examples of qualitative data in education

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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.

Learn more about academic surveys here

Learn more about academic research services for students here

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 research in education : Background information

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Home » Qualitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Educational Research Basics by Del Siegle

Qualitative research.

Although researchers in anthropology and sociology have used the approach known as qualitative research  for a century, the term was not used in the social sciences until the late 1960s. The term qualitative research is used as an umbrella term to refer to several research strategies. Five common types of qualitative research are grounded theory , ethnographic , narrative research , case studies , and phenomenology.

It is unfair to judge qualitative research by a quantitative research paradigm, just as it is unfair to judge quantitative research from the qualitative research paradigm .

“Qualitative researchers seek to make sense of personal stories and the ways in which they intersect” (Glesne & Peshkin, 1992). As one qualitative researcher noted, “I knew that I was not at home in the world of numbers long before I realized that I was at home in the world of words.”

The data collected in qualitative research has been termed “soft”, “that is, rich in description of people, places, and conversations, and not easily handled by statistical procedures.” Researchers do not approach their research with specific questions to answer or hypotheses to test. They are concerned with understanding behavior from the subject’s own frame of reference. Qualitative researcher believe that “multiple ways of interpreting experiences are available to each of us through interacting with others, and that it is the meaning of our experiences that constitutes reality. Reality, consequently,  is ‘socially constructed'” (Bogdan & Biklen, 1992).

Data is usually collected through sustained contact with people in the settings where they normally spend their time. Participant observations and in-depth interviewing are the two most common ways to collect data. “The researcher enters the world of the people he or she plans to study, gets to know, be known, and trusted by them, and systematically keeps a detailed written record of what is heard and observed. This material is supplemented by other data such as [artifacts], school memos and records, newspaper articles, and photographs” (Bogdan & Biklen, 1992).

Rather than test theories, qualitative researchers often inductively analyze their data and develop theories through a process that Strauss called ” developing grounded theory “. They use purposive sampling to select the people they study. Subjects are selected because of who they are and what they know, rather than by chance.

Some key terms:

Access to a group is often made possible by a gate keeper . The gate keeper is the person who helps you gain access to the people you wish to study. In a school setting it might be a principal.

Most qualitative studies involve at least one key informant . The key informant knows the inside scoop and can point you to other people who have valuable information. The “key informant” is not necessarily the same as the gate keeper. A custodian might be a good key informant to understanding faculty interactions. The process of one subject recommending that you talk with another subject is called “ snowballing .”

Qualitative researchers use rich-thick description when they write their research reports. Unlike quantitative research where the researcher wished to generalize his or her findings beyond the sample from whom the data was drawn, qualitative researcher provide rich-thick descriptions for their readers and let their readers determine if the situation described in the qualitative study applies to the reader’s situation. Qualitative researchers do not use the terms validity and reliability. Instead they are concerned about the trustworthiness of their research.

Qualitative researchers often begin their interviews with grand tour questions . Grand tour questions are open ended questions that allow the interviewee to set the direction of the interview. The interviewer then follows the leads that the interviewee provides. The interviewer can always return to his or her preplanned interview questions after the leads have been followed.

Qualitative researchers continue to collect data until they reach a point of data saturation . Data saturation occurs when the researcher is no longer hearing or seeing new information. Unlike quantitative researchers who wait until the end of the study to analyze their data, qualitative researcher analyze their data throughout their study.

Note:   It is beyond the scope of this course to provide an extensive overview of qualitative research. Our purpose is to make you aware of this research option, and hopefully help you develop an appreciation of it. Qualitative research has become a popular research procedure in education.

Del Siegle, PhD [email protected] www.delsiegle.info

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5 Collecting Data in Your Classroom

ESSENTIAL QUESTIONS

• What sort of methodological considerations are necessary to collect data in your educational context?
• What methods of data collection will be most effective for your study?
• What are the affordances and limitations associated with your data collection methods?
• What does it mean to triangulate data, and why is it necessary?

As you develop an action plan for your action research project, you will be thinking about the primary task of conducting research, and probably contemplating the data you will collect. It is likely you have asked yourself questions related to the methods you will be using, how you will organize the data collection, and how each piece of data is related within the larger project. This chapter will help you think through these questions.

Data Collection

The data collection methods used in educational research have originated from a variety of disciplines (anthropology, history, psychology, sociology), which has resulted in a variety of research frameworks to draw upon. As discussed in the previous chapter, the challenge for educator-researchers is to develop a research plan and related activities that are focused and manageable to study. While human beings like structure and definitions, especially when we encounter new experiences, educators-as-researchers frequently disregard the accepted frameworks related to research and rely on their own subjective knowledge from their own pedagogical experiences when taking on the role of educator-researcher in educational settings. Relying on subjective knowledge enables teachers to engage more effectively as researchers in their educational context. Educator-researchers especially rely on this subjective knowledge in educational contexts to modify their data collection methodologies. Subjective knowledge negotiates the traditional research frameworks with the data collection possibilities of their practice, while also considering their unique educational context. This empowers educators as researchers, utilizing action research, to be powerful agents for change in educational contexts.

Thinking about Types of Data

Whether the research design is qualitative, quantitative or mixed-methods, it will determine the methods or ways you use to collect data. Qualitative research designs focus on collecting data that is relational, interpretive, subjective, and inductive; whereas a typical quantitative study, collects data that are deductive, statistical, and objective.

In contrast, qualitative data is often in the form of language, while quantitative data typically involves numbers. Quantitative researchers require large numbers of participants for validity, while qualitative researchers use a smaller number of participants, and can even use one (Hatch, 2002). In the past, quantitative and qualitative educational researchers rarely interacted, sometimes holding contempt for each other’s work; and even published articles in separate journals based on having distinct theoretical orientations in terms of data collection. Overall, there is a greater appreciation for both quantitative and qualitative approaches, with scholars finding distinct value in each approach, yet in many circles the debate continues over which approach is more beneficial for educational research and in educational contexts.

The goal of qualitative data collection is to build a complex and nuanced description of social or human problems from multiple perspectives. The flexibility and ability to use a variety of data collection techniques encompasses a distinct stance on research. Qualitative researchers are able to capture conversations and everyday language, as well as situational attitudes and beliefs. Qualitative data collection is able to be fitted to the study, with the goal of collecting the most authentic data, not necessarily the most objective. To researchers who strictly use quantitative methods, qualitative methods may seem wholly unstructured, eclectic, and idiosyncratic; however, for qualitative researchers these characteristics are advantageous to their purpose. Quantitative research depends upon structure and is bounded to find relationship among variables and units of measurement. Quantitative research helps make sense of large amounts of data. Both quantitative and qualitative research help us address education challenges by better identifying what is happening, with the goal of identifying why it is happening, and how we can address it.

Most educator-researchers who engage in research projects in schools and classrooms utilize qualitative methodologies for their data collection. Educator-researchers also use mixed methods that focus on qualitative methods, but also use quantitative methods, such as surveys, to provide a multidimensional approach to inquiring about their topic. While qualitative methods may feel more comfortable, there is a methodological rationale for using quantitative research.

Research methodologists use two distinct forms of logic to describe research: induction and deduction. Inductive approaches are focused on developing new or emerging theories, by explaining the accumulation of evidence that provides meaning to similar circumstances. Deductive approaches move in the opposite direction, and create meaning about a particular situation by reasoning from a general idea or theory about the particular circumstances. While qualitative approaches are inductive – observe and then generate theories, for example – qualitative researchers will typically initiate studies with some preconceived notions of potential theories to support their work.

Flexible Research Design

A researcher’s decisions about data collection and activities involve a personal choice, yet the choice of data sources must be responsive to the proposed project and topic. Logically, researchers will use whatever validated methods help them to address the issue they are researching and will develop a research plan around activities to implement those methods. While a research plan is important to conducting valid research in schools and classrooms, a research plan should also be flexible in design to allow data to emerge and find the best data to address research questions. In this way, a research plan is recommended, but data collection methods are not always known in advance. As you, the educator-researcher, interacts with participants, you may find it necessary to continue the research with additional data sources to better address the question at the center of your research. When educators are researchers and a participant in their study, it is especially important to keep an open mind to the wide range of research methodologies. All-in-all educator-researchers should understand that there are varied and multiple paths to move from research questions to addressing those questions.

Mixed Methods

As mentioned above, mixed methods is the use of both qualitative and quantitative methods. Researchers generally use mixed methods to clarify findings from the initial method of data collection. In mixed-methods research, the educator-researcher has increased flexibility in data collection. Mixed methods studies often result in a combination of precise measurements (e.g., grades, test scores, survey, etc.) along with in-depth qualitative data that provide meaningful detail to those measurements. The key advantage of using mixed methods is that quantitative details enhance qualitative data sources that involve conclusions and use terms such as usually, some, or most which can be substituted with a number or quantity, such as percentages or averages, or the mean, the median, and/or the mode. One challenge to educator-researchers is that mixed methods require more time and resources to complete the study, and more familiarity about both qualitative and quantitative data collection methods.

Mixed methods in educator research, even if quantitative methods are only used minimally, provide an opportunity to clarify findings, fill gaps in understanding, and cross-check data. For example, if you are looking at the use of math journals to better engage students and improve their math scores, it would be helpful to understand their abilities in math and reading before analyzing the math journals. Therefore, looking at their test scores might give you some nuanced understanding of why some students improved more than others after using the math journals. Pre- and post-surveys would also provide valuable information in terms of students’ attitudes and beliefs about math and writing. In line with thinking about pre- and post-surveys, some researchers suggest using either qualitative or quantitative approaches in different phases of the research process. In the previous example, pre- and post test scores may quantitatively demonstrate growth or improvement after implementing the math journal; however, the qualitative data would provide detailed evidence as to why the math journals contributed to growth or improvement in math. Quantitative methods can establish relationships among variables, while qualitative methods can explain factors underlying those same relationships.

I caution the reader at this point to not simply think of qualitative methodologies as anecdotal details to quantitative reports. I only highlight mixed methods to introduce the strength of such studies, and to aid in moving educational research methodology away from the binary thinking of quantitative vs. qualitative. In thinking about data collection, possible data sources include questionnaires or surveys, observations (video or written notes), collaboration (meetings, peer coaching), interviews, tests and records, pictures, diaries, transcripts of video and audio recordings, personal journals, student work samples, e-mail and online communication, and any other pertinent documents and reports. As you begin to think about data collection you will consider the available materials and think about aspects discussed in the previous chapter: who, what, where, when, and how. Specifically:

• Who are the subjects or participants for the study?
• What data is vital evidence for this study?
• Where will the data be collected?
• When will the data be collected?
• How will the data be collected?

If you find you are having trouble identifying data sources that support your initial question, you may need to revise your research question – and make sure what you are asking is researchable or measurable. The research question can always change throughout the study, but it should only be in relation the data being collected.

Participant Data

As an educator, your possible participants selection pool is narrower than most researchers encounter – however, it is important to be clear about their role in the data design and collection. A study can involve one participant or multiple participants, and participants often serve as the primary source of data in the research process. Most studies by educator-researchers utilize purposeful sampling, or in other words, they select participants who will be able to provide the most relevant information to the study. Therefore, the study design relies upon the participants and the information they can provide. The following is a description of some data collection methods, which include: surveys or questionnaires, individual or group interviews, observations, field notes or diaries, narratives, documents, and elicitation.

Surveys, or questionnaires, are a research instrument frequently used to receive data about participants’ feelings, beliefs, and attitudes in regard to the research topic or activities. Surveys are often used for large sample sizes with the intent of generalizing from a sample population to a larger population. Surveys are used with any number of participants and can be administered at different times during the study, such as pre-activity and post-activity, with the same participants to determine if changes have occurred over the course of the activity time, or simply change over time. Researchers like surveys and questionnaires as an instrument because they can be distributed and collected easily – especially with all of the recent online application possibilities (e.g., Google, Facebook, etc.). Surveys come in several forms, closed-ended, open-ended, or a mix of the two. Closed-ended surveys are typically multiple-choice questions or scales (e.g. 1-5, most likely–least likely) that allow participants to rate or select a response for each question. These responses can easily be tabulated into meaningful number representations, like percentages. For example, Likert scales are often used with a five-point range, with options such as strongly agree, agree, neutral, disagree, and strongly disagree. Open-ended surveys consist of prompts for participants to add their own perspectives in short answer or limited word responses. Open-ended surveys are not always as easy to tabulate, but can provide more detail and description.

Interviews and Focus Groups

Interviews are frequently used by researchers because they often produce some of the most worthwhile data. Interviews allow researchers to obtain candid verbal perspectives through structured or semi-structured questioning. Interview questions, either structured or semi-structured, are related to the research question or research activities to gauge the participants’ thoughts, feelings, motivations, and reflections. Some research relies on interviewing as the primary data source, but most often interviews are used to strengthen and support other data sources. Interviews can be time consuming, but interviews are worthwhile in that you can gather richer and more revealing information than other methods that could be utilized (Koshy, 2010). Lincoln and Guba (1985) identified five outcomes of interviewing:

Outcomes of Interviewing

• Here and now explanations;
• Reconstructions of past events and experiences;
• Projections of anticipated experiences;
• Verification of information from other sources;
• Verification of information (p. 268).

As mentioned above, interviews typically take two forms: structured and semi-structured. In terms of interviews, structured means that the researcher identifies a certain number of questions, in a prescribed sequence, and the researcher asks each participant these questions in the same order. Structured interviews qualitatively resemble surveys and questionnaires because they are consistent, easy to administer, provide direct responses, and make tabulation and analysis more consistent. Structured interviews use an interview protocol to organize questions, and maintain consistency.

Semi-structured interviews have a prescribed set of questions and protocol, just like structured interviews, but the researcher does not have to follow those questions or order explicitly. The researcher should ask the same questions to each participant for comparison reasons, but semi-structured interviews allow the researcher to ask follow-up questions that stray from the protocol. The semi-structured interview is intended to allow for new, emerging topics to be obtained from participants. Semi-structured questions can be included in more structured protocols, which allows for the participant to add additional information beyond the formal questions and for the researcher to return to preplanned formal questions after the participant responds. Participants can be interviewed individually or collectively, and while individual interviews are time-consuming, they can provide more in-depth information.

When considering more than two participants for an interview, researchers will often use a focus group interview format. Focus group interviews typically involve three to ten participants and seek to gain socially dependent perspectives or organizational viewpoints. When using focus group interviews with students, researchers often find them beneficial because they allow student reflection and ideas to build off of each other. This is important because often times students feel shy or hesitant to share their ideas with adults, but once another student sparks or confirms their idea, belief, or opinion they are more willing to share. Focus group interviews are very effective as pre- and post-activity data sources. Researchers can use either a structured or semi-structured interview protocol for focus group interviews; however, with multiple participants it may be difficult to maintain the integrity of a structured protocol.

Observations

One of the simplest, and most natural, forms of data collection is to engage in formal observation. Observing humans in a setting provides us contextual understanding of the complexity of human behavior and interrelationships among groups in that setting. If a researcher wants to examine the ways teachers approach a particular area of pedagogical practice, then observation would be a viable data collection tool. Formal observations are truly unique and allow the researcher to collect data that cannot be obtained through other data sources. Ethnography is a qualitative research design that provides a descriptive account based on researchers’ observations and explorations to examine the social dynamics present in cultures and social systems – which includes classrooms and schools. Taken from anthropology, the ethnographer uses observations and detailed note taking, along with other forms of mapping or making sense of the context and relationships within. For Creswell (2007), several guidelines provide structure to an observation:

Structuring Observations

• Identify what to observe
• Determine the role you will assume — observer or participant
• Design observational protocol for recording notes
• Record information such as physical situation, particular events and activities
• Thank participants and inform them of the use of and their accessibility to the data (pp. 132– 134)

As an educator-researcher, you may take on a role that exceeds that of an observer and participate as a member of the research setting. In this case, the data sources would be called participant observation to clearly identify the degree of involvement you have in the study. In participant observation, the researcher embeds themselves in the actions of the participants. It is important to understand that participant observation will provide completely different data, in comparison to simply observing someone else. Ethnographies, or studies focused completely on observation as a data source, often extend longer than other data sources, ranging from several months to even years. Extended time provides the researcher the ability to obtain more detailed and accurate information, because it takes time to observe patterns and other details that are significant to the study. Self-study is another consideration for educators, if they want to use observation and be a participant observer. They can use video and audio recordings of their activities to use as data sources and use those as the source of observation.

Field Diaries and Notes

Utilizing a field dairy, or keeping field notes, can be a very effective and practical data collection method. In purpose, a field diary or notes keep a record of what happens during the research activities. It can be useful in tracking how and why your ideas and the research process evolved. Many educators keep daily notes about their classes, and in many ways, this is a more focused and narrower version of documenting the daily happenings of a class. A field diary or notes can also serve as an account of your reflections and commentary on your study, and can be a starting place for your data analysis and interpretations. A field diary or notes are typically valuable when researchers begin to write about their project because it allows them to draw upon their authentic voice. The reflective process that represents a diary can also serve as an additional layer of professional learning for researchers. The format and length of a field diary or notes will vary depending on the researching and the topic; however, the ultimate goal should be to facilitate data collection and analysis.

Data narratives and stories are a fairly new form of formalized data. While researchers have collected bits and pieces of narratives in other forms of data, asking participants to compose a narrative (either written, spoken, or performed) as a whole allows researchers to examine how participants embrace the complexities of the context and social interactions. Humans are programmed to engage with and share narratives to develop meaningful and experiential knowledge. Educator autobiographies bring to life personal stories shaped by knowledge, values, and feelings that developed from their classroom experiences. Narrative data includes three primary areas: temporality, sociality, and place (Clandinin & Conolley, 2000). In terms of temporality, narratives have a past, present, and future because stories are time-based and transitional. Sociality highlights the social relationships in narratives as well as the personal and moral dispositions. Place includes the spaces where the narratives happen. Furthermore, bell hooks (1991) notes that narratives, or storytelling, as inquiry can be a powerful way to study how contexts are influenced by power structures, often linking and intersecting the structural dynamics of social class, race, and gender to highlight the struggle.

Documents provide a way to collect data that is unobtrusive to the participant. Documents are unobtrusive data because it is collected without modifying or distracting the research context when gathered. Educational settings maintain records on all sorts of activities in schools: content standards, state mandates, student discipline records, student attendance, student assessments, performance records, parental engagement, records of how teachers spend PTO money, etc. Documents often provide background and contextual material providing a snapshot of school policies, demographic information, ongoing records over a period of time, and contextual details from the site of the research study. Documents can be characterized similarly to historical research, as primary and secondary. Examples of primary materials are first-hand sources from someone in the educational context, such as minutes from a school board or faculty meeting, photographs, video recordings, and letters. Examples of secondary sources typically include analysis or interpretations of a primary source by others, such as texts, critiques, and reviews. Both types of sources are especially valuable in action research.

Elicitation Methods

We have talked about several methods of data collection that each have useful ways of documenting, inquiring, and thinking about the research question. However, how does a researcher engage participants in ways that allow them to demonstrate what they know, feel, think, or believe? Asking participants directly about their thinking, feeling, or beliefs will only take you so far depending on the comfort and rapport the participant has with the researcher. There are always a variety of hurdles in extracting participants’ knowledge. Even the manner in which questions are framed and the way researchers use materials in the research process are equally important in getting participants to provide reliable, comparable, and valid responses. Furthermore, all individuals who participate in research studies vary in their ability to recall and report what they know, and this affects the value of traditional data collection, especially structured and semi-structured interviewing. In particular, participants’ knowledge or other thinking of interest may be implicit and difficult for them to explicate in simple discussion.

Elicitation methods help researchers uncover unarticulated participant knowledge through a potential variety of activities. Researchers will employ elicitation methods and document the participants’ actions and typically the description of why they took those particular actions. Educators may be able to relate the process of elicitation methods to a “think aloud” activity in which the researcher wants to record or document the activity. Elicitation methods can take many forms. What follows are some basic ideas and formats for elicitation methods.

Brainstorming/Concept Map

Most educators are probably familiar with the process of brainstorming or creating a concept map. These can be very effective elicitation methods when the researcher asks the participant to create a concept map or representation of brainstorming, and then asks the participant to explain the connections between concepts or ideas on the brainstorming or concept map.

Sorting provides an engaging way to gather data from your participants. Sorting, as you can imagine, involves participants sorting, grouping, or categorizing objects or photographs in meaningful ways. Once participants have sorted the objects or photographs, the researcher records or documents the participant explaining why they sorted or grouped the objects or photographs in the way that they did. As a former history teacher, I would often use sorting to assess my students’ understanding of related concepts and events in a world history class. I would use pictures too as the means for students to sort and demonstrate what they understood from the unit. For broader discussion of elicitation techniques in history education see Barton (2015).

Listing/ Ranking

Listing can be an effective way to examine participants’ thinking about a topic. Researchers can have participants construct a list in many different ways to fit the focus of the study and then have the participants explain their list. For example, if an educator was studying middle school student perceptions of careers, they could ask them to complete three lists: Careers in Most Demand; Careers with Most Education/Training; Careers of most Interest.

Then, once participants have filled out the lists, the most important part is documenting them explaining their thinking, and why they filled out the lists the way they did. As you may imagine, in this example, every participant would have a list that is different based on their personal interests.

Researchers can also elicit responses by simply giving participants a prompt, and then asking them to recall whatever they know about that prompt. Researchers will have the participants do this in some sort of demonstrative activity. For example, at the end of a world history course, I might ask students to explain what “culture” means to them and to explain their thinking.

Re-articulation (writing or drawing)

A unique way to engage participants in elicitation methods is to have them write about, rewrite, or draw visual representations of either life experiences or literature that they have read. For example, you could ask them to rewrite a part of the literature they did not like, add a part they thought should be there, or simply extend the ending. Participants can either write or draw these re-articulations. I find that drawing works just as well because, again, the goal is to have participant describe their thinking based on the activity.  

Scenario Decision-Making

Elicitation methods can also examine skills. Researchers can provide participants scenarios and ask them to make decisions. The researchers can document those decisions and analyze the extent to which the participant understands the skill.

  Document, Photograph, or Video Analysis

This is the most basic elicitation in which the researcher provides a document, photograph, or video for the participant to examine. Then, the researcher asks questions about the participants interpretations of the document, photograph, or video. One method that would support this sort of elicitation is to ask the participants to provide images from their everyday words. For example, asking students to document the literacy examples in their homes (i.e., pictures of calendars, bookshelves etc.).  With the availability of one-to-one tech, and iPads, participant documentation is easier.

There are many more methods of data collection also, as well as many variations of the methods described above. The goal for you is to find the data collection methods that are going to give you the best data to answer your research question. If you are unsure, there is nothing wrong with collecting more data than you need to make sure you use effective methods – the only thing you have to lose is time!

Use of Case Studies

Case studies are a popular way for studying phenomena in settings using qualitative methodology. Case studies typically encompass qualitative studies which look closely at what happens when researchers collect data, analyze the data, and present the results. Case studies can focus on a single case or examine a phenomenon across multiple cases. Case studies frame research in a way that allows for rich description of data and depth of analysis.

An advantage of using case study design is that the reader often identifies with the case or phenomena, as well as the participants in the study. Yin (2003) describes case study methodology as inquiry that investigates a contemporary phenomenon within its authentic context. Case studies are particularly appropriate when the boundaries and relationship between the phenomenon and the context are not clear. Case studies relate well with the processes involved in action research. Critics of action research case studies sometimes criticize the inevitable subjectivity, just like general criticisms of action research. Case studies provide researchers opportunities to explore both the how and the why of phenomena in context, while being both exploratory and descriptive.

We want to clarify the differences between methodologies and methods of research. There are methodologies of research, like case study and action research, and methods of data collection. Methodologies like ethnography, narrative inquiry, and case study draw from some similar methods of data collecting that include interviews, collection of artifacts (writings, drawings, images), and observations. The differences between the methodologies include the time-frame for research; the boundaries of the research; and the epistemology.

Triangulation of Data

Triangulation is a method used by qualitative researchers to check and establish trustworthiness in their studies by using and analyzing multiple (three or more) data collection methods to address a research question and develop a consistency of evidence from data sources or approaches. Thus, triangulation facilitates trustworthiness of data through cross verification of evidence, to support claims, from more than two data collection sources. Triangulation also tests the consistency of findings obtained through different data sources and instruments, while minimizing bias in the researcher’s interpretations of the data.

If we think about the example of studying the use of math journals in an elementary classroom, the researcher would want to collect at least three sources of data – the journal prompts, assessment scores, and interviews. When the researcher is analyzing the data, they will want to find themes or evidence across all three data sources to address their research question. In a very basic analysis, if the students demonstrated a deeper level of reflection about math in the journals, their assessment scores improved, and their interviews demonstrated they had more confidence in their number sense and math abilities – then, the researcher could conclude, on a very general level, that math journals improved their students’ math skills, confidence, or abilities. Ideally, the study would examine specific aspects of math to enable deeper analysis of math journals, but this example demonstrates the basic idea of triangulation. In this example, all of the data provided evidence that the intervention of a math journal improved students’ understanding of math, and the three data sources provided trustworthiness for this claim.

Data Collection Checklist

• Based on your research question, what data might you need ?
• What are the multiple ways you could collect that data ?
• How might you document this data , or organize it so that it can be analyzed?
• What methods are most appropriate for your context and timeframe ?
• How much time will your data collection require? How much time can you allow for?
• Will you need to create any data sources (e.g., interview protocol, elicitation materials)?
• Do your data sources all logically support the research question, and each other?
• Does your data collection provide for multiple perspectives ?
• How will your data achieve triangulation in addressing the research question?
• Will you need more than three data sources to ensure triangulation of data?

Action Research Copyright © by J. Spencer Clark; Suzanne Porath; Julie Thiele; and Morgan Jobe is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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84 Comments

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Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

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Analyzing Qualitative Data in Mathematics Education

• First Online: 13 September 2019

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• Martin A. Simon 4  

Part of the book series: Research in Mathematics Education ((RME))

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The weak link in many qualitative research studies is the methodology for analyzing data. I define qualitative data analysis as a process of working with data, so that more can be gleaned from the data than would be available from merely reading, viewing, or listening carefully to the data multiple times. I describe and exemplify a recursive analysis process, which we use to analyze teaching-experiment data.

This chapter is partially based on Simon ( 2013 ).

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Cited in Steffe and Thompson ( 2000 ).

Prior to analyzing the data in an extended study of multiple concepts, we sometimes attach codes to the data to keep track of which data are related to which research question. This coding is used to organize and manage the data, not to analyze it. The codes are not emergent but rather are related to our intended analyses.

Note that in contrast to open coding, this is an interpretive process, not a categorizing process. The issue is what these data show, not how the data cluster.

The mental runs were the student’s narration of what she would do if she were to draw a diagram to solve the task.

The data on which this analysis is based are too extensive to share in this chapter; the second level of analysis is based on multiple first-level inferences, each representing a chunk of data. The original article gives more detail.

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Simon, M.A. (2019). Analyzing Qualitative Data in Mathematics Education. In: Leatham, K.R. (eds) Designing, Conducting, and Publishing Quality Research in Mathematics Education. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-030-23505-5_8

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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 .

What’s the Difference Between Educational Equity and Equality?

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ENCYCLOPEDIC ENTRY

Qualitative data.

Not everything in science is numbers and formulas

Mathematics

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Not everything in science is numbers and formulas . In fact, some of the most helpful bits of information come in the form of qualitative data . Think about qualitative data as descriptions of the qualities of whatever it is you're studying.

If you're drinking a cup of hot chocolate, you might stick a thermometer in the cup and learn that the temperature of the liquid is 100 degrees Fahrenheit (you might want to hold off on drinking it, by the way, if it's that hot). You might also look at the ingredients used (chocolate, milk, marshmallows) to see what's in it. But that's not the end of the story when observing a subject, even a subject as simple as a cup of hot chocolate.

Qualitative data like sweet taste, distinctive smell, and warmth are all important observations to make. Making these observations can actually lead to a more in-depth search for particular quantitative, or numbers-driven, qualities of the subject.

Even though qualitative data is considered important in scientific research, it's also considered less valuable than numbers-heavy quantitative data . Science reports tend to carry a lot less meaning if they only stick to the subject's colors and smells.

Compare these qualitative and quantitative data: The Sahara Desert is a big place is a qualitative statement. By looking on a map (or flying over the area), you can see that the Sahara Desert is a large feature in the North African landscape . The statement is based on a quality: size. This visible quality may lead scientists to study exactly how big the Sahara Desert is. The Sahara Desert is nine million square kilometers (3.5 million square miles) is a quantitative statement. It is based on a quantity, or amount. Here, it's the amount of land in the Sahara Desert.

Qualitative Data is Hot Stuff Qualitative data can come in handy for small children. Saying the pot is "100 degrees Fahrenheit" doesn't mean much. But saying it's "hot" tells the story pretty quickly.

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• Published: 20 February 2015

Qualitative Research in Early Childhood Education and Care Implementation

• Wendy K. Jarvie 1  

International Journal of Child Care and Education Policy volume  6 ,  pages 35–43 ( 2012 ) Cite this article

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Metrics details

Governments around the world have boosted their early childhood education and care (ECEC) engagement and investment on the basis of evidence from neurological studies and quantitative social science research. The role of qualitative research is less understood and under-valued. At the same time the hard evidence is only of limited use in helping public servants and governments design policies that work on the ground. The paper argues that some of the key challenges in ECEC today require a focus on implementation. For this a range of qualitative research is required, including knowledge of organisational and parent behaviour, and strategies for generating support for change. This is particularly true of policies and programs aimed at ethnic minority children. It concludes that there is a need for a more systematic approach to analysing and reporting ECEC implementation, along the lines of “implementation science” developed in the health area.

Introduction

Research conducted over the last 15 years has been fundamental to generating support for ECEC policy reform and has led to increased government investments and intervention in ECEC around the world. While neurological evidence has been a powerful influence on ECEC policy practitioners, quantitative research has also been persuasive, particularly randomised trials and longitudinal studies providing evidence (1) on the impact of early childhood development experiences to school success, and to adult income and productivity, and (2) that properly constructed government intervention, particularly for the most disadvantaged children, can make a significant difference to those adult outcomes. At the same time the increased focus on evidence-informed policy has meant experimental/quantitative design studies have become the “gold standard” for producing knowledge (Denzin & Lincoln, 2005 ), and pressures for improved reporting and accountability have meant systematic research effort by government has tended to focus more on data collection and monitoring, than on qualitative research (Bink, 2007 ). In this environment the role of qualitative research has been less valued by senior government officials.

Qualitative Research-WhatIs It?

The term qualitative research means different things to different people (Denzin & Lincoln, 2005 ). For some researchers it is a way of addressing social justice issues and thus is part of radical politics to give power to the marginalised. Others see it simply as another research method that complements quantitative methodologies, without any overt political function. Whatever the definition of qualitative research, or its role, a qualitative study usually:

Features an in depth analysis of an issue, event, entity, or process. This includes literature reviews and meta studies that draw together findings from a number of studies.

Is an attempt to explain a highly complex and/or dynamic issue or process that is unsuited to experimental or quantitative analysis.

Includes a record of the views and behaviours of the players — it studies the world from the perspective of the participating individual.

Cuts across disciplines, fields and subject matter.

Uses a range of methods in one study, such as participant observation; in depth interviewing of participants, key stakeholders, and focus groups; literature review; and document analysis.

High quality qualitative research requires high levels of skill and judgement. Sometimes it requires pulling together information from a mosaic of data sources and can include quantitative data (the latter is sometimes called mixed mode studies). From a public official perspective, the weaknesses of qualitative research can include (a) the cost-it can be very expensive to undertake case studies if there are a large number of participants and issues, (b) the complexity — the reports can be highly detailed, contextually specific examples of implementation experience that while useful for service delivery and front line officials are of limited use for national policy development, (c) difficultyin generalising from poor quality and liable to researcher bias, and (d) focus, at times, more on political agendas of child rights than the most cost-effective policies to support the economic and social development of a nation. It has proved hard for qualitative research to deliver conclusions that are as powerful as those from quantitative research. Educational research too, has suffered from the view that education academics have over-used qualitative research and expert judgement, with little rigorous or quantitative verification (Cook & Gorard, 2007 ).

Qualitative Research and Early Childhood Education and Care

In fact, the strengths of qualitative ECEC research are many, and their importance for government, considerable. Qualitative research has been done in all aspects of ECEC operations and policies, from coordinating mechanisms at a national level (OECD, 2006 ), curriculum frameworks (Office for Children and Early Childhood Development, 2008 ), and determining the critical elements of preschool quality (Siraj-Blatchford et al., 2003 ), to developing services at a community level including effective outreach practices and governance arrangements. Qualitative research underpins best practice guides and regulations (Bink, 2007 ). Cross country comparative studies on policies and programs rely heavily on qualitative research methods.

For public officials qualitative components of program evaluations are essential to understanding how a program has worked, and to what extent variation in outcomes and impacts from those expected, or between communities, are the result of local or national implementation issues or policy flaws. In addition, the public/participant engagement in qualitative components of evaluations can reinforce public trust in public officials and in government more broadly.

In many ways the contrast between quantitative and qualitative research is a false dichotomy and an unproductive comparison. Qualitative research complements quantitative research, for example, through provision of background material and identification of research questions. Much quantitative research relies on qualitative research to define terms, and to identify what needs to be measured. For example, the Effective Provision of PreSchool Education (EPPE) studies, which have been very influential and is a mine of information for policy makers, rely on initial qualitative work on what is quality in a kindergarten, and how can it be assessed systematically (Siraj-Blatchford et al., 2003 ). Qualitative research too can elucidate the “how” of a quantitative result. For example, quantitative research indicates that staff qualifications are strongly associated with better child outcomes, but it is qualitative work that shows that it is not the qualification per se that has an impact on child outcomes-rather it is the ability of staff to create a high quality pedagogic environment (OECD, 2012 ).

Challenges of Early Childhood Education and Care

Systematic qualitative research focused on the design and implementation of government programs is essential for governments today.

Consider some of the big challenges facing governments in early childhood development (note this is not a complete list):

Creating coordinated national agendas for early childhood development that bring together education, health, family and community policies and programs, at national, provincial and local levels (The Lancet, 2011 ).

Building parent and community engagement in ECEC/Early Childhood Development (ECD), including increasing parental awareness of the importance of early childhood services. In highly disadvantaged or dysfunctional communities this also includes increasing their skills and abilities to provide a healthy, stimulating and supportive environment for young children, through for example parenting programs (Naudeau, Kataoka, Valerio, Neuman & Elder, 2011 ; The Lancet, 2011 ; OECD, 2012 ).

Strategies and action focused on ethnic minority children, such as outreach, ethnic minority teachers and teaching assistants and informal as well as formal programs.

Enhancing workforce quality, including reducing turnover, and improved practice (OECD, 2012 ).

Building momentum and advocacy to persuade governments to invest in the more “invisible” components of quality such as workforce professional development and community liaison infrastructure; and to maintain investment over significant periods of time (Jarvie, 2011 ).

Driving a radical change in the way health/education/familyservicepro fessions and their agencies understand each other and to work together. Effectively integrated services focused on parents, children and communities can only be achieved when professions and agencies step outside their silos (Lancet, 2011 ). This would include redesign of initial training and professional development, and fostering collaborations in research, policy design and implementation.

There are also the ongoing needs for,

Identifying and developing effective parenting programs that work in tandem with formal ECEC provision.

Experiments to determine if there are lower cost ways of delivering quality and outcomes for disadvantaged children, including the merits of adding targeted services for these children on the base of universal services.

Figuring out how to scale up from successful trials (Grunewald & Rolnick, 2007 ; Engle et al., 2011 ).

Working out how to make more effective transitions between preschool and primary school.

Making research literature more accessible to public officials (OECD, 2012 ).

Indeed it can be argued that some of the most critical policy and program imperatives are in areas where quantitative research is of little help. In particular, qualitative research on effective strategies for ethnic minority children, their parents and their communities, is urgently needed. In most countries it is the ethnic minority children who are educationally and economically the most disadvantaged, and different strategies are required to engage their parents and communities. This is an area where governments struggle for effectiveness, and public officials have poor skills and capacities. This issue is common across many developed and developing countries, including countries with indigenous children such as Australia, China, Vietnam, Chile, Canada and European countries with migrant minorities (OECD, 2006 ; COAG, 2008 ; World Bank, 2011 ). Research that is systematic and persuasive to governments is needed on for example, the relative effectiveness of having bilingual environments and ethnic minority teachers and teaching assistants in ECEC centres, compared to the simpler community outreach strategies, and how to build parent and community leadership.

Many countries are acknowledging that parental and community engagement is a critical element of effective child development outcomes (OECD, 2012 ). Yet public officials, many siloed in education and child care ministries delivering formal ECEC services, are remote from research on raising parent awareness and parenting programs. They do not see raising parental skills and awareness as core to their policy and program responsibilities. Improving parenting skills is particularly important for very young children (say 0–3) where the impact on brain development is so critical. It has been argued there needs to be a more systematic approach to parenting coach/support programs, to develop a menu of options that we know will work, to explore how informal programs can work with formal programs, and how health programs aimed young mothers or pregnant women can be enriched with education messages (The Lancet, 2011 ).

Other areas where qualitative research could assist are shown in Table 1 (see p. 40).

Implementation Science in Early Childhood Education and Care

Much of the suggested qualitative research in Table 1 is around program design and implementation . It is well-known that policies often fail because program design has not foreseen implementation issues or implementation has inadequate risk management. Early childhood programs are a classic example of the “paradox of non-evidence-based implementation of evidence-based practice” (Drake, Gorman & Torrey, 2005). Governments recognise that implementation is a serious issue: there may be a lot of general knowledge about “what works”, but there is minimal systematic information about how things actually work . One difficulty is that there is a lack of a common language and conceptual framework to describe ECEC implementation. For example, the word “consult” can describe a number of different processes, from public officials holding a one hour meeting with available parents in alocation,to ongoing structures set up which ensureall communityelementsare involved and reflect thespectrum of community views, and tocontinue tobuild up community awareness and engagement over time.

There is a need to derive robust findingsof generic value to public officials, for program design. In the health sciences, there is a developing literature on implementation, including a National implementation Research Network based in the USA, and a Journal of Implementation Science (Fixsen, Naoom, Blasé, Friedman & Wallace, 2005 ). While much of the health science literature is focused on professional practice, some of the concepts they have developed are useful for other fields, such as the concept of “fidelity” of implementation which describes the extent to which a program or service has been implemented as designed. Education program implementation is sometimes included in these fora, however, there is no equivalent significant movement in early childhood education and care.

A priority in qualitative research for ECEC of value to public officials would then appear to be a systematic focus on implementation studies, which would include developing a conceptual framework and possibly a language for systematic description of implementation, as well as, meta-studies. This need not start from scratch-much of the implementation science literature in health is relevant, especially the components around how to influence practitioners to incorporate latest evidence-based research into their practice, and the notions of fidelity of implementation. It could provide an opportunity to engage providers and ECE professionals in research, where historically ECEC research has been weak.

Essential to this would be collaborative relationships between government agencies, providers and research institutions, so that there is a flow of information and findings between all parties.

Quantitative social science research, together with studies of brain development, has successfully made the case for greater investment in the early years.There has been less emphasis on investigating what works on the ground especially for the most disadvantaged groups, and bringing findings together to inform government action. Yet many of the ECEC challenges facing governments are in implementation, and in ensuring that interventions are high quality. This is particularly true of interventions to assist ethnic minority children, who in many countries are the most marginalised and disadvantaged. Without studies that can improve the quality of ECEC implementation, governments, and other bodies implementing ECEC strategies, are at risk of not delivering the expected returns on early childhood investment. This could, over time, undermine the case for sustained government support.

It is time for a rebalancing of government research activity towards qualitative research, complemented by scaled up collaborations with ECEC providers and research institutions. A significant element of this research activity could usefully be in developing a more systematic approach to analysing and reporting implementation, and linking implementation to outcomes. This has been done quite effectively in the health sciences. An investment in developing an ECEC ‘implementation science’ would thus appear to be a worthy of focus for future work.

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7 Quantitative Data Examples in Education

Quantitative data plays a crucial role in education, providing valuable insights into various aspects of the learning process. By analyzing numerical information, educators can make informed decisions and implement effective strategies to improve educational outcomes. But what exactly is quantitative data in education , and why is it essential? In this article, we’ll delve into seven illustrative quantitative data examples in education and analyze their impact.

• Standardized Test Scores: Measuring Performance at Scale
• Attendance Rates: More than Just Numbers
• Graduation Rates: Tracking Long-Term Success
• Class Average Scores: Gauging Collective Performance
• Student-to-Teacher Ratios: A Reflection of Learning Environments
• Homework Completion Rates: Analyzing Daily Academic Engagement
• Frequency of Library Book Checkouts: Monitoring Reading Habits

The Importance of Quantitative Data Examples in Education

Before delving into specific examples, it’s important to understand the importance of quantitative data in education.

Quantitative data plays a crucial role in education by providing objective evidence of student achievement and progress. Mining educational data allows educators to identify trends and patterns, enabling them to tailor teaching methods and interventions to meet the individual needs of students. For example, if a particular group of students consistently underperforms in standardized tests, quantitative data can help educators identify the specific areas where additional support is needed. This data-driven approach ensures that resources are allocated effectively, and students receive the targeted support they require to succeed.

Read next: How data analytics is reshaping the education industry

In addition to informing classroom instruction, quantitative data also plays a significant role in shaping education policies. Policymakers rely on this data to make informed decisions about curriculum development, resource allocation, and educational reforms. By analyzing quantitative data on a larger scale, policymakers can identify systemic issues and implement evidence-based strategies to address them. For instance, if quantitative data reveals a high dropout rate in a specific region, policymakers can develop targeted interventions to improve graduation rates and ensure that students have access to quality education.

1.  Standardized Test Scores: Measuring Performance at Scale

Standardized test scores, spanning from globally recognized exams like the SAT and ACT to national or regional board examinations, have become a cornerstone in the world of education. These scores serve multiple purposes, providing a consistent, objective measure of a student’s grasp of specific subjects and skills. This universal consistency allows for comparisons across regions, states, or even countries, simplifying the monumental task for college and university admissions offices when they sift through thousands of applications from varied educational backgrounds. For these institutions, these scores are invaluable in determining a student’s readiness for the rigors of higher education.

However, the significance of these scores isn’t restricted to tertiary institutions. K-12 schools and districts also harness these numbers to assess the efficacy of their teaching methodologies, curricula, and allocated resources. Consistently low scores might hint at areas where instructional techniques need refinement or indicate students who require additional support. But, as pivotal as they are, it’s essential to approach standardized test scores with a balanced perspective. They capture just one dimension of a student’s academic journey, and their true value is unlocked when integrated with other forms of quantitative and qualitative data.

2.  Attendance Rates: More than Just Numbers

Attendance rates in schools often serve as more than just basic metrics of student presence. At its core, this data provides a nuanced understanding of how engaged, motivated, and committed students are to their educational pursuits. By calculating the percentage of days students are present over a set period, institutions can glean insights into a myriad of underlying factors. A consistently high attendance rate, for instance, could indicate a thriving school environment where students feel inspired and eager to participate. Conversely, a sudden drop might hint at external challenges, from health outbreaks to socio-economic disturbances.

However, diving deeper, these rates also unveil more subtle issues affecting education. Consistent absences can indicate personal struggles, whether they be familial, psychological, or health-related. For educators and administrators, understanding the intricacies behind these numbers is essential. Addressing the root causes, whether they involve bolstering student engagement through innovative teaching methods or providing additional resources for those facing challenges, ensures a more inclusive and responsive educational environment.

3.  Graduation Rates: Tracking Long-Term Success

Graduation rates stand as a pivotal metric in assessing the long-term success and effectiveness of educational institutions. This rate, which depicts the percentage of students who complete their academic programs within a standard timeframe, is more than just a reflection of student diligence. It also provides insights into the quality of instruction, the adequacy of resources, and the overall support infrastructure in place. High graduation rates often suggest that an institution is not only providing valuable academic content but also fostering an environment conducive to sustained student success.

On the flip side, lower graduation rates can act as an early warning sign for potential challenges within the educational framework. Whether it’s a curriculum that doesn’t resonate with the student body, inadequate support for those with learning differences, or external factors like socio-economic challenges that affect a student’s ability to prioritize education, these numbers prompt introspection. For educators and institutional leaders, these rates serve as a guidepost, highlighting areas of success and illuminating opportunities for enhancement in the ever-evolving landscape of academia.

4.  Class Average Scores: Gauging Collective Performance

Class average scores play a fundamental role in deciphering the collective performance of a student group, offering a holistic view of how a class or cohort is faring academically. By taking the mean of scores across a specific subject or class, educators can identify patterns, strengths, and areas that may require more attention. High averages might suggest that teaching methods, curricula, and learning materials are resonating with students, leading to broad comprehension and mastery of the content.

Conversely, consistently lower average scores can serve as a catalyst for introspection and change. They may indicate potential misalignments between the curriculum and students’ learning styles, a need for more interactive or varied teaching methods, or even external factors impacting students’ ability to grasp content. By closely monitoring and analyzing these averages, educational institutions can adapt dynamically, ensuring that teaching strategies evolve to meet the unique needs of every student cohort.

5.  Student-to-Teacher Ratios: A Reflection of Learning Environments

The student-to-teacher ratio in educational settings offers a clear, quantifiable snapshot of the learning environment’s structure. A direct representation of how many students are assigned to each educator, this metric provides insights into the potential for individualized attention within a class. In instances where the ratio is low, it often implies that teachers have fewer students to manage, allowing for more one-on-one interactions, personalized feedback, and a closer understanding of each student’s needs and challenges.

However, a higher ratio can signify challenges in resource allocation or an influx of students beyond the institution’s standard capacity. In such scenarios, teachers might find it challenging to address individual student concerns, potentially leading to overlooked learning gaps or unmet needs. Recognizing the implications of these ratios allows educational institutions to strategize effectively, whether it’s hiring additional staff, incorporating teaching assistants, or leveraging technology to ensure every student receives the attention and support they deserve.

6.  Homework Completion Rates: Analyzing Daily Academic Engagement

Homework, a staple in the K-12 educational journey, can provide more insights than just individual student performance. By tracking homework completion rates, schools gain a clearer perspective on daily academic engagement outside the classroom. Consistently high completion rates typically indicate a student body that’s committed, understands the material, and can effectively manage their time. It can also suggest that the homework given is appropriately challenging and relevant, resonating with students and thus motivating them to complete it.

Conversely, lower homework completion rates might raise flags about potential challenges students face. These can range from the homework being perceived as too difficult or irrelevant, to external factors such as familial obligations or extracurricular activities taking up significant time. Schools can use this quantitative data to reassess the nature and volume of homework assigned or to initiate conversations with students about their challenges, ensuring that homework remains a productive, beneficial aspect of the learning process.

7.  Frequency of Library Book Checkouts: Monitoring Reading Habits

In K-12 schools, libraries often serve as hubs of exploration, learning, and growth. Tracking the frequency of library book checkouts can provide a quantitative measure of students’ reading habits and interests. A high frequency indicates an enthusiastic student body actively engaging with literature, research, or both. It can also reflect the effectiveness of library programs, reading challenges, or events aimed at promoting literary exploration.

On the other hand, a decline or consistently low checkout rate might signal a waning interest in reading or challenges in accessing library resources. This could prompt schools to examine the relevance and variety of available books, consider introducing digital reading platforms, or revamp the library’s ambiance to make it more inviting. Ultimately, this quantitative data aids schools in ensuring their libraries remain vibrant centers of literary exploration and learning for all students.

Quantitative data examples in education offer valuable insights into various aspects of the learning process. By analyzing different types of data in education , policymakers can make informed decisions and develop strategies to enhance educational outcomes. Harnessing the power of quantitative data allows educators to foster an environment where every student has the opportunity to thrive and reach their full potential.

As you delve into the diverse landscape of quantitative data in education, it’s paramount to harness tools that streamline analysis and interpretation. The Inno™ Starter Kits have been meticulously crafted to assist educators in navigating the intricate world of data. Whether you’re just beginning your data-driven journey or are an established expert, these kits offer a comprehensive solution to visualizing, understanding, and applying quantitative insights. Explore today and unlock unparalleled potential in educational outcomes!

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