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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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5 Examples of Qualitative Data in Education

Qualitative data is a game-changer in education, unlocking valuable insights into students’ experiences, perspectives, and behaviors. Capturing the richness and depth of human experiences empowers educators to make informed decisions, revamp teaching strategies, and shape impactful school policies.

In this article, we’ll explore five examples of qualitative data in education, unraveling their significance and tackling potential challenges along the way. Let’s dive in!

• Understanding Qualitative Data in Education
• Examples of Qualitative Data in Education
• The Role of Qualitative Data in Educational Improvement
• Challenges in Collecting and Analyzing Qualitative Data

1.  Understanding Qualitative Data in Education

Before we delve into the examples, let’s first define what qualitative data entails. In education, qualitative data refers to non-numerical and descriptive information gathered through interviews, observations, surveys, or the analysis of documents and artifacts. Its purpose is to understand the nuances and complexities of educational phenomena, going beyond surface-level statistics.

Qualitative data encompasses a wide range of sources, such as open-ended survey responses, transcripts of interviews, and field notes from classroom observations. It provides detailed accounts of individuals’ thoughts, feelings, and experiences, shedding light on the why and how of educational processes.

For example, in a study on student motivation, qualitative data may include interviews with students where they express their personal goals, aspirations, and challenges. These insights can help educators understand the underlying factors that drive or hinder student engagement and achievement.

Read next: How data analytics is reshaping the education industry

The Importance of Qualitative Data in Education

Qualitative data complements quantitative data , providing a holistic view of students’ learning journeys. It helps educators understand the contextual factors influencing student performance, identify patterns and trends, and gain deeper insights into the effectiveness of educational interventions.

For instance, when analyzing the impact of a new teaching method, qualitative data can reveal the specific strategies or activities that resonate with students, leading to improved learning outcomes. This information can guide educators in refining their instructional practices and tailoring their approaches to meet the diverse needs of their students.

Moreover, qualitative data allows us to give voice to marginalized groups, acknowledging their unique perspectives and experiences. It fosters inclusivity and helps educators create equitable learning environments.

For example, through qualitative research, educators can explore the experiences of students from underrepresented communities and gain a deeper understanding of the barriers they face. This knowledge can inform the development of targeted interventions and support systems to address these challenges and promote educational equity.

2.  Examples of Qualitative Data in Education

In this section, we’ll take a look at five examples of qualitative data in education.

• Observational data in classroom settings
• Interview and survey responses from students
• Teachers’ reflective journal entries
• Parent-teacher meeting notes
• Student artifacts and portfolios

Each of these types of data in education tell us something different. Let’s take a closer look!

Observational Data in Classroom Settings

Observational data involves systematically observing students’ behaviors and interactions within the classroom. It provides valuable insights into students’ engagement levels, social dynamics, and learning styles. By observing how students respond to different teaching strategies, educators can refine their approaches and tailor instruction to individual needs.

For example, observing a student struggling with a particular concept can prompt educators to provide targeted support or adapt instructional materials, ensuring every student has a fair chance to succeed.

Furthermore, through careful observation, educators can also identify patterns of behavior that may indicate underlying issues affecting student learning. For instance, noticing that a group of students consistently disengages during a specific activity may prompt educators to investigate potential barriers to their participation and implement strategies to address them.

Observational data can also shed light on the social dynamics within the classroom. By observing students’ interactions, educators can gain insights into the formation of peer groups, the emergence of leadership roles, and the impact of these dynamics on learning outcomes. This information can inform educators’ decisions on group assignments, seating arrangements, and fostering a positive classroom culture.

Interview and Survey Responses from Students

Conducting interviews and collecting survey responses directly from students allows educators to understand their opinions, beliefs, and experiences. This qualitative data provides a nuanced understanding of student motivation, attitudes towards learning, and the impact of educational practices on their lives.

Through interviews and surveys, educators can identify students’ strengths and challenges, gather feedback on classroom experiences, and tailor their teaching to better meet students’ needs. It also encourages student agency, as they feel heard and valued in shaping their own educational journey.

Moreover, interviews and surveys can uncover valuable insights about students’ interests and aspirations beyond the classroom. By understanding their extracurricular activities, hobbies, and future goals, educators can make connections between students’ personal interests and the curriculum, creating more meaningful and engaging learning experiences.

Additionally, interviews and surveys can be used to explore students’ perspectives on broader social issues, such as diversity, equity, and inclusion. By giving students a platform to share their thoughts and experiences, educators can foster empathy, understanding, and a sense of belonging within the classroom.

Teachers’ Reflective Journal Entries

Reflective journals, written by teachers, provide a unique window into their thoughts, emotions, and reflections on their teaching practices. These personal accounts offer valuable qualitative data that helps educators examine their teaching strategies and goals, analyze the impact of their actions, and make informed decisions to improve student learning.

By critically reflecting upon their own practice, teachers can identify areas for growth, address challenges, and share successful strategies with colleagues. Reflective journals foster a culture of continuous professional development and collaboration among educators.

In addition to self-reflection, teachers’ reflective journal entries can also capture the evolving nature of the teaching profession. They can document the changing educational landscape, the integration of technology, and the impact of policy decisions on classroom practices. These insights can contribute to broader discussions on educational reform and inform decision-making at the institutional level.

Furthermore, teachers’ reflective journal entries can serve as a source of inspiration and support for other educators. By sharing their experiences, challenges, and triumphs, teachers can create a community of practice that promotes innovation, resilience, and growth.

Parent-Teacher Meeting Notes

During parent-teacher meetings, qualitative data is gathered through conversations and notes taken by educators. These insights highlight parents’ perspectives on their child’s learning, their concerns, and aspirations. Understanding parental views is essential in fostering effective home-school partnerships.

By actively listening and engaging with parents, educators can tailor their support, provide resources, and build trust. Collaborating with parents helps create a holistic understanding of students’ needs and enables educators to create learning environments that align with students’ home lives.

Parent-teacher meeting notes can also reveal valuable information about students’ learning preferences, family dynamics, and cultural backgrounds. This knowledge can guide educators in designing culturally responsive instruction and creating inclusive classroom environments that celebrate diversity.

Furthermore, parent-teacher meetings provide an opportunity for educators to share insights and strategies for supporting student learning at home. By establishing open lines of communication, educators and parents can work together to reinforce learning goals, address challenges, and celebrate student achievements.

Student Artifacts and Portfolios

Student artifacts, such as essays, artwork, or projects, provide qualitative evidence of students’ learning and growth. Portfolios, in particular, are collections of student work that showcase their progress over time. These tangible representations illustrate students’ knowledge, skills, and creativity.

By analyzing student artifacts and portfolios, educators can gain insights into individual strengths, challenges, and areas for improvement. They can also identify patterns across multiple students, informing instructional decisions and shaping curriculum design.

Moreover, student artifacts and portfolios can serve as a catalyst for self-reflection and goal-setting. By reviewing their own work, students can identify areas of growth, set targets for improvement, and take ownership of their learning journey. This metacognitive process enhances students’ self-awareness and empowers them to become active participants in their education.

Student artifacts and portfolios can also be used as a means of assessment beyond traditional tests and quizzes. By evaluating the quality and depth of students’ work, educators can gain a more comprehensive understanding of their learning outcomes and provide targeted feedback for further improvement.

Furthermore, student artifacts and portfolios can be shared with peers, parents, and the wider community, fostering a sense of pride and accomplishment. This public display of students’ work promotes a positive learning culture and encourages students to strive for excellence.

3.  The Role of Qualitative Data in Educational Improvement

Informing teaching strategies.

Qualitative data helps educators tailor their teaching strategies to meet students’ diverse needs. By understanding students’ preferences, learning styles, and perceptions of their classroom experiences, educators can create engaging and inclusive learning environments.

For instance, qualitative data might reveal that students respond well to hands-on activities or small group discussions. Armed with this knowledge, educators can integrate these strategies into their lessons, maximizing student engagement and learning outcomes.

Enhancing Student Engagement

Qualitative data allows educators to uncover factors that influence student engagement, such as classroom environment, relationships with peers and teachers, and the relevance of the curriculum to students’ lives. This information enables educators to create meaningful and culturally responsive learning experiences.

By actively involving students in the design of their educational journey and incorporating their voices and interests, educators can promote a sense of ownership and foster intrinsic motivation, leading to increased student engagement and achievement.

Shaping School Policies

Qualitative data has a powerful role to play in shaping school policies and decision-making processes. It provides rich insights into students’ experiences, perceptions of the learning environment, and the impact of educational practices on their overall well-being.

By utilizing qualitative data, educators and policymakers can identify areas of improvement, address systemic inequities, and advocate for change. By considering multiple perspectives and experiences, schools can create policies that meet the needs of all students.

To learn more, read our complete guide to using qualitative data to improve student outcomes.

4.  Challenges in Collecting and Analyzing Qualitative Data

Ensuring data authenticity.

One of the challenges with qualitative data is ensuring its authenticity and credibility. Researchers and educators must be vigilant when collecting and analyzing data, employing rigorous methodologies and strategies to minimize bias and subjectivity.

By implementing clear protocols, involving multiple data sources, and engaging in reflexivity, educators can enhance the trustworthiness and reliability of qualitative data, leading to more accurate insights into students’ experiences.

Dealing with Subjectivity

Qualitative data, by its nature, is subjective. It relies on individual interpretations and perspectives. While this subjectivity can provide rich insights, it also poses challenges when comparing or generalizing findings across different contexts or individuals.

To address this challenge, researchers and educators employ triangulation, seeking multiple corroborating sources of evidence, such as interviews, observations, and documents. This helps build a comprehensive and nuanced understanding of complex educational phenomena.

Time and Resource Constraints

Collecting and analyzing qualitative data can be time-consuming and resource-intensive. Conducting interviews, observations, or analyzing reflective journals requires careful planning, organization, and sufficient resources.

Despite these constraints, the value of qualitative data in education cannot be understated. Administrators must prioritize allocating time and resources to support educators in collecting and analyzing qualitative data, recognizing its significant contribution to educational improvement.

Examples of qualitative data in education provide crucial insights into students’ experiences, perspectives, and needs. By embracing qualitative data in research and practice, educators can create transformative educational experiences that empower students, foster their academic and personal growth, and build inclusive learning communities.

Understanding qualitative data is only half the battle; presenting it in a manner that drives decision-making is equally crucial. After exploring these five examples, why not take the next step with our Inno™ Starter Kits ? They’re tailored for educators looking to seamlessly plug in their student data , offering clear visual representations that illuminate student experiences and insights. Dive deeper, connect the dots, and use your data to shape the future of education with the Inno™ Starter Kits.

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Examples of Qualitative Data in Education: How to Use

The phrase “Examples of Qualitative Data in Education” reveals rich and detailed knowledge in the field of education, where understanding fuels creativity and insights drive advancement. While quantitative data frequently grabs the spotlight, its qualitative counterpart is just as important for understanding how complicated learning environments work.

In this blog, we will delve into the significance of qualitative data in education and provide a range of examples showcasing its applications in fostering educational excellence.

What is qualitative data?

Qualitative data is a type of data that is open to interpretation and can be used in a variety of ways- both as a measure of quality and as the basis for analysis. 

It describes the way things are and tells you why something is happening, rather than what is happening (for example, if a student isn’t doing well in math, qualitative data would tell you their reasons for this), rather than describing its characteristics or how much of it there is. 

Qualitative data is not numerical and does not have a set meaning, which makes it difficult to analyze. Understanding how to use qualitative data collection in education effectively can be crucial for educational institutions.

LEARN ABOUT: Qualitative Interview

Using qualitative data in educational settings

Qualitative data provides insight into the learning experience that cannot always be expressed through numbers. It allows you to better understand how students learn by asking open-ended questions and listening carefully to their answers. 

When you use qualitative data, you can investigate particular areas of concern for your organization and formulate action plans as needed. Also, qualitative data addresses a number of the shortcomings of quantitative research. 

For instance, quantitative data can indicate that a particular school district’s test scores have outperformed those of other regional school districts but cannot explain why this is the case.

Examples of qualitative data in education

Here are some examples of qualitative data in education:

Field observations

Teachers and administrators could observe the classroom during different times of day, at different points during the year, or when a special event is happening.

Documentary research

School organizations can spend time looking closely at their current documents to learn more about students.

Focus groups

Conducting focus group discussions with students, teachers, or parents can provide qualitative insights into their perceptions, experiences, and opinions related to educational practices and policies.

Student portfolios

Reviewing student portfolios that showcase their work, assignments, and projects over time offers qualitative data on their progress, growth, and learning journey.

Peer review and feedback

Encouraging students to provide peer reviews and feedback on each other’s work generates qualitative data on their ability to critically assess and provide constructive input.

Learning diaries

Similar to journals, learning diaries encourage students to document their daily experiences, challenges, and triumphs, offering qualitative insights into their engagement and progress.

Parent-teacher conferences

Conversations during parent-teacher conferences provide qualitative data about a student’s strengths, weaknesses, and overall development from both home and school perspectives.

Online discussion forums

Analyzing interactions on online platforms where students and educators discuss topics related to coursework offers qualitative insights into their understanding, questions, and collaboration and is one of the best examples of qualitative data in education.

Classroom artifacts

Examining classroom artifacts like bulletin boards, student artwork, and project displays provides qualitative data on the learning environment, student creativity, and the integration of various subjects.

Audio and video recordings

Recording classroom discussions, presentations, or group activities captures qualitative data on communication skills, collaboration, and the depth of student understanding.

Student surveys with open-ended questions

Incorporating open-ended questions into student surveys enables them to express their thoughts, opinions, and suggestions in their own words, yielding qualitative data that complements quantitative results.

Teacher reflective journals

Teachers maintaining reflective journals about their teaching experiences, challenges, and innovative approaches generate qualitative data on professional growth and instructional strategies.

Student interviews

One-on-one interviews with students are one of the most common examples of qualitative data in education. It can provide qualitative insights into their learning experiences, interests, and motivations, helping educators tailor instruction to individual needs.

How can a survey tool help with qualitative data analysis in education?

A survey tool is a useful research tool that can help with qualitative data analysis in education. Qualitative data is best analyzed through close inspection and asking questions to understand the root causes of phenomena, but this is a time-consuming process. 

A well-designed survey questionnaire can simplify the qualitative analysis by giving you insight into what most concerns your group and helping you to prioritize your responses.

Key steps to using a survey tool: In order to successfully use a survey tool, you’ll need to:

Define your goal

What are you trying to accomplish? If you don’t know where you want the findings of your Qualitative research project to lead, it will be difficult for people to provide feedback and difficult for you to analyze the results.

Choose your qualitative research method

What are your options? How will people be invited to give feedback, and where will this feedback come from? Identifying how participants/respondents/users will be asked about their experiences is an important first step.

Design survey questions

Craft thoughtful and relevant survey questions that align with your research goals. Ensure a mix of closed-ended questions to gather quantitative data and open-ended questions to gather qualitative insights. 

Use clear and concise language to avoid ambiguity, and consider using skip logic or branching to tailor the survey experience based on participants’ responses. Well-designed questions will make the data analysis process smoother.

Distribute and collect responses

Utilize the survey tool to distribute the survey to your target audience, whether it’s students, teachers, parents, or administrators. You can use various distribution channels such as email, social media, or school websites. 

Track the responses as they come in and monitor the data collection process. Keep the survey open for an appropriate amount of time to ensure a diverse range of responses.

Analyze qualitative data

Once you’ve collected a sufficient number of responses, begin the qualitative data analysis process. Start by categorizing and coding open-ended responses. Look for recurring themes, patterns, and trends within the qualitative data. 

You can use tools like thematic analysis to identify key themes that emerge from participants’ responses. Software like NVivo or even Excel can help organize and analyze qualitative data effectively.

Triangulate with quantitative data

If your survey included closed-ended questions with quantitative responses (e.g., Likert scales), you can enrich your analysis by comparing qualitative insights with quantitative data. This triangulation can provide a more comprehensive understanding of the research topic. 

For example, if participants express negative sentiments about a particular aspect of education, you can cross-reference this sentiment with the corresponding quantitative rating to see if there’s a correlation.

LEARN ABOUT: Steps in Qualitative Research

Methods to analyze qualitative data

Qualitative research methods form the cornerstone of understanding human experiences, utilizing an array of data collection methods to delve into the nuances of perspectives. From interviews and focus groups to ethnographic studies and content analysis, these qualitative methods harmonize to reveal the intricate tapestry of human narratives. 

Now, we will unveil how these methods converge, creating a symphony of insights that deepen our comprehension of the human condition.

Content analysis

The content of the data is analyzed by scrutinizing and interpreting texts, pictures, video, audio, and other materials. This involves looking at the words in a document, for example, and deciding their meaning.

Grounded theory

To create a grounded theory, you study what is happening in a particular situation and try to formulate a theory about why it happens. This process often begins with an initial assumption or question, which will be tested out over time. For example: “How do we know when this analysis process is finished?”

Phenomenology

Phenomenology looks at experiences from the perspective of those who experience them. It tries to understand what these experiences mean to people rather than the events themselves. This is relevant for understanding students’ learning experiences in an educational setting.

Framework analysis

Framework analysis is a conversation with participants and then using the content of that discussion to analyze the data. It could involve asking individuals, “What was the knowledge you gained from this project?” and then anonymizing their answers in order to avoid starting your article with personal stories.

Discourse analysis

Discourse analysis looks at how individuals use language and what the implications of those uses are. This can be helpful in a classroom setting where students use their voices to express themselves about their learning process within the walls of academia.

Interpretative phenomenological analysis

Interpretive phenomenological analysis (IPA) helps you understand that qualitative data is a type of open-ended and interpretable data that can be used in various ways. Whether you’re trying to learn more about your customer’s experiences or the educational process, qualitative analysis will help get insights into what’s important for your project.

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Qualitative data in education is a treasure trove of insights that empowers educators and administrators to create holistic learning experiences. 

By leveraging methods such as interviews, observations, and reflections, educational institutions can gain a deeper understanding of student needs, teaching strategies, and program effectiveness. The fusion of quantitative and qualitative data enriches decision-making processes and paves the way for continuous improvement in education.

If you want help analyzing the qualitative aspects of your research projects, we’re here to provide assistance with our survey tool ! Let us know if there are any other information needs, and we’ll work on providing an answer as soon as possible.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Quantitative vs. Qualitative Research Design: Understanding the Differences

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

• often used in fields related to education, sociology and anthropology; 
• designed to arrive at conclusions regarding social phenomena; 
• focused on data-gathering techniques like interviews, focus groups or case studies; 
• dedicated to perpetuating a flexible, adaptive approach to data gathering;
• known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

• you work in a field concerned with enhancing humankind through the lens of social change;
• your research focuses on understanding complex social trends and individual perceptions of those trends;
• you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

• often used in fields related to education, data analysis and healthcare; 
• designed to arrive at numerical, statistical conclusions based on objective facts;
• focused on data-gathering techniques like experiments, surveys or observations;
• dedicated to using mathematical principles to arrive at conclusions;
• known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

• you work in a field concerned with analyzing data to inform decisions;
• your research focuses on studying relationships between variables to form data-driven conclusions;
• you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

At Peabody College, you will experience

• an inclusive, welcoming community of like-minded professionals;
• the guidance of expert faculty with real-world industry experience;
• opportunities for valuable, hands-on learning experiences,
• the option of specializing depending on your specific area of interest.

Explore our monthly publication — Ideas in Action — for an inside look at how Peabody College translates discoveries into action.

Please click below to explore a few of the graduate degrees offered at Peabody College:

• Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
• Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
• Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
• Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

Connect with the Community of Professionals Seeking to Enhance Humankind at Peabody College

At Peabody College, we equip you with the marketable, transferable skills needed to secure a valuable career in education and beyond. You will emerge from the graduate program of your choice ready to enhance humankind in more meaningful ways than you could have imagined.

If you want to develop the sought-after skills needed to be a force for change in the social and educational spaces, you are in the right place .

We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

Prevent plagiarism, run a free check.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Raimo Streefkerk