"

4 Overview of Research Methods: Qualitative, Quantitative, and Mixed Methods

Martin LaGrow

Telling a Story

Imagine that one day, two ornithologists went out to study and gather information about the rare blue-eyed ground dove together. One brought a high-tech pair of binoculars: sharp, precise, perfect for measuring wingspan, counting feathers, and noting flight speed. The other brought a sketchpad, observed the bird, and took notes on its posture, movement, and the bird’s interaction with its surroundings.

Hours later, both returned and recorded vastly different sets of information gathered. The first researcher described the bird in numbers: “Wingspan: 1.2 inches. Flight time between perches: 4.7 seconds.” The second researcher wrote a narrative: “It hopped cautiously, head tilted, as if listening for something. It stayed close to the shaded branches.”

Which ornithologist collected the most important or relevant information possible about this rare species of bird? Whose record of the event is more critical or valuable? Which one of the researchers got the “right” information that day? The obvious answer is that both collected and reported valuable information to contribute to the field of research on this endangered species. But whether one of them took the “right” approach or the “wrong” approach is dependent upon one thing: what questions were they attempting to answer?

This short illustration articulates this generalization: If you want to measure, you go quantitative. If you want to understand why, you go qualitative.

Learning Objectives

By the end of this chapter, you will be able to:

  1. Define and differentiate among qualitative, quantitative, and mixed methods research approaches.
  2. Select a research method that best aligns with your research questions and problem statement.
  3. Describe common data collection techniques used in qualitative, quantitative, and mixed methods research.

Overview of Key Words and Concepts

Before we explore research methods in depth, it’s helpful to understand some of the key terms you’ll encounter throughout your dissertation process. These foundational concepts shape how you think about research questions, collect and interpret data, and present your findings. In this section, we introduce three important terms: qualitative, quantitative, and mixed methods. These will be explained more fully later in the chapter, but here is a high-level overview to get you started.

Qualitative

Qualitative Research is used when the goal is to explore complex ideas, behaviors, or experiences that are difficult to measure with numbers. It often seeks to answer questions like why, how, or what does it mean. This approach is reliant upon researcher observation and perspective, and often uses tools like interviews and focus groups.

Qualitative research is especially useful when you’re exploring new areas of inquiry or when you want to understand context and meaning from the participant’s perspective.

Quantitative

Quantitative Research, on the other hand, is used to examine measurable data and often focuses on the relationship between variables. It typically seeks to answer questions like how much, how many, or what is the relationship between variables. This approach emphasizes objectivity and often uses tools like surveys, tests, or statistical analysis.

Quantitative methods are often used when you want to test hypotheses, make predictions, or draw conclusions from large data sets.

Mixed Methods

Mixed Methods Research combines both qualitative and quantitative approaches in a single study. This allows researchers to benefit from the strengths of each method and to build a more complete picture of a research problem. Naturally, a mixed methods approach uses a mixture of qualitative and quantitative data gathering techniques.

Mixed methods can be particularly powerful when you’re tackling complex issues that require both measurable outcomes and rich, contextual understanding.

Qualitative Research Methods

Qualitative research is a method of inquiry that looks to gain insight on a problem by examining human experiences based on their thoughts, behaviors, and feelings (Merriam & Tisdell, 2016; Patton, 2015). Unlike quantitative research, which emphasizes numerical data and statistical analysis, qualitative research seeks to explore the how and why of a particular issue in depth through the perceptions of the research subjects (Creswell & Creswell, 2018). It is often used when people’s thoughts, feelings, or motivations can provide insight into complex or poorly understood topics. Researchers using this approach typically work with small, focused samples, and aim to interpret patterns and meanings in what they learn rather than measure variables.

Common Qualitative Research Designs

Case Study. A case study is an in-depth investigation of a single individual, group, organization, or event designed to shed light on a specific issue within its real-life context (Yin, 2018). Case studies often involve multiple data sources such as interviews, observations, and documents.

Example: A researcher conducts an in-depth analysis of a low-performing urban high school to understand the factors contributing to its low graduation rates.

Phenomenology. This type of study focuses on exploring and understanding how individuals experience a particular phenomenon (Merriam & Tisdell, 2016). The goal of a phenomenological study is to describe the essence of that phenomenon from the participants’ perspectives.

Example: A study explores the experiences of COVID-19 survivors suffering from long COVID to understand what it feels like to live through and beyond the disease.

Ethnography. An ethnography is a detailed study of the culture, behaviors, and social interactions of a  particular group or community. It is usually conducted with long-term observation and immersion in the group’s setting (Patton, 2015).

Example: A researcher spends a month living among a cloistered religious community, documenting what they observe to provide insights into how they express their faith, how they interact with the outside world, and how they maintain their traditions in the face of change.

Grounded Theory. This kind of research study is designed to generate a new theory grounded in the data collected from participants. In this approach, a researcher gathers data and analyzes it continuously to identify patterns and build a conceptual framework (Creswell & Creswell, 2018).

Example: A study of indigenous college students leads to the development of a theory explaining how they adapt to university life as a minority culture.

Narrative Inquiry. A narrative inquiry is built on collecting and interpreting people’s personal stories to understand how they make sense of their experiences (Clandinin & Connelly, as cited in Creswell & Creswell, 2018). It emphasizes the meaning individuals assign to events through storytelling. This kind of study may also be referred to as bibliographical or historical research.

Example: A researcher collects life stories from political refugees to understand how they reconstruct their identities after resettlement.

Qualitative Data Collection Techniques

The most common qualitative research methods include:

Interviews. Interviews involve one-on-one conversations where the researcher asks open-ended questions to explore the participant’s experiences, thoughts, or feelings (Patton, 2015). They can be structured, semi-structured, or unstructured. Whether qualitative interviews should be structured depends on your research question, the topic, and how much comparability you need between different interview subjects.

Focus Groups. A focus group gathers a small group of participants to discuss a topic guided by a facilitator; typically the researcher. This method is useful for exploring group norms, shared experiences, or diverse perspectives on an issue that all participants are invited to share their insight into (Creswell & Plano Clark, 2018).

Observations. Researchers can observe participants in their natural settings to understand behaviors, interactions, and social environments. Observations can be passive (non-participatory) or active (where the researcher becomes part of the setting). Observations work particularly well for ethnographic studies (Patton, 2015).

Document or Artifact Analysis. This involves analyzing written, visual, or audio materials such as letters, social media posts, photos, diaries, or organizational documents to gain insight into the context and meaning behind them. This is especially useful in a narrative (historic) study, particularly when survey subjects may no longer be alive (Merriam & Tisdell, 2016).

Field Notes. These are detailed notes taken by the researcher during or after observations, interviews, or fieldwork as in an ethnographic study. They include descriptions of what occurred as well as the researcher’s reflections and interpretations (Yin, 2018). Field notes can be a critical tool for ethnographic studies, and a secondary tool to gather unspoken information from focus groups and interviews.

Ethical Considerations in Qualitative Research

Qualitative research often involves direct interaction with participants, making ethical issues especially important. Common considerations include:

  • Informed consent: Participants must fully understand the purpose of the study, how their data will be used, and that participation is voluntary.
  • Confidentiality: Personal stories, cultural details, and sensitive experiences must be protected by using pseudonyms and careful data handling.
  • Power dynamics: Be mindful of the researcher’s role and how it may influence what participants share.
  • Emotional risk: Interviews or narratives may cause participants to recall traumatic experiences. Therefore, provide access to support or resources if needed.
  • Researcher bias: Reflexivity and transparency in how data are interpreted are critical (Merriam & Tisdell, 2016).

When to Use Qualitative Methods

Qualitative research is best suited for answering questions that explore the meaning, experience, or understanding of a particular issue (Creswell & Creswell, 2018). These questions focus on understanding people’s perspectives, emotions, behaviors, or social contexts. Qualitative studies are especially useful when the topic is complex, not well understood, or when little existing research is available. They are ideal for exploring new areas, generating ideas, and gaining insight into processes, relationships, or cultural patterns.

Questions that could be answered with qualitative research may begin with one of the following stems:

  • How do people experience…?
  • What are the perceptions of…?
  • In what ways do individuals interpret…?
  • Why do participants feel…?
  • What is it like to…?
  • What are the lived experiences of…?
  • How is [a phenomenon] understood within a specific context?
  • What influences how people respond to…?
  • What themes emerge from participants’ narratives about…?
  • How do cultural or social factors shape…?
  • What challenges do people face when…?

Strengths and Weaknesses of Qualitative Research

Qualitative research is a valuable tool for exploring complex human experiences. One of its key advantages is the ability to provide rich, in-depth data that captures the thoughts, emotions, and motivations of participants (Patton, 2015). It offers insights into behavior within its natural context, offering nuanced observations that are often missed by more structured methods (Creswell & Creswell, 2018). The flexibility of qualitative research allows it to adapt as new information emerges, making it particularly useful in exploratory studies (Merriam & Tisdell, 2016). Additionally, by prioritizing participants’ perspectives, qualitative research uncovers meanings and interpretations that are deeply rooted in individual and cultural experiences (Denzin & Lincoln, 2018).

However, qualitative research also has notable weaknesses. Qualitative research, by its very nature, relies on collecting large amounts of information from a small, non-random population. Thus, its findings are contextual and often difficult to generalize (Creswell & Creswell, 2018). The interpretive nature of data collection and analysis mean that researcher bias is always a potential limitation (Silverman, 2020). Conducting qualitative research is time-consuming, requiring extensive effort in data collection and analysis, such as transcribing interviews and coding responses (Patton, 2015). Furthermore, because the methods are flexible and context-dependent, studies can be hard to replicate (Merriam & Tisdell, 2016). Managing the often large volumes of unstructured data can also pose significant challenges in organizing and interpreting results effectively (Silverman, 2020).

Deciding on a Qualitative Approach

At this point, you may be leaning toward one research methodology over another. It may be helpful to you to consider the thought process of a (fictional) student who faced the same challenging decision, and how their thought process led them to ground their study in a qualitative approach. Before moving on to the other two reserach methods, read about Jennifer’s journey.

Beginning a Qualitative Journey

Jennifer sat at the corner table in the campus library, surrounded by highlighters, sticky notes, and reflected on her Crafting the Dissertation manual chapter about research methods.. Her laptop screen was filled with articles about teacher burnout, a subject she’d been passionate about ever since she began her doctoral program at Saint Peter’s University.

She had narrowed her dissertation topic down to one question: How do first-year urban teachers experience the emotional demands of their work? But now she faced the next big decision: selecting the right research methodology.

Her professor’s notes echoed in her head: If you want to measure, you go quantitative. If you want to understand why, you go qualitative. Jennifer knew she wasn’t looking for percentages or test scores. She wanted stories. She wanted meaning.

She pulled up her notes from the “Qualitative Research Methods” section of her book. Five designs: case study, phenomenology, ethnography, grounded theory, narrative inquiry. Each offered a different lens.

  • Case Study seemed too narrow; she didn’t want to focus on a single school.
  • Ethnography required long-term immersion, but she couldn’t spend months embedded in a single classroom.
  • Grounded Theory was tempting, but she wasn’t aiming to build a new theory.
  • Narrative Inquiry was appealing, but she wanted to get at the shared essence of the experience, not just individual stories.

Her eyes stopped on Phenomenology. It was all about describing the essence of an experience from the participants’ perspectives. That was perfect, since her aim was to find out what it felt like to be a first-year teacher in a high-pressure urban environment.

The chapter also reminded her of the data collection tools that would work best. She pictured herself conducting semi-structured interviews, giving teachers space to speak freely but with enough guidance to cover key themes. She’d keep field notes after each conversation, capturing not just the words, but the pauses, the sighs, the expressions.

Jennifer made a determination:
Design: Phenomenology. Data Collection: Semi-structured interviews + field notes.

She paused and glanced at the ethical considerations section of her notes. They included confidentiality, emotional risk, informed consent. She knew teachers might share sensitive feelings about administrators, parents, or students. She’d need to protect their identities and be prepared to connect them with support if the interviews stirred up stress.

Leaning back, Jennifer smiled. For weeks she’d been overwhelmed by choices, but now she felt clarity. This wasn’t just a methodology, it was a match between her problem statement and the kind of understanding she sought. She was ready to tackle writing Chapter One.

Quantitative Research Methods

Quantitative research is a method of inquiry that investigates the cause of a problem by collecting and analyzing numerical data to identify patterns, relationships, or trends (Fraenkel et al., 2019). Unlike qualitative research, which focuses on human experiences and perceptions, quantitative research consists of objective measurement and statistical analysis to test hypotheses or answer specific research questions. It is often used when researchers seek to measure variables, compare groups, or generalize findings to larger populations. This approach typically involves larger sample sizes and structured tools like likert surveys or experiments, aiming to produce results that are reliable, replicable, and generalizable (Gall et al., 2007).

Common Quantitative Research Designs

Experimental. An experimental design tests cause-and-effect relationships by investigating an Independent Variable (the cause, or predictor) and its impact on a Dependent Variable (the effect, or outcome). The experimental approach is best used when attempting to answer a question framed as “How does X affect Y?” where X represents the independent variable and Y represents the dependent variable. It is a valid approach when the researcher has the ability to randomly assign participants to groups. It provides the highest level of control and is considered the gold standard for determining causality when all other variables are removed and the independent variable can be isolated.

Example: A study tests whether a new computer program for Math intervention improves student performance by randomly assigning students to either the computer program group or a traditional method group, then comparing their test scores. In this example, the independent variable is the new computer program; the dependent variable is the test scores of the students who participated in the intervention. To phrase it another way, “How does the Math intervention (x) affect student performance (Y)?”

Quasi-experimental. Similar to experimental design, this approach examines cause and effect relationships but lacks the advantage of random assignment, usually due to research constraints (Fraenkel et al., 2019). It’s often used in real-world settings where full experimental control isn’t possible, and therefore has lower Internal Validity than experimental design. Note that this type of research can be conducted with the same group of subjects using a pretest/posttest approach.

Example: A school with two sixth grade classrooms introduces a mindfulness program to one class but not another, and the researcher compares student stress levels in both classes afterward.

Note that in this example, there are other factors that could influence the students because they are in different classrooms. It’s possible that teacher practices, classroom schedule, the environment, and other variables could be contributing factors that affect students beyond just the mindfulness intervention. That is why this kind of study is said to have lower internal validity.

Correlational. This design investigates whether there is an existing relationship between two or more variables or not. It identifies patterns or associations but cannot determine cause and effect (Gall et al., 2007). Correlational design is limited by the universal understanding that “correlation does not equal causation.” This type of research may be a best first step when the researcher expects a causal relationship, but needs to develop a hypothesis to support it. A correlational study opens the door for future research in an experimental style.

Example: A researcher analyzes data to see if there’s a relationship between eating a healthy breakfast and academic performance among first graders.

Descriptive. This design aims to accurately and systematically describe a population, situation, or phenomenon. It does not assess relationships or test hypotheses, but focuses on observing and reporting what exists (Fraenkel et al., 2019). Like correlational research, it is best used as a first step to gather information that can be evaluated with future research studies and as such, has limited application in dissertation research writing.

Example: A researcher conducts a survey to find out how many hours high school students spend on homework each week.

Quantitative Data Collection Techniques

The most common quantitative research methods include:

Surveys and Questionnaires. Structured tools with closed-ended questions (e.g., multiple choice, Likert scales, yes/no) surveys and questionnaires are designed to gather numerical data from large groups (Fraenkel et al., 2019). They are commonly used to measure attitudes, behaviors, demographics, or opinions. To make data results generalizable and to increase External Validity, it is a best practice to use a Validated Survey Instrument. When possible, researchers should avoid simply writing their own surveys or questionnaires unless they have the wherewithal to put them through a thorough validation process (Fraenkel et al., 2019).

Experiments. In experimental research, data is collected by manipulating one or more variables and observing the effect on another variable (Gall et al., 2007). This is often done in controlled settings to test hypotheses and establish cause-and-effect relationships. Naturally, experiments align with experimental and quasi-experimental research.

Secondary Data Analysis. This involves analyzing existing numerical data collected by other sources, such as government databases, institutional records, or previously conducted surveys (Plano Clark & Creswell, 2015). This method works well when the researcher, due to their position or relationship with an institution, has permission to access the data necessary to complete the study.

Tests and Assessments. Standardized tests or assessments (e.g., academic exams, psychological tests) provide quantitative data related to performance, ability, or outcomes (Gall et al., 2007).

Ethical Considerations in Quantitative Research

Quantitative studies may seem more objective, but they still involve important ethical choices:

  • Use of validated instruments: Researchers must avoid using unreliable or untested surveys that may produce misleading results.
  • Data accuracy and honesty: Falsifying or selectively reporting data violates ethical research standards.
  • Fair sampling: Randomization should not exploit vulnerable populations, and group assignment should not harm participants (Gall et al., 2007).
  • Anonymity and data security: Participant data, even when numerical, must be stored securely and shared responsibly.
  • Informed consent for testing: Participants need to understand what the tests measure and any potential implications.

When to Use Quantitative Methods

Quantitative research is best suited for answering questions that involve measuring variables, testing hypotheses, or identifying patterns, relationships, or trends using numerical data (Fraenkel et al., 2019). These questions focus on outcomes that can be counted, compared, or statistically analyzed. Quantitative studies are especially useful when the goal is to evaluate interventions, compare groups, or generalize findings to a larger population. They are ideal for testing theories, making predictions, or confirming existing knowledge using objective, structured, and whenever possible, standardized methods.

Questions that could be answered with quantitative research may begin with one of the following stems:

  • How many…?
  • To what extent…?
  • What is the relationship between…?
  • How does [variable] affect [outcome]?
  • Is there a significant difference between…?
  • How often does… occur?
  • What factors predict…?
  • What are the trends in…?
  • How effective is… compared to…?

Strengths and Weaknesses of Quantitative Research

Quantitative research has several strengths that make it particularly effective for studying large populations and testing hypotheses, as in experimental and quasi experimental studies. One of its primary advantages is the ability to produce statistically reliable and generalizable results due to the use of structured methods, and large sample sizes (Creswell & Creswell, 2018). The use of validated instruments ensures research objectivity and creates comparability among other studies (Merriam & Tisdell, 2015). Finally, statistical analysis can reveal patterns and trends that may not be immediately evident, making quantitative methods valuable for informing policy, decision-making, and further research (Silverman, 2020).

Despite these strengths, quantitative research has limitations that can affect the depth and relevance of its findings. It often fails to capture the richness and complexity of human experience, as it relies heavily on numerical data and predetermined variables (Denzin & Lincoln, 2018). It can oversimplify social phenomena and may disregard the context in which data is generated (Creswell & Creswell, 2018). Additionally, the rigid design of quantitative studies allows little flexibility once the research is underway, making it difficult to explore unexpected findings (Merriam & Tisdell, 2015); researchers may find themselves “locked in” to their predetermined research approach. When researchers use an unvalidated instrument, participant responses may be influenced by poorly designed survey instruments or limited response options, potentially leading to measurement errors or incomplete insights (Silverman, 2020).

Deciding on a Quantitative Approach

Recall Jennifer’s process and rationale for determing the type of qualitative study she’d pursue. Now consider Marlon, whose problem and thought process led him to a completely different conclusion.

Beginning a Quantitative Journey

Marlon sat in his favorite comfortable chair in the Student Center at Saint Peter’s University, tapping a pencil against his spiral notebook. His dissertation topic had been slowly taking shape for months, and now he was on the verge of nailing it down: Does daily participation in a short mindfulness exercise reduce stress levels among college freshmen during midterm exams?

The question felt exciting, but it also left him at a crossroads. His advisor’s last comment echoed in his mind: If you want to understand the meaning of experiences, go qualitative. If you want to measure and compare, go quantitative.

Marlon knew he didn’t want to just collect stories. He wanted hard numbers. He needed proof that an intervention either worked or didn’t. The “Quantitative Research Methods” section he’d just read was open beside him.

He scanned the section on common designs:

  • Descriptive sounded too basic; it could tell him how stressed students were but not whether the mindfulness exercise made a difference.
  • Correlational could show if stress levels and mindfulness practice were related, but it couldn’t prove cause and effect.
  • Quasi-experimental looked promising, but it lacked random assignment, and Marlon wanted the strongest possible evidence.
  • Experimental jumped off the page, because it allowed random assignment, controlled conditions, and could establish causality.

Marlon pictured the setup: randomly assign half the freshman participants to begin each class with a ten-minute guided mindfulness exercise, while the control group would start class as usual. After four weeks, both groups would complete the same validated stress inventory. The independent variable: the mindfulness exercise. The dependent variable: the measured stress level.

He noted the recommended data collection techniques:

Marlon also contemplated the ethical considerations section. He’d need informed consent so students understood the purpose of the study and the nature of the mindfulness activity. He’d have to store the stress inventory scores securely and keep identities anonymous. And he’d make sure no group was harmed. After all, mindfulness exercises weren’t risky, but participation had to remain voluntary.

By the time he finished sketching his design in the margins of his notebook, Marlon could see the project clearly. The structured approach, the large sample, and the ability to produce generalizable results all pointed in one direction.

Let’s take one last look at the key differences between qualitative and quantitative research before moving on to the hybrid approach known as Mixed Methods.

Aspect Qualitative Research Quantitative Research
Purpose Explore meaning, experiences, and understanding Measure variables, test hypotheses, identify patterns
Type of Data Non-numerical (words, themes, narratives) Numerical (counts, percentages, statistics)
Research Questions Open-ended (“How do people experience…?”) Close-ended (“How many…?”, “How does X affect Y?”)
Data Collection Interviews, focus groups, observations, documents Surveys, experiments, tests, secondary data
Data Analysis Thematic analysis, coding, narrative analysis Statistical analysis (correlations, t-tests, regression)
Sample Size Small, purposefully selected Large, randomly/systematically selected
Approach Inductive (builds theory from data) Deductive (tests theory/hypotheses with data)
Outcomes In-depth understanding, detailed descriptions Generalizable findings, measurable outcomes
Flexibility More flexible, adapts during study More structured, fixed design
Usefulness When little is known, or for complex topics When measuring impact, testing relationships

Mixed Methods Research

What does a researcher do when neither qualitative nor quantitative approaches seem to capture the extent of the context needed to shed insight on a research problem? They may consider taking a mixed methods approach. Mixed methods research combines some aspects of both qualitative and quantitative research to provide a more holistic understanding of a research problem (Creswell & Plano Clark, 2018). By integrating the depth and context of qualitative data with the measurable and generalizable findings of quantitative data, mixed methods research allows researchers to explore complex questions from multiple angles. Researchers using mixed methods may collect data sequentially or simultaneously and aim to draw on the strengths of both approaches to enhance the validity and richness of their findings. Although mixed methods research can provide the most insight addressing a problem statement, its complex and time-consuming nature make it prohibitive for dissertation research. However, an ambitious research student will on occasion endeavor to conduct a mixed methods study.

Common Mixed Methods Research Designs

Explanatory Sequential. This design methodology starts with some manner of quantitative data collection and analysis, followed by gathering qualitative data to help explain or expand on the numerical results.

Example: A researcher conducts a likert scale survey on student satisfaction with a college program, and then follows up with student interviews to explore why the results revealed what they did.

Exploratory Sequential. The inverse of explanatory sequential, this design begins with qualitative data collection, then uses the findings to inform or build a quantitative phase.

Example: A researcher conducts focus groups to identify key concerns among college freshmen. The results are then turned into a likert scale survey administered to a larger population.

Convergent Parallel. In this design, quantitative and qualitative data are collected simultaneously, analyzed separately, and then either compared or combined to draw overall conclusions.

Example: A researcher conducts interviews and distributes surveys at the same time to study teacher burnout, then compares both sets of findings for a fuller picture.

While this text highlights the three most common mixed methods designs for dissertation research, other designs are also recognized in the research methods literature. For example, an Embedded Design places qualitative or quantitative data collection within a larger study of the opposite type, often to provide supplemental insight (e.g., adding a few interviews to contextualize an experiment). A Multiphase Design extends data collection and analysis across several sequential stages, sometimes integrating multiple methods at different points in a long-term project (Creswell & Plano Clark, 2018; Fetters, Curry, & Creswell, 2013).

These additional designs are less common in dissertation work because of their complexity and time demands, but it is helpful to be aware of them so you can recognize their use in the scholarly literature. For most doctoral students, the three primary designs provide more than enough flexibility to match the research questions and scope of a dissertation study.

Mixed Methods Data Collection Techniques

Mixed methods research does not rely on any unique data collection techniques but rather some combination of qualitative and quantitative techniques. As the three designs above explain, qualitative and quantitative techniques can be used in either order or simultaneously to create a bigger picture of the problem or phenomenon studied.

Ethical Considerations in Mixed Methods Research

Because mixed methods combine both qualitative and quantitative elements, researchers must attend to both sets of ethical concerns in addition to:

  • Consistent consent: Participants must understand all phases of the study. If qualitative follow-up is based on a survey, consent must extend to both parts.
  • Data integration: Ensure that neither the narrative nor the numbers are misrepresented when results are merged.
  • Time burden: Because mixed methods often require multiple points of contact, be mindful of participant fatigue or over-involvement.

When to Use Mixed Methods

Mixed methods research is best suited for answering complex questions that require both numerical evidence and in-depth understanding (Creswell & Plano Clark, 2018). It combines the strengths of both qualitative and quantitative approaches to provide a more complete picture of a research problem. Mixed methods are especially useful when one method alone does not fully address the research question, when results need further explanation, or when exploring a topic from multiple perspectives enhances validity and depth.

Questions that could be answered with mixed methods research may begin with one of the following stems:

  • What are the outcomes of… and how do people experience them?
  • How do statistical results align with participants’ perspectives?
  • What explains the differences observed in the data?
  • What patterns exist, and how do participants interpret them?
  • How effective is… and what factors contribute to its success or failure?
  • What does the data show, and what does it mean in context?
  • How do quantitative trends reflect real-life experiences?

Strengths and Weaknesses of Mixed Methods Research

Mixed methods research combines both qualitative and quantitative approaches to capitalize on the strengths of each, offering a comprehensive perspective on research problems. One of its major strengths is Methodological Triangulation, which enhances the validity of findings by comparing and integrating data from different sources and different data collection methods (Creswell & Creswell, 2018). By incorporating both numerical data and narrative accounts, researchers can explore complex questions more fully and answer both “what” and “why” types of inquiries (Merriam & Tisdell, 2015). This approach is especially beneficial in fields like education, health, and social sciences, where understanding both outcomes and lived experiences is critical (Patton, 2015). Furthermore, mixed methods research can improve the practical relevance of findings by providing both empirical evidence and contextual understanding that can inform policy or practice (Denzin & Lincoln, 2018).

Despite these advantages, mixed methods research presents several challenges. These challenges can make a mixed methods approach especially prohibitive for doctoral research. Firstly, it is often time-consuming and resource-intensive. This approach also requires skills in both qualitative and quantitative methodologies, as well as expertise in integrating different types of data (Creswell & Creswell, 2018). Designing a coherent study that successfully aligns both approaches can be difficult, particularly since the philosophical foundations of the methods differ (Silverman, 2020). There is also a risk that one method may dominate the other, leading to an imbalance that weakens the study’s overall contribution (Merriam & Tisdell, 2015). In addition, publishing mixed methods studies can be challenging due to journal space constraints and differing standards for qualitative and quantitative reporting. These limitations highlight the need for careful planning and a clear rationale for using a mixed methods approach; the selection of a mixed method study is not to be taken lightly.

Deciding on a Mixed Methods Approach

We will look at one more fictional students’ experience and methodology choice. How and why does one ultimately decide to commit to a mixed methods study? Sophia’s example shows the thought process.

Arriving at a Mixed Methods Approach

Sophia sat at her kitchen table, sipping coffee as the early morning light fell across her stack of research articles. Her dissertation idea had taken months to shape: What is the impact of peer-mentoring programs on first-generation college students’ academic success?

She’d already read the “Qualitative Research Methods” and “Quantitative Research Methods” sectionss twice. Each had compelling advantages but also gaps that bothered her.

Quantitative methods could give her measurable outcomes: GPA changes, retention rates, and survey scores on academic confidence. Those numbers could show whether peer mentoring “worked.” But they wouldn’t reveal why some students thrived while others dropped out.

Qualitative methods could uncover the heart of the matter: students’ stories, motivations, and the challenges they faced. But without the numbers, she worried her findings might seem too subjective to inform program funding decisions.

Then she opened the “Mixed Methods Research” section.

The description felt like it had been written for her: combine the numerical strength of quantitative with the contextual richness of qualitative. Sophia studied the three designs.

  • Explanatory Sequential: Start with the numbers, then follow up with interviews to explain them.
  • Exploratory Sequential: Begin with interviews to identify themes, then use those to build a large-scale survey.
  • Convergent Parallel: Collect numbers and narratives at the same time, analyze separately, then merge the results.

She imagined her study. A convergent parallel design seemed perfect. She could distribute a validated survey to measure GPA, study habits, and program satisfaction, and conduct semi-structured interviews with a sample of those same students. Then she could compare the patterns in the numbers to the themes in the interviews, looking for alignment or differences worth exploring.

Her notes grew quickly:

  • Quantitative phase: Survey all peer-mentoring participants using validated instruments for academic self-efficacy and engagement.
  • Qualitative phase: Conduct interviews with 15–20 students, selected to represent a range of GPAs and mentoring experiences.
  • Integration: Compare survey trends with interview themes to get both the “what” and the “why.”

The ethical considerations section reminded her she’d need to secure consent for both phases at the start, be transparent about the time commitment, and protect student privacy whether she was handling survey data or interview transcripts. She also flagged the warning about participant fatigue. She’d need to schedule interviews well after surveys to avoid overwhelming students.

Sophia leaned back and furrowed her brow. The project would be more work, because there wasmore data to collect and more analysis to integrate. However, she knew it was the only way to answer her research questions fully.

Closing her notebook, she resolved, “Mixed Methods it is!”

Research Methodology Considerations

Now that you are familiar with the three primary research methods, and have read about three different students’ journeys, you may already find yourself leaning toward one type of research study over the others. Certainly personal preference and your own research strengths will influence your decision when it comes to selecting a methodology, but take into consideration that your problem statement and research questions will also play a significant role determining the appropriateness of your preferred method. A researcher should never bend a methodology to create a research study that doesn’t lend itself to that method; rather, let the study dictate the type of research to be conducted. This section provides additional considerations that will assist you in selecting the best research methodology for your study.

Connecting Your Research Questions to Methods

Selecting appropriate methods for your study begins with a thoughtful evaluation of your research questions, in the case of a qualitative study, or research hypotheses in the case of a quantitative study. Each question or hypothesis should guide and inform decisions about your methodology, ensuring that the approach selected is capable of effectively addressing the questions posed.

Step 1: Evaluate the Nature of Your Research Questions or Hypotheses

Begin by categorizing your research questions according to their purpose:

  • Exploratory questions (e.g., What are the experiences of…?) often indicate a qualitative approach, seeking in-depth understanding of meanings, perceptions, or processes.
  • Explanatory or cause-effect questions (e.g., What is the effect of X on Y?) generally align with quantitative methods that can measure relationships between variables and test hypotheses.
  • Descriptive questions (e.g., How often does X occur?) may lend themselves to either qualitative or quantitative designs, depending on the specificity and depth of information required.

Ask yourself:

  • Is my question open-ended or seeking a definitive answer?
  • Am I trying to understand a phenomenon or measure it?
  • Do I need numerical data, patterns and themes, or both?

Step 2: Match Research Methods to Question Types

Once the intent behind your question is clear, identify which methodological approach provides the best fit. In summary, remember:

  • Qualitative methods (e.g., interviews, focus groups, ethnography) are ideal for exploring lived experiences, uncovering meanings, and examining processes within natural contexts.
  • Quantitative methods (e.g., surveys, experiments, statistical analysis) are suitable for measuring variables, testing hypotheses, and establishing generalizable findings.
  • Mixed methods combine both to address complex questions that benefit from numerical breadth and narrative depth.

Impact on Data Collection and Analysis

Once your research questions and methodology are aligned, the next critical step is understanding how this alignment shapes both the data you collect and the strategies you use to analyze it. Your chosen method directly determines what kind of data you gather, how you gather it, and the type of conclusions you can draw from it (Creswell & Creswell, 2018).

Qualitative Methods: Rich, Descriptive Data

A qualitative approach emphasizes collecting rich, narrative data that provide insight into human experience, meaning, and behavior (Merriam & Tisdell, 2016; Patton, 2015). Data collection typically includes: the methods described early in the chapter:

  • Interviews that explore individual perceptions,
  • Focus groups that reveal group dynamics or shared perspectives,
  • Observations that capture behavior in natural environments,
  • Artifact or document analysis for understanding context and meaning.

These methods aim to generate detailed, context-specific insights. The analysis process is typically inductive, involving:

  • Thematic coding of patterns and categories (Creswell & Creswell, 2018),
    Narrative analysis to interpret participants’ stories (Clandinin & Connelly, as cited in Creswell & Creswell, 2018),
  • Continuous reflection and interpretation by the researcher.

Because qualitative research seeks to explore rather than test, its findings are often not generalizable but instead offer deep understanding of the specific context studied (Denzin & Lincoln, 2018).

Quantitative Methods: Structured, Measurable Data

In quantitative research, data are collected in numerical form to measure variables, identify relationships, or test hypotheses (Fraenkel et al., 2019). Common tools include those mentioned previously:

  • Surveys or questionnaires with closed-ended questions,
  • Standardized tests or assessments,
  • Experiments or quasi-experiments to evaluate cause and effect.

These methods allow for objective measurement and statistical analysis. Typical data analysis includes:

  • Descriptive statistics to summarize data (Gall et al., 2007),
  • Inferential tests (e.g., regression, ANOVA) to examine relationships or differences,
  • Hypothesis testing based on predefined variables (Silverman, 2020).

Quantitative methods are deductive in nature, allowing researchers to draw conclusions from a theory-based framework. Findings are often generalizable to larger populations due to standardized instruments and larger sample sizes (Creswell & Creswell, 2018).

Mixed Methods: Integrating Depth and Breadth

A mixed methods design combines the strengths of both qualitative and quantitative approaches, resulting in more comprehensive and valid findings (Creswell & Plano Clark, 2018). This approach involves collecting two types of data, often in sequential or parallel phases, which significantly impacts data collection:

  • Researchers may need to conduct both surveys and interviews,
  • Data from one phase may inform the next, such as using interview findings to develop a survey (exploratory sequential design),
  • Both sets of data are analyzed independently and then integrated to draw comprehensive conclusions.

Mixed methods analysis includes:

  • Triangulation to cross-validate results (Denzin & Lincoln, 2018),
  • Side-by-side comparisons of qualitative themes and quantitative trends,
  • Development of meta-inferences that merge insights across methods (Creswell & Plano Clark, 2018).

While this approach increases the richness and applicability of research findings, it also requires greater skill, time, and effort to manage and interpret multiple data streams (Silverman, 2020).

Incorporating AI Responsibly: Using AI as a Research Assistant

Instead of thinking of AI as a content generator or as a provider of answers to all questions, think of AI as though it were a research tutor or a writing assistant. It’s a powerful tool if it is used ethically and strategically. Here are some ways you can leverage AI in your exploration of research methods.

Understanding your options. No doubt, you are seeing a lot of new terms and ideas in research strategies. This book provides a high level overview of many of these concepts, but you may still wish to further your understanding, or see more concrete examples to help you visualize how the research methods work. Leverage AI to answer questions like, “How are a case study and a phenomenologial study different,” or “Can you give me an easy to understand example of a quasi-experimental study in education?”

Creating models to follow. AI can generate sample research questions, method descriptions, or even brief paragraphs that serve as a model for you to build from. These examples can spark ideas, help you overcome writer’s block, and give you a head start on drafts you’ll later revise to match your study. For example, if you want to do a quantitative experiment, you could ask “Write a sample paragraph describing a basic experimental design using pre- and post- tests in a higher education context.” Remember, this is not what you would submit for your work, but it can give you ideas and point you in the right direction.

Assist in organization. Deciding on a research method can feel overwhelming with so many parts: research design, participants, data collection, instruments, and analysis…the list goes on and on! AI can help you organize your thinking, suggest section headings, or even re-sequence your outline to improve logical flow. You can treat AI as a proofreader and planning tool with questions such as: “Help me organize the structure of a qualitative methods research project on student engagement using interviews and document analysis.”

Improve clarity and flow. You can ask AI to review a paragraph or section and suggest ways to make it clearer, more concise, or more formal. This is especially helpful in polishing methods descriptions or ensuring your writing meets academic tone and expectations. AI can function like a second pair of eyes that helps you strengthen your sentence structure, eliminate redundancy, or improve transitions. You can ask questions like “Can you revise this paragraph to make it sound more formal and scholarly?”

Remember, though, YOU are the scholar; you are the researcher. Even though you use AI as a writing assistant, remember, your dissertation must reflect:

  • YOUR decisions about which approach best answers your research questions
  • YOUR rationale for choosing particular tools and techniques
  • YOUR understanding of ethical and methodological issues
  • YOUR ability to explain how your study is grounded in research theory and practice

Only you can make and defend those choices. A strong dissertation isn’t just technically correct; it demonstrates ownership, insight, and scholarly reasoning. AI can help you get there, but it cannot do it for you.

Conversation Topics for Your Mentor

A student beginning the dissertation process should engage their mentor in discussions that clarify, refine, and support the foundation of their study. Here are recommended conversation topics you may wish to bring up after reading this chapter:

Clarifying the Research Problem and Purpose

Discussing the clarity and scope of the research problem will help ensure alignment between the study’s focus and the appropriate methodology.

“Is my research problem clearly defined and significant enough for a dissertation?”

“Does my problem statement logically lead to the type of research questions I’m drafting?”

Formulating Research Questions or Hypotheses

The student should seek feedback on whether their questions are feasible, well-structured, and answerable within the scope of a doctoral project.

“Have I correctly formed research questions or hypotheses that align with qualitative, quantitative, or mixed methods?”

“Should I revise my questions to better suit a specific research method?”

Choosing a Research Methodology

This conversation can help ensure the method fits the questions, rather than the other way around, as emphasized in this chapter.

“Which methodology is most appropriate for my focus?

“Given my topic and goals, would a qualitative, quantitative, or mixed methods approach be strongest?”

Planning for Data Collection and Analysis

This will help the student understand the practical implications of their methodology, especially regarding scope, timeline, and tools required.

“Is my identified population going to be accessible and reasonable to work with?”

“What data collection techniques will work best given my questions and population?”

“How should I plan my data analysis approach based on my method and framework?”

Structuring Chapter One

These questions ensure that the student starts building their dissertation according to institutional expectations and the guidance in the Saint Peter’s University Dissertation Handbook.

“How should I begin to organize my information for Chapter One?”

“What citations should I include to provide adequate background information in my introduction without going too far into my topic?”

Suggested Writing Activities

Before embarking on drafting the research methodology sections of your dissertation, you may want to sit down and do some prewriting exercises to target your approach. Here are some suggestions that may help you focus your writing.

Three Column Charts

Make a three column chart labeled Qualitative/Quantitative/Mixed Methods. Write as many potential research questions you can think of about your research topic, and then categorize them by which research method makes the most sense. Where do most of your questions fall?

Alternatively, make a three column chart labeled Qualitative/Quantitative/Mixed Methods. This time, intentionally write three research questions about your topic for each one of the three major methods. Which questions were easiest to write and align? Which questions were the hardest to write? Which questions represent most closely what you would like to study?

What Do I Want to Know?

Set a timer for 10 minutes. Without editing or concern about grammar and mechanics, freewrite a response to the prompt:

“What am I trying to understand, measure, or explain through my research?”

When time is up, use different color highlighters to code any words, questions, or phrases that suggest a particular type of research method (qualitative, quantitative, or mixed). Which comes up the most often? Which reflects what you want to know the most accurately?

Key Takeaways

  • Research methods should fit your questions – the best approach (qualitative, quantitative, or mixed) depends on what you’re trying to understand or measure.

  • Qualitative and quantitative methods offer different lenses – one captures meaning and experience; the other captures patterns and numbers. Both are valid and useful in different contexts.

  • Mixed methods combine the strengths of both approaches – offering a fuller picture but requiring more time, skill, and planning.

  • The method you choose shapes how you collect and analyze data – from interviews to surveys to statistical tests, each tool reflects the goals of the study.

  • Strong research begins with alignment – your problem statement, research questions, and method must work together to guide meaningful inquiry.

In the next  chapter, you will look at the parts of Chapter One of qualitative and quantitative studies.

Understanding the strengths and limitations of each methodology allows you to select an approach that fits your research questions. The next step is to formally articulate your research problem and the procedures you will use to study it. The next chapter will show you how to align your problem statement, research questions, and methodology into a coherent plan for your dissertation study.

References

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.

Denzin, N. K., & Lincoln, Y. S. (Eds.). (2018). The SAGE handbook of qualitative research (5th ed.). SAGE Publications.

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (10th ed.). McGraw-Hill Education.

Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Pearson Education.

Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.

Mertens, D. M. (2020). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods (5th ed.). SAGE Publications.

Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). Sage Publications.

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.

Plano Clark, V. L., & Creswell, J. W. (2015). Understanding research: A consumer’s guide (2nd ed.). Pearson.

Silverman, D. (2020). Doing qualitative research (6th ed.). SAGE Publications.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.

definition