hypotheses. These questions often follow a structure that includes population, intervention, comparison, and outcome. For example: Does a 12-week mindfulness program reduce blood pressure compared to standard care in adults aged 50–65 with stage 1 hypertension? This question allows for clear measurement and statistical analysis.

Development Process

To develop quantitative questions, we should:

Identify variables: Determine which are independent (predictor) and dependent (outcome) variables.
Define measurements: Specify how to measure abstract concepts (e.g., define "academic success" as GPA plus retention rate).
Form hypotheses: State expected relationships (e.g., "Students with better sleep will have higher GPAs").
Finalize the question: Turn hypotheses into questions while keeping them testable.

A good example is: To what extent does graphene layer thickness correlate with thermal conductivity in battery anodes? This question specifies exact measurements, making data collection straightforward.

Common Problems to Avoid

Two common mistakes in quantitative questions are:

Too broad: Questions like Does social media affect mental health? don't specify platforms, populations, or health indicators.
Vague variables: Terms like "community engagement" need clear measurements (e.g., volunteer hours, meeting attendance).

A better version would be: Do Instagram users aged 18–24 who spend more than 2 hours daily report higher depression scores than non-users? This specifies all variables clearly.

Mixed Methods Research Questions: Combining Approaches

Basic Designs

Mixed methods questions combine qualitative and quantitative elements to address complex problems. There are three main designs:

Explanatory Sequential: First collect quantitative data, then follow up with qualitative methods (surveys → interviews).
Exploratory Sequential: Start with qualitative findings, then test them quantitatively (focus groups → experiments).
Convergent Parallel: Collect both types of data at the same time and integrate during analysis.

For example: How do nutritional literacy scores relate to dietary choices among low-income mothers, and what personal experiences explain outliers in this relationship? Here, we use quantitative data to find patterns and qualitative interviews to understand exceptions.

Creating Integrated Questions

A useful framework for mixed methods questions has three levels:

Main Question: Addresses the core research goal.
- How do neighborhood walkability scores and residents' safety perceptions together affect exercise frequency?
Quantitative Sub-question: Focuses on relationships between variables.
- What is the correlation between walkability ratings and weekly exercise minutes?
Qualitative Sub-question: Explores context and experiences.
- How do residents describe sidewalk maintenance affecting their willingness to walk?

This structure keeps methods aligned while allowing separate analysis before integration.

Handling Challenges

Two common challenges in mixed methods research are:

Conflicting Data: When quantitative and qualitative results disagree (e.g., surveys show a program works, but interviews show dissatisfaction), we can:
- Check findings with participants
- Use qualitative data to explain unusual quantitative results
Complex Analysis: Mixed methods often needs diverse expertise. For example, a concussion study might need statisticians for brain scan data and anthropologists for patient journals.

Choosing the Right Method

Decision Framework

When choosing between methods, consider these factors:

Factor	Qualitative	Quantitative	Mixed Methods
Research Goal	Explore, interpret	Test, predict	Explain, integrate
Data Type	Words, images	Numbers	Both
Sample Size	Small, purposive	Large, random	Multiple levels
Analysis Focus	Themes, stories	Statistics	Combined insights

Qualitative methods work well for early exploration, while quantitative approaches fit established research areas with clear metrics. Mixed methods bridge these phases, as seen in agricultural studies that first used farmer interviews to identify climate challenges, then developed models to predict crop yields under different policies.

Common Myths

"Qualitative is Easy": Actually, qualitative research often takes more time per participant. Transcribing and analyzing 50 hours of interviews can take longer than processing 500 surveys.
"Mixed Methods is Always Best": Mixed methods isn't always the right choice, especially when:
- Resources are limited
- Your field strongly prefers one approach
- A single method can answer your question completely

Conclusion: Matching Questions to Methods

Research question development isn't random—it reflects your theoretical approach, field standards, and practical limitations. Good research aligns every methodological choice with your study's core goals. As you develop your skills, practice analyzing published questions in your field, get peer feedback, and test your data collection tools. As mixed methods become more common for addressing complex problems, the ability to design integrated questions will become increasingly valuable. While technology may help generate research questions in the future, human judgment will always be essential for meaningful research.