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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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

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

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

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

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• Published: 16 November 2020

Navigating the qualitative manuscript writing process: some tips for authors and reviewers

• Chris Roberts   ORCID: orcid.org/0000-0001-8613-682X 1 ,
• Koshila Kumar   ORCID: orcid.org/0000-0001-8504-1052 2 &
• Gabrielle Finn   ORCID: orcid.org/0000-0002-0419-694X 3  

BMC Medical Education volume  20 , Article number:  439 ( 2020 ) Cite this article

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Qualitative research explores the ‘black box’ of how phenomena are constituted. Such research can provide rich and diverse insights about social practices and individual experiences across the continuum of undergraduate, postgraduate and continuing education, sectors and contexts. Qualitative research can yield unique data that can complement the numbers generated in quantitative research, [ 1 ] by answering “how” and “why” research questions. As you will notice in this paper, qualitative research is underpinned by specific philosophical assumptions, quality criteria and has a lexicon or a language specific to it.

A simple search of BMC Medical Education suggests that there are over 800 papers that employ qualitative methods either on their own or as part of a mixed methods study to evaluate various phenomena. This represents a considerable investment in time and effort for both researchers and reviewers. This paper is aimed at maximising this investment by helping early career researchers (ECRs) and reviewers new to the qualitative research field become familiar with quality criteria in qualitative research and how these can be applied in the qualitative manuscript writing process. Fortunately, there are numerous guidelines for both authors and for reviewers of qualitative research, including practical “how to” checklists [ 2 , 3 ]. These checklists can be valuable tools to confirm the essential elements of a qualitative study for early career researchers (ECRs). Our advice in this article is not intended to replace such “how to” guidance. Rather, the suggestions we make are intended to help ECRs increase their likelihood of getting published and reviewers to make informed decisions about the quality of qualitative research being submitted for publication in BMC Medical Education. Our advice is themed around long-established criteria for the quality of qualitative research developed by Lincoln and Guba [ 4 ]. (see Table  1 ) Each quality criterion outlined in Table 1 is further expanded in Table  2 in the form of several practical steps pertinent to the process of writing up qualitative research.

As a general starting point, the early career writer is advised to consult previously published qualitative papers in the journal to identify the genre (style) and relative emphasis of different components of the research paper. Patton [ 5 ] advises researchers to “FOCUS! FOCUS! FOCUS!” in deciding which components to include in the paper, highlighting the need to exclude side topics that add little to the narrative and reduce the cognitive load for readers and reviewers alike. Authors are also advised to do significant re-writing, rephrasing, re-ordering of initial drafts, to remove faulty grammar, and addresses stylistic and structural problems [ 6 ]. They should be mindful of “the golden thread,” that is their central argument that holds together the literature review, the theoretical and conceptual framework, the research questions, methodology, the analysis and organisation of the data and the conclusions. Getting a draft reviewed by someone outside of the research/writing team is one practical strategy to ensure the manuscript is well presented and relates to the plausibility element.

The introduction of a qualitative paper can be seen as beginning a conversation. Lingard advises that in this conversation, authors need to persuade the reader and reviewer of the strength, originality and contributions of their work [ 7 ]. In constructing a persuasive rationale, ECRs need to clearly distinguish between the qualitative research phenomenon (i.e. the broad research issue or concept under investigation) and the research context (i.e. the local setting or situation) [ 5 ]. The introduction section needs to culminate in a qualitative research question/s. It is important that ECRs are aware that qualitative research questions need to be fine-tuned from their original state to reflect gaps in the literature review, the researcher/s’ philosophical stance, the theory used, or unexpected findings [ 8 ]. This links to the elements of plausibility and consistency outlined in Table 1 .

Also, in the introduction of a qualitative paper, ECRs need to explain the multiple “lenses” through which they have considered complex social phenomena; including the underpinning research paradigm and theory. A research paradigm reveals the researcher/s’ values and assumptions about research and relates to axiology (what do you value?), ontology (what is out there to know?) epistemology (what and how can you know it?), and methodology (how do you go about acquiring that knowledge?) [ 9 ] ECRs are advised to explicitly state their research paradigm and its underpinning assumptions. For example, Ommering et al., state “We established our research within an interpretivist paradigm, emphasizing the subjective nature in understanding human experiences and creation of reality.” [ 10 ] Theory refers to a set of concepts or a conceptual framework that helps the writer to move beyond description to ‘explaining, predicting, or prescribing responses, events, situations, conditions, or relationships.’ [ 11 ] Theory can provide comprehensive understandings at multiple levels, including: the macro or grand level of how societies work, the mid-range level of how organisations operate; and the micro level of how people interact [ 12 ]. Qualitative studies can involve theory application or theory development [ 5 ]. ECRs are advised to briefly summarise their theoretical lens and identify what it means to consider the research phenomenon, process, or concept being studied with that specific lens. For example, Kumar and Greenhill explain how the lens of workplace affordances enabled their paper to draw “attention to the contextual, personal and interactional factors that impact on how clinical educators integrate their educational knowledge and skills into the practice setting, and undertake their educational role.” [ 13 ] Ensuring that the elements of theory and research paradigm are explicit and aligned, enhances plausibility, consistency and transparency of qualitative research. The use of theory can also add to the currency of research by enabling a new lens to be cast on a research phenomenon, process, or concept and reveal something previously unknown or surprising.

Moving to the methods, methodology is a general approach to studying a research topic and establishes how one will go about studying any phenomenon. In contrast, methods are specific research techniques and in qualitative research, data collection methods might include observation or interviewing, or photo elicitation methods, while data analysis methods may include content analysis, narrative analysis, or discourse analysis to mention a few [ 8 ]. ECRs will need to ensure the philosophical assumptions, methodology and methods follow from the introduction of a manuscript and the research question/s, [ 3 ] and this enhances the consistency and transparency elements. Moreover, triangulation or the combining of multiple observers, theories, methods, and data sources, is vital to overcome the limitation of singular methods, lone analysts, and single-perspective theories or models [ 8 ]. ECRs should report on not only what was triangulated but also how it was performed, thereby enhancing the elements of plausibility and consistency. For example, Touchie et al., describe using three researchers, three different focus groups, and representation of three different participant cohorts to ensure triangulation [ 14 ]. When it comes to the analysis of qualitative data, ECRs may claim they have used a specific methodological approach (e.g. interpretative phenomenological approach or a grounded theory approach) whereas the analytical steps are more congruent with a more generalist approach, such as thematic analysis [ 15 ]. ECRs are advised that such methodological approaches are founded on a number of philosophical considerations which need to inform the framing and conduct of a study, not just the analysis process. Alignment between the methodology and the methods informs the consistency, transparency and plausibility elements.

Comprehensively describing the research context in a way that is understandable to an international audience helps to illuminate the specific ‘laboratory’ for the research, and how the processes applied or insights generated in this ‘laboratory’ can be adapted or translated to other contexts. This addresses the relevancy element. To further enhance plausibility and relevance, ECRs should situate their work clearly on the evaluation–research continuum. Although not a strictly qualitative research consideration, evaluation focuses mostly on understanding how specific local practices may have resulted in specific outcomes for learners. While evaluation is vital for quality assurance and improvement, research has a broader and strategic focus and rates more highly against the currency and relevancy criteria. ECRs are more likely to undertake evaluation studies aimed at demonstrating the impact and outcomes of an educational intervention in their local setting, consistent with level one of Kirkpatrick’s criteria [ 16 ]. For example, Palmer and colleagues explain that they aimed to “develop and evaluate a continuing medical education (CME) course aimed at improving healthcare provider knowledge” [ 17 ]. To be competitive for publication, evaluation studies need to (measure and) report on at least level two and above of Kirkpatrick’s criteria. Learning how to problematise and frame the investigation of a problem arising from practice as research, provides ECRs with an opportunity to adopt a more critical and scholarly stance.

Also, in the methods, ECRs may provide detail about the study context and participants but little in the way of personal reflexive statements. Unlike quantitative research which claims that knowledge is objective and seeks to remove subjective influences, qualitative research recognises that subjectivity is inherent and that the researcher is directly involved in interpreting and constructing meanings [ 8 ]. For example, Bindels and colleagues provide a clear and concise description about their own backgrounds making their ‘lens’ explicit and enabling the reader to understand the multiple perspectives that have informed their research process [ 18 ]. Therefore, a clear description of the researcher/s position and relationship to the research phenomenon, context and participants, is vital for transparency, relevance and plausibility. We three are all experienced qualitative researchers, writers, reviewers and are associate editors for BMC Medical Education. We are situated in this research landscape as consumers, architects, and arbiters and we engage in these roles in collaboration with others. This provides a useful vantage point from which to provide commentary on key elements which can cause frustration for would-be authors and reviewers of qualitative research papers [ 19 ].

In the discussion of a qualitative paper, ECRs are encouraged to make detailed comments about the contributions of their research and whether these reinforce, extend, or challenge existing understandings based on an analysis that is theoretically or socially significant [ 20 ]. As an example, Barratt et al., found important data to inform the training of medical interns in the use of personal protective equipment during the COVID 19 pandemic [ 21 ]. ECRs are also expected to address the “so what” question which relates to the the consequence of findings for policy, practice and theory. Authors will need to explicitly outline the practical, theoretical or methodological implications of the study findings in a way that is actionable, thereby enhancing relevance and plausibility. For example, Burgess et al., presented their discussion according to four themes and outlined associated implications for individuals and institutions [ 22 ]. A balanced view of the research can be presented by ensuring there is congruence between the data and the claims made and searching the data and/or literature for evidence that disconfirms the findings. ECRs will also need to put forward the sources of uncertainty (rather than limitations) in their research and argue what these may mean for the interpretations made and how the contributions to knowledge could be adopted by others in different contexts [ 23 ]. This links to the plausibility and transparency elements.

In conclusion

Qualitative research is underpinned by specific philosophical assumptions, quality criteria and a lexicon, which ECRs and reviewers need to be mindful of as they navigate the qualitative manuscript writing and reviewing processes. We hope that the guidance provided here is helpful for ECRs in preparing submissions and for reviewers in making informed decisions and providing quality feedback.

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CR and KK wrote the first draft. All three authors contributed to severally revising the manuscript. The author(s) read and approved the final manuscript.

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Writing up qualitative research

There are specific challenges to writing up research based on qualitative data: there is a lot of data to present, complex stories to tell and conclusions that took a long time to develop. But some simple guidelines can help structuring any kind of research output

Daniel Turner

There are specific challenges to writing up research based on qualitative data: there is a lot of data to present, complex stories to tell and conclusions that took a long time to develop. But some simple guidelines can help structuring any kind of research output based on qualitative methods, and help communicate the rich results that come from qualitative data.

There’s a very general statement whenever you are writing or speaking – consider your audience. Who is likely to read your research output? Usually, you would have several from a project, such as a summary for the participants or special interest groups, a research paper for the scientific community (and the journal reviewers) and a thesis which is written to demonstrate knowledge and research skills for an examining panel. Understanding who you are writing for should dictate much of the specifics below, especially how much you need to write to explain certain aspects. Will your readers understand qualitative methodologies? A challenge is often writing for reviewers (or supervisors) who don’t have a qualitative background and don’t understand the methods and small sampling approaches in qualitative research.

This guide is designed to be general enough to apply to a research article or thesis chapter, but note that each will have it’s own specific guidelines for structure and content that should be applied. Some research journals have very specific structures and formatting guidelines to follow, where others allow for more flexibility. Regardless, the key points below should give a general overview of ‘best practice’ in writing up qualitative research.

Theory and Methodology

If you are coming from quantitative disciplines, this is the first section that seems unusual when writing up a research project. Although not explicit, all research has a epistemological and theoretical background and set of assumptions that guide the collection methods and interpretation. However, qualitative research has a strong tradition of making those explicit and justified. The researcher(s) should state their philosophical beliefs on knowledge that have shaped the project.

Describing the epistemology in detail would entail writing about how the researchers see the notion of ‘truth’, positivity, and how much can be understood in the world. Usually this need go no further than adopt one an existing critique or interpretation, such as feminist research methods, or post-positivism. But it should also explain why the qualitative approach was appropriate for this research question, and thus what the appropriate methodology was.

While often connected most clearly to method, the methodology should also touch on the approach selected for analysis and interpretation, noting that adopting something common like grounded theory actually implies a whole series of theoretical and epistemological underpinnings. In a journal article, this will be short: typically only a few sentences, unless the paper is adopting or proposing a new or unusual take on some theoretical stance. But in a qualitative-based thesis, this could well be a chapter in it’s own right.

Finally, researchers should consider some sort of engagement with ‘reflexivity’ and explaining the role and influence of the researcher on the research project. I’ve got a whole article on this , but being open and explicit about the fact that research is not ‘value-free’ and that the background and positionality of the researcher influences the project is usually a key principle in qualitative research.

Methods and analysis

Qualitative methodologies have great variety in the type of methods that can be used, and subtleties as to how they are applied. Therefore it is probably more important to describe the method in detail than in quantitative based research projects, which often have very standard formula for applying tools and measurements. It’s rarely enough to say ‘We interviewed’ or ‘We ran focus groups ’ as these common methods can be applied in many different ways. So specify not just whether you used semi-structured or unstructured interviews, but consider describing the settings, interview guides, and any underlining theoretical or methodological stances.

As a reviewer, I also like to see details of both the sampling and recruitment categories – and as I’ve written before, I think these are distinct and important things. When using the small sample sizes that are hallmarks of most qualitative research, it is especially important to know who these people are, where they are from and how they were selected. Although we might get details about individual respondents lives and backgrounds later, its still important to give an overview of the people who make up the data – just remember it’s not always necessary to reduce these to statistical averages, such as the percentage of people in each age range.

It’s also very important to give a good level of detail about the analytical approach. I’ve written a LOT about this, but suffice it to say that just saying ‘We used grounded theory ’ or ‘We applied thematic analysis ’ is rarely specific enough – again due to the many different ways these terms are interpreted and enacted. Usually the qualitative analysis process is exploratory, non-linear and fraught with missteps and overlapping approaches, so is worthy of more detail. As qualitative research relies so much on the interpretation of the researcher, authors deserve some insight into the analytic process. If using coding, a list of codes, or illustration of some of the main ones can be insightful, even in an appendix.

Another pet hate or mine is papers that just refer to any qualitative analysis software as ‘We analysed the results in Quirkos/Nvivo’. Again, this tells the reader no more useful information than saying ‘We wrote the results using a blue pen’ – it’s not the tool, but how is is applied that affects and defines the analytic approach!

Quoting Data

In the results, findings and discussion sections, a lot of outputs based on qualitative research include extracts from the qualitative data: often from the transcripts from interviews, focus groups , diaries etc. Including quotes like this is a key feature in qualitative research outputs, they give the reader a chance to hear the participants in their own words, giving some validation of the researcher’s own interpretations. They also are effective at illustrating key points in the data and moving on the flow, and providing a framing device for discussing findings. Qualitative software like Quirkos can also help greatly at this stage, helping you keep track of quotes on particular topics, or organised by key quotes so you can quickly find them. However, there is the challenge of the ‘wall of text’.

There is a risk of the paper or chapter becoming over-run with quotations, which coming from different participants with different perspectives can become overwhelming for the reader. In general quotes should be used sparingly, for illustrative snapshots, or colourful instances where the quote says something better than the researcher can. In general, quotes from transcripts won’t be as short and generalisable as a sentence from the writer.

There’s also a temptation to use a quote to prove every conclusion or statement that the author wants to make, in the same way you would have a reference to back up each statement made in a literature review or introduction. But this is rarely necessary, as it will also break the flow of the writing and lead to issues with space and length. The writer should use just enough quotes to establish trust from the reader of their interpretations, saving quotes for when the author is making an important or surprising finding.

There is also the possibility of using two different types of quotes: short in-text quips, and longer multiple-sentence extractions. Shorter quotes are best for using just a few words, or a sentence at most that fits within the discussion. For example, one person may comment, ‘Time is always precious when you have children’. Here it’s not always necessary to attribute the quote to a particular individual (unless what they say is particular to their identity or case).

Longer quotes break the flow of the text, and should usually be formatted as an indented paragraph. These should almost always have a separate line after the quote giving the participant’s number, pseudonym and any relevant information about them, for example age, location or gender if it is relevant to understanding the context of the quote. Usually these should be kept as short as necessary, using … to truncate any sections of the quote that are not relevant. They also need to make sense alone, so any references to ‘it’ or ‘them’ or ‘that’ which are not obvious in the extract should be replaced with a definition in square brackets, for example [my feelings], [my husband] or [my car].

This is even true of other data types, especially pictures. Although most research products like journal articles and theses unfortunately make it very difficult to include audio and video extracts, pictures are possible. Yet these also break the flow of text, and take up a lot of space. They should also be used sparingly, and be well integrated and illustrative of the argument being made.

There can also be additional confidentiality considerations including any multimedia extracts. Text quotations automatically remove the audible voice and face of participants, but this might have to be done manually for participants. Permission to share photos, especially those showing faces and other sensitive or personal situations should also be explicitly sought, ideally for each type of output that might be produced.

Finally, quotes can’t do everything, many important aspects of subtext, and the unspoken (whether non-verbal, or not willing to share) are important context to give the reader. It’s up to the writer to connect the dots, be the expert that was part of the whole process and summarise a huge amount of rich qualitative data into a coherent story.

Discussion and other considerations

Latterly, any output needs to summarise the data and process, and refer back to the research questions and the wider literature. There will often be new questions posed by the data, as well as any conclusions from the data. Finally, consider being honest about what went wrong in the project – either in the data collection stage, or the analysis when theories and assumptions were challenged and changed.

Remember, just as good qualitative research has a story at it’s centre, the same should be true of communicating the findings. Partly it’s a story of the research project (and the role of the researchers in it), but also the story of the respondents – their own journey, experiences and lives. That is why creating a single story that encapsulates a common and differing tales across many participants can be a challenge.

Quirkos is a unique tool for qualitative analysis that helps you explore, manage and discover stories in your qualitative data. We designed it to help us write up our own qualitative data easier and better, and you can try yourself for free right here ! There’s no registration needed for the offline version, and the cloud subscription service lets researchers, work, collaborate and share project data with ease.

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Guidelines for Preparing Qualitative Manuscripts

Authors submitting qualitative manuscripts to Psychology of Religion and Spirituality ( PRS ) should familiarize themselves with the Journal Article Reporting Standards for Qualitative Primary Research (JARS-Qual) and seek to adhere to them as much as possible.

In particular, the following JARS-Qual standards are deemed especially important for authors to uphold in their submissions of qualitative research submitted to PRS .

• Download in PDF format (118KB)

Study Objectives/Aims/Research Goals

• State the purpose(s)/goal(s)/aim(s) of the study.
• Qualitative studies tend not to identify hypotheses, but research questions and goals.

Research Design Overview

• Summarize the research design (data-collection strategies, data-analytic strategies) and, if illuminating, approaches to inquiry (e.g., descriptive, interpretive, feminist, psychoanalytic, postpositivist, critical, postmodern or constructivist, pragmatic approaches).
• Provide the rationale for the design selected.

Researcher Description

• Describe the researchers' backgrounds in approaching the study, emphasizing their prior understandings of the phenomena under study. Prior understandings relevant to the analysis could include, but are not limited to, descriptions of researchers' demographic/cultural characteristics, credentials, experience with phenomena, training, values, or decisions in selecting archives or material to analyze.
• Describe how researchers' prior understandings of the phenomena under study were managed and/or influenced the research (e.g., enhancing, limiting, or structuring data collection and analysis).

Recruitment Process

• Describe the rationale for decision to halt data collection (e.g., saturation).
• Provide a rationale for the number of participants chosen.

Participant Selection

• Describe the participants/data sources selection process (e.g., purposive sampling methods such as maximum variation, diversity sampling, or convenience sampling methods such as snowball selection, theoretical sampling), inclusion/exclusion criteria.
• State the form of data collected (e.g., interviews, questionnaires, media, observation).
• Describe any alterations of data-collection strategy in response to the evolving findings or the study rationale.
• For interview and written studies, indicate the mean and range of the time duration in the data-collection process (e.g., "interviews were held for 75 to 110 min, with an average interview time of 90 min").
• Describe questions asked in data collection: Content of central questions, form of questions (e.g., open vs. closed).

Data-Analytic Strategies

• Describe the methods and procedures used and for what purpose/goal.
• Explicate in detail the process of analysis, including some discussion of the procedures (e.g., coding, thematic analysis, etc.) with a principle of transparency.
• Describe coders or analysts and their training, if not already described in the researcher description section (e.g., coder selection, collaboration groups).
• Identify whether coding categories emerged from the analyses or were developed a priori.
• Identify units of analysis (e.g., entire transcript, unit, text) and how units were formed, if applicable.
• Describe the process of arriving at an analytic scheme, if applicable (e.g., if one was developed before or during the analysis or was emergent throughout).
• Provide illustrations and descriptions of their development, if relevant.
• Indicate software, if used.

Methodological Integrity

The task force that developed JARS-Qual defines methodological integrity and its component processes as follows:

Methodological integrity can be evaluated through its two composite processes: fidelity to the subject matter and utility in achieving research goals. … Fidelity to the subject matter is the process by which researchers select procedures that develop and maintain allegiance to the phenomenon under study as it is conceived within their approach to inquiry (e.g., the phenomenon might be understood as a social construction). … The second composite process of methodological integrity, utility in achieving research goals is the process by which researchers select procedures that usefully answer their research questions and address their aims (e.g., raising critical consciousness, developing theory, deepening understanding, identifying social practices, forming conceptual frameworks, and developing local knowledge). (Levitt et al. [2018], Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board Task Force report. American Psychologist, 73 (1), 26-46. P. 33).

• Demonstrate that the claims made from the analysis are warranted and have produced findings with methodological integrity. The procedures that support methodological integrity (i.e., fidelity and utility) typically are described across the relevant sections of a paper, but they could be addressed in a separate section when elaboration or emphasis would be helpful. Issues of methodological integrity include:
• Assess the adequacy of the data in terms of its ability to capture forms of diversity most relevant to the question, research goals, and inquiry approach.
• Describe how the researchers' perspectives were managed in both the data collection and analysis (e.g., to limit their effect on the data collection, to structure the analysis).
• Demonstrate that findings are grounded in the evidence (e.g., using quotes, excerpts, or descriptions of researchers' engagement in data collection).
• Demonstrate that the contributions are insightful and meaningful (e.g., in relation to the current literature and the study goal).
• Provide relevant contextual information for findings (e.g., setting of study, information about participant, presenting the interview question asked prior to presenting the excerpt, as needed).
• Present findings in a coherent manner that makes sense of contradictions or disconfirming evidence in the data (e.g., reconcile discrepancies, describe why a conflict might exist in the findings).
• Demonstrate consistency with regard to the analytic processes (e.g., analysts may use demonstrations of analyses to support consistency, describe their development of a stable perspective, interrater reliability, consensus) or describe responses to inconsistencies, as relevant (e.g., coders switching mid-analysis, an interruption in the analytic process). If alterations in methodological integrity were made for ethical reasons, explicate those reasons and the adjustments made.
• Describe how support for claims was supplemented by any checks added to the qualitative analysis. Examples of supplemental checks that can strengthen the research may include:
• Transcripts/data collected returned to participants for feedback.
• Triangulation across multiple sources of information, findings, or investigators.
• Checks on the interview thoroughness or interviewer demands.
• Consensus or auditing process.
• Member checks or participant feedback on findings.
• Data displays/matrices.
• In-depth thick description, case examples, illustrations.
• Structured methods of researcher reflexivity (e.g., sending memos, field notes, diary, log books, journals, bracketing).
• Checks on the utility of findings in responding to the study problem (e.g., an evaluation of whether a solution worked).
• Although findings may or may not include quantified information, depending upon the study's goals, approach to inquiry, and study characteristics, we encourage authors to include even simple quantified information about the qualitative findings (themes), recognizing that this will assist many readers in understanding the relative importance or frequency of themes.
• Identify the study's strengths and limitations (e.g., consider how the quality, source, or types of the data or the analytic processes might support or weaken its methodological integrity).
• Describe the limits of the scope of transferability (e.g., what should readers bear in mind when using findings across contexts).
• Consider the implications for future research, policy, or practice.

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How to Do Qualitative Research

Last Updated: October 26, 2022 Fact Checked

This article was co-authored by Jeremiah Kaplan . Jeremiah Kaplan is a Research and Training Specialist at the Center for Applied Behavioral Health Policy at Arizona State University. He has extensive knowledge and experience in motivational interviewing. In addition, Jeremiah has worked in the mental health, youth engagement, and trauma-informed care fields. Using his expertise, Jeremiah supervises Arizona State University’s Motivational Interviewing Coding Lab. Jeremiah has also been internationally selected to participate in the Motivational Interviewing International Network of Trainers sponsored Train the Trainer event. Jeremiah holds a BS in Human Services with a concentration in Family and Children from The University of Phoenix. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 750,683 times.

Qualitative research is a broad field of inquiry that uses unstructured data collections methods, such as observations, interviews, surveys and documents, to find themes and meanings to inform our understanding of the world. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source Qualitative research tends to try to cover the reasons for behaviors, attitudes and motivations, instead of just the details of what, where and when. Qualitative research can be done across many disciplines, such as social sciences, healthcare and businesses, and it is a common feature of nearly every single workplace and educational environment.

Preparing Your Research

• The research questions is one of the most important pieces of your research design. It determines what you want to learn or understand and also helps to focus the study, since you can't investigate everything at once. Your research question will also shape how you conduct your study since different questions require different methods of inquiry.
• You should start with a burning question and then narrow it down more to make it manageable enough to be researched effectively. For example, "what is the meaning of teachers' work to teachers" is too broad for a single research endeavor, but if that's what you're interested you could narrow it by limiting the type of teacher or focusing on one level of education. For example, "what is the meaning of teachers' work to second career teachers?" or "what is the meaning of teachers' work to junior high teachers?"

Tip: Find the balance between a burning question and a researchable question. The former is something you really want to know about and is often quite broad. The latter is one that can be directly investigated using available research methods and tools.

• For example, if your research question focuses on how second career teachers attribute meaning to their work, you would want to examine the literature on second career teaching - what motivates people to turn to teaching as a second career? How many teachers are in their second career? Where do most second career teachers work? Doing this reading and review of existing literature and research will help you refine your question and give you the base you need for your own research. It will also give you a sense of the variables that might impact your research (e.g., age, gender, class, etc.) and that you will need to take into consideration in your own study.
• A literature review will also help you to determine whether you are really interested and committed to the topic and research question and that there is a gap in the existing research that you want to fill by conducting your own investigation.

For example, if your research question is "what is the meaning of teachers' work to second career teachers?" , that is not a question that can be answered with a 'yes' or 'no'. Nor is there likely to be a single overarching answer. This means that qualitative research is the best route.

• Consider the possible outcomes. Because qualitative methodologies are generally quite broad, there is almost always the possibility that some useful data will come out of the research. This is different than in a quantitative experiment, where an unproven hypothesis can mean that a lot of time has been wasted.
• Your research budget and available financial resources should also be considered. Qualitative research is often cheaper and easier to plan and execute. For example, it is usually easier and cost-saving to gather a small number of people for interviews than it is to purchase a computer program that can do statistical analysis and hire the appropriate statisticians.

• Action Research – Action research focuses on solving an immediate problem or working with others to solve problem and address particular issues. [7] X Research source
• Ethnography – Ethnography is the study of human interaction in communities through direct participation and observation within the community you wish to study. Ethnographic research comes from the discipline of social and cultural anthropology but is now becoming more widely used. [8] X Research source
• Phenomenology – Phenomenology is the study of the subjective experiences of others. It researches the world through the eyes of another person by discovering how they interpret their experiences. [9] X Research source
• Grounded Theory – The purpose of grounded theory is to develop theory based on the data systematically collected and analyzed. It looks at specific information and derives theories and reasons for the phenomena.
• Case Study Research – This method of qualitative study is an in-depth study of a specific individual or phenomena in its existing context. [10] X Research source

Collecting and Analyzing Your Data

• Direct observation – Direct observation of a situation or your research subjects can occur through video tape playback or through live observation. In direct observation, you are making specific observations of a situation without influencing or participating in any way. [12] X Research source For example, perhaps you want to see how second career teachers go about their routines in and outside the classrooms and so you decide to observe them for a few days, being sure to get the requisite permission from the school, students and the teacher and taking careful notes along the way.
• Participant observation – Participant observation is the immersion of the researcher in the community or situation being studied. This form of data collection tends to be more time consuming, as you need to participate fully in the community in order to know whether your observations are valid. [13] X Research source
• Interviews – Qualitative interviewing is basically the process of gathering data by asking people questions. Interviewing can be very flexible - they can be on-on-one, but can also take place over the phone or Internet or in small groups called "focus groups". There are also different types of interviews. Structured interviews use pre-set questions, whereas unstructured interviews are more free-flowing conversations where the interviewer can probe and explore topics as they come up. Interviews are particularly useful if you want to know how people feel or react to something. For example, it would be very useful to sit down with second career teachers in either a structured or unstructured interview to gain information about how they represent and discuss their teaching careers.
• Surveys – Written questionnaires and open ended surveys about ideas, perceptions, and thoughts are other ways by which you can collect data for your qualitative research. For example, in your study of second career schoolteachers, perhaps you decide to do an anonymous survey of 100 teachers in the area because you're concerned that they may be less forthright in an interview situation than in a survey where their identity was anonymous.
• "Document analysis" – This involves examining written, visual, and audio documents that exist without any involvement of or instigation by the researcher. There are lots of different kinds of documents, including "official" documents produced by institutions and personal documents, like letters, memoirs, diaries and, in the 21st century, social media accounts and online blogs. For example, if studying education, institutions like public schools produce many different kinds of documents, including reports, flyers, handbooks, websites, curricula, etc. Maybe you can also see if any second career teachers have an online meet group or blog. Document analysis can often be useful to use in conjunction with another method, like interviewing.

• Coding – In coding, you assign a word, phrase, or number to each category. Start out with a pre-set list of codes that you derived from your prior knowledge of the subject. For example, "financial issues" or "community involvement" might be two codes you think of after having done your literature review of second career teachers. You then go through all of your data in a systematic way and "code" ideas, concepts and themes as they fit categories. You will also develop another set of codes that emerge from reading and analyzing the data. For example, you may see while coding your interviews, that "divorce" comes up frequently. You can add a code for this. Coding helps you organize your data and identify patterns and commonalities. [15] X Research source tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• Descriptive Statistics – You can analyze your data using statistics. Descriptive statistics help describe, show or summarize the data to highlight patterns. For example, if you had 100 principal evaluations of teachers, you might be interested in the overall performance of those students. Descriptive statistics allow you to do that. Keep in mind, however, that descriptive statistics cannot be used to make conclusions and confirm/disprove hypotheses. [16] X Research source
• Narrative analysis – Narrative analysis focuses on speech and content, such as grammar, word usage, metaphors, story themes, meanings of situations, the social, cultural and political context of the narrative. [17] X Research source
• Hermeneutic Analysis – Hermeneutic analysis focuses on the meaning of a written or oral text. Essentially, you are trying to make sense of the object of study and bring to light some sort of underlying coherence. [18] X Research source
• Content analysis / Semiotic analysis – Content or semiotic analysis looks at texts or series of texts and looks for themes and meanings by looking at frequencies of words. Put differently, you try to identify structures and patterned regularities in the verbal or written text and then make inferences on the basis of these regularities. [19] X Research source For example, maybe you find the same words or phrases, like "second chance" or "make a difference," coming up in different interviews with second career teachers and decide to explore what this frequency might signify.

Community Q&A

• Qualitative research is often regarded as a precursor to quantitative research, which is a more logical and data-led approach which statistical, mathematical and/or computational techniques. Qualitative research is often used to generate possible leads and formulate a workable hypothesis that is then tested with quantitative methods. [20] X Research source Thanks Helpful 0 Not Helpful 0
• Try to remember the difference between qualitative and quantitative as each will give different data. Thanks Helpful 4 Not Helpful 0

You Might Also Like

• ↑ https://www.ncbi.nlm.nih.gov/books/NBK470395/
• ↑ https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html
• ↑ https://academic.oup.com/humrep/article/31/3/498/2384737?login=false
• ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4275140/
• ↑ http://www.qual.auckland.ac.nz/
• ↑ http://www.socialresearchmethods.net/kb/qualapp.php
• ↑ http://www.socialresearchmethods.net/kb/qualdata.php
• ↑ tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• ↑ https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
• ↑ https://explorable.com/qualitative-research-design

About This Article

To do qualitative research, start by deciding on a clear, specific question that you want to answer. Then, do a literature review to see what other experts are saying about the topic, and evaluate how you will best be able to answer your question. Choose an appropriate qualitative research method, such as action research, ethnology, phenomenology, grounded theory, or case study research. Collect and analyze data according to your chosen method, determine the answer to your question. For tips on performing a literature review and picking a method for collecting data, read on! Did this summary help you? Yes No

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How to Write a Qualitative Report

A qualitative report is a description of an event, activity, observation, research or experience. The structure of a qualitative report includes an abstract, introduction, background to the problem, the researcher's role, theoretical perspective, methodology, ethical considerations, results, data analysis, limitations, discussion, conclusions and implications, references and appendix. Choice of words and visual aids depends on intended audience. Be concise and use the past tense.

Summarize the report briefly. Include subject matter, research question and purpose. Briefly state data collection methods, rationale and interpretation. (Abstract)

State your research question, problem leading to the study and purpose for the study. Identify the research approach, participants and research location. The introduction should give a clear idea of reason, method and significance for intended audience of the report. (Introduction)

Give a brief history of the problem. Describe participants, research site/location, and subject matter. Use established sources, previous studies and support with statistics where possible. (Background to the Problem)

State your or the researcher's relationship to the participants and past experiences and how these may affect perceptions and interpretations, hinder objectivity or enhance understanding, awareness, knowledge and sensitivity to issues and contexts studied. (The Researcher's Role)

State the theory, theme or lens through which the study was guided. Cite authentic sources, theorists or past studies. (Theoretical Perspective)

Describe the method of selection of participants (sampling) and research setting. State the method of gathering data---face-to-face structured/unstructured interviews, questionnaires, focus group discussion, participant observation, document analysis. Explain how instruments were developed, observations recorded and rationale for chosen methods. (Methodology)

Explain steps taken, prior to the research, to inform participants of data collection activities and the proposed use of findings. Describe steps taken to respect the rights of participants, get their consent, gain permission for the research site and ensure confidentiality of participants' information. (Ethical Considerations)

Present findings in a visually appealing manner at a level that your audience can readily understand. Use tables, graphs and charts if necessary. Do not include interpretations in this section. (Results)

Interpret data presented. Describe data analysis process and computer programs. Use graphs and charts as necessary. Support important interpretations with evidence and diverse quotes if applicable. (Data Analysis)

Show how the boundaries of the sample, research site, timing, methodology and/or analysis limited results. (Limitations)

Write a descriptive narrative around interpretations and research question(s). Use illustrations such as quotes from participants and analytic evidence to support arguments. Use literature review of past studies and relevant theory to compare if necessary. Pose new questions if data suggests unforeseen results. (Discussion)

State main conclusions in relation to research question(s) and purpose of the study. Include how results relate to theoretical perspectives. Add main discussion points and questions for future research. (Conclusions and Implications)

Write a reference list using appropriate style such as the American Psychological Association (A.P.A.) style. Include all citations. Be accurate and precise. A reader must be able to locate your sources. (References)

Include extra data sets and diagrams illustrating analysis. Add permission letters and a sample of participants' consent forms. Block out names of persons and institutions. (Appendix)

• For long reports, consider writing an executive summary: a summarized version of your comprehensive report.

Things You'll Need

• Research design: Qualitative, quantitative, and mixed methods approaches By John W. Creswell

A travel writer for over 10 years, Seeta Shah Roath features economic reviews, investment opportunities, history, culture and tourism in different countries. She has a Doctoral degree in education from the University of Phoenix and a passion for e-learning. Her articles appear in Washington Times Global and Examiner.com.

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Writing a qualitative research report

Affiliation.

• 1 School of Nursing and Midwifery Studies, University of Wales College of Medicine, Heath Park, Cardiff, UK. [email protected]
• PMID: 15234716
• DOI: 10.1016/j.aaen.2003.11.006

A research project in nursing or nursing education is probably only complete once the findings have been published. This paper offers a format for writing a qualitative research report for publication. It suggests, at least, the following sections: introduction, aims of the study, review of the literature, sample, data collection methods, data analysis methods, findings, discussion, conclusion, abstract. Each of these sections is addressed along with many written-out examples. In some sections, alternative approaches are suggested. The aim of the paper is to help the neophyte researcher to structure his or her report and for the experienced researcher to reflect on his or her current practice. References to other source material on qualitative research are given.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Qualitative data analysis: how market researchers can crack the code

What is qualitative data, what are the ingredients of a good qualitative data analysis, how to conduct an enlightening qualitative data analysis, the pros and cons of qualitative data analysis, get great results from qualitative data analysis.

When numbers fall short and you need the full story, qualitative data analysis comes to the rescue. Instead of following assumptions based on numerical data, qualitative data analysis methods let you dig deeper. Qualitative data analysis examines non-numerical data – words, images, and observations, to uncover themes, patterns, and meanings. 

And in this article, we’ll tell you exactly how to do it yourself, in-house. 

Qualitative data analysis uncovers the stories and feelings behind numbers. Qualitative methods gain information from conversations, interviews, and observations, capturing what people think and why they act a certain way. Unlike hard numbers, qualitative data helps us see the color and texture of people’s opinions, experiences, and emotions. 

Examples of the textual data that often makes up qualitative data pieces are a user’s detailed feedback on a mobile app’s usability, a shopper’s narrative about choosing eco-friendly products, or observational notes on customer behavior in a retail setting. 

This type of qualitative data collection helps us understand real feelings and thoughts, and goes beyond numbers and assumptions.

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There’s a big difference between knowing that 50% of customers prefer your new product and understanding the nuanced reasons behind that preference.

It’s easy to get blinded by shiny numbers. In this case, a preference signals that you’re doing something great. But not knowing what, means you can’t replicate it, or double down on it to crank up that 50% even more.

So what you’ll need to do is dig into the ‘why’ behind the ‘what’. And we mean really dig. A strong qualitative data analysis process really aims at not putting words inside your customers mouths but letting them speak for themselves.

Another example is when a company finds out through a quick quantitative data survey that customers rate their service 4 out of 5. Which isn’t bad. But how can they improve it – or even work to maintain it? Guesswork is lethal here, yet it’s what so many companies resort to.

Which leads to obvious follow-up actions that are usually not customer-centric. Let’s say that this company assumes people are mostly happy because of their quick response times. So, they implement chatbots to take care of the first part of conversations, to speed things up even more. What could be wrong with that? 

But what if through in-depth interviews, they could have discovered that the personal touch from the staff right from the get-go is what customers really value? 

In consumer research, these nuances are gold. They allow your team to make finely tuned adjustments that resonate deeply with your audience. It’s what helps you move beyond the one-size-fits-all approach suggested by quantitative data. 

So if you want to start making experiences and products that feel personal and relevant to each customer, here are some ways to approach qualitative data research.

Content analysis: unveiling customer sentiments

What it is: Content analysis involves examining texts, reviews, and comments to identify frequently occurring words and sentiments, providing a quantitative measure of qualitative feedback.

Good to know:

• Focus on reviews, comments, and social media posts.
• Look for repeating words and sentiments to identify trends.
• Helps prioritize actions based on frequently mentioned topics by customers.

Chances are, you already have a lot of content that can be analyzed for qualitative data research. In that case, content analysis is your go-to approach to getting started. Content analysis means zooming in on recurring words, phrases, and sentiments scattered across reviews and comments.

Dig into reviews, comments, and emails and start flagging words and phrases that keep coming back. These can help you identify areas for improvement, but also show you what really is working.

This way, content analysis offers a quantitative measure of qualitative feedback, enabling you to prioritize actions based on what’s most mentioned by your customers, when they’re not prompted or asked anything specifically.

By systematically categorizing and quantifying this feedback, you’ll be able to make informed decisions on product features, marketing messages, and even future design innovations.

Narrative analysis: connecting through stories

What it is: Narrative analysis delves into customers’ stories to understand their experiences, decisions, and emotions throughout their journey with your brand.

• Analyze customer stories from initial contact to purchase.
• Focus on customers’ thoughts and feelings at each stage.
• Useful for identifying communication and support opportunities.

A lot of times brands are mostly interested in the beginning and end of a customer journey: how do I get in front of customers, and how do I get in their shopping basket?

But the story of what happens between those two moments is just as, if not more important. And with narrative analysis, you can help connect the dots.

You won’t just be looking at the touchpoints there were, but also what customers were thinking and feeling at each stage. By interpreting qualitative data, you can create a full story from start to finish on how customers think and feel and make decisions in your market.

And that is so much more than just a nice story. Narrative analysis shows you where you can swoop in, where you should change your communications or where you should offer more support — for a happy ever after.

Discourse analysis: shaping perceptions through conversation

What it is: Discourse analysis examines language and communication on platforms like social media to understand how they influence public perception and consumer behavior.

• Explore broader conversations around topics relevant to your brand.
• Understand cultural, social, and environmental contexts.
• Align your messaging with audience values and lead discussions.

Discourse analysis looks at the broader conversation around topics relevant to your brand. This qualitative data analysis method looks at how language and communication on platforms like social media shape public perception and influence consumer behavior.

Discourse analysis not just about what’s being said about your brand and products; it’s about understanding the cultural, social, and environmental currents that drive these conversations.

For example, when customers discuss “sustainability,” they’re not just talking about your specific packaging; they’re engaging in a larger dialogue about corporate responsibility, environmental impact, and ethical consumption.

Discourse analysis helps you grasp the nuances of these discussions, revealing how your brand can authentically contribute to and lead within these conversations.

This strategic insight allows you to align your messaging with your audience’s values, build credibility, and position your brand as a leader in meaningful sustainability efforts.

By engaging with and influencing the discourse, you can adapt to current consumer expectations but you can even take it a step further, and shape future trends and behaviors in alignment with your brand’s values and goals.

Thematic analysis: finding overlapping themes in chaos

What it is: Thematic analysis seeks to find common themes within qualitative data, moving beyond individual opinions to uncover broader patterns.

• Organize feedback into distinct themes.
• Requires systematic data collection and coding.
• Offers clear, actionable insights for different business areas.

Plenty of brands are already sitting on qualitative data from thousands of customer interactions, which might seem like a jumble of individual opinions and experiences.

You might look at them and think ‘ ha, humans really all want or value different things ’. But there will be overlap, and that is where the real value lies.

Thematic analysis aims at finding common themes in this qualitative data. You move beyond surface-level chaos by categorizing all pieces of feedback into distinct themes.

These themes could range from specific product features, such as “battery life” in electronics, to broader experiential factors, like “customer service excellence” or “ease of use.” By identifying these recurring patterns, you gain a clearer, more organized understanding of your customers’ priorities and pain points.

One of the benefits of thematic analysis is that it helps you organize a wide range of feedback into clear, actionable insights for each team in your business. You may uncover themes about the product, about communication, or other parts of your business that customers get exposed to. In other words: every business could benefit from some thematic analysis.

Grounded theory: building strategies from real feedback

What it is: Grounded theory uses early feedback from users to develop theories and strategies that meet their needs, focusing on continuous improvement.

• Start with feedback from early users or testers.
• Engage deeply with feedback to guide product development.
• Ideal for new services or products, ensuring they align with customer expectations.

For those launching a new service, grounded theory takes feedback from early users and starts building from there. It uses real, raw customer thoughts to shape a strategy that better meets their needs.

This approach isn’t just about collecting data; it’s about letting qualitative data direct your next moves, ensuring your innovations are not just shots in the dark but informed, strategic decisions aimed at fulfilling genuine customer needs.

When you adopt grounded theory, you commit to a process of continuous improvement and adaptation. As feedback starts rolling in from those first users or beta testers, you’re given a unique opportunity to see your product through the eyes of those it’s meant to serve.

This early-stage feedback is gold—unfiltered, direct, and incredibly insightful. It tells you what’s resonating with your audience, what’s missing the mark, and, crucially, how to adjust your offering for better alignment with customer expectations.

Bear in mind that when done right, grounded theory goes beyond merely reacting to feedback. It’s about proactively seeking it out and engaging with it. This means not just reading comments or reviews, but diving deeper through follow-up questions, interviews, or focus groups to really understand the why behind the feedback. 

Diving into qualitative data analysis can feel like a big task for many brands. There’s often worry about how much time it’ll take. Or how much money. And then there’s the question of whether all that detail might lead you off track instead of to clear answers.

After all, businesses move fast these days, and spending a lot of time on a research project doesn’t always fit the schedule.

But those worries don’t have to stop you. With the right plan and the best tools, you can dodge those issues. Start by creating a roadmap, so you know what the next few days, weeks or months will look like. See? It’s less daunting already.

Below, we’ll break the whole process down into simple steps. We’re going to walk through how to tackle qualitative data analysis without getting bogged down.

1. Transcribing interviews and collecting qualitative survey data

When it comes to qualitative research, if something’s said, it’s crucial. And that means you gotta write it down. Or at least have a tool to do it for you.

‘ ’I don’t wanna miss a thing’ ’ is your theme song for this step.

Every chuckle, pause, or sigh can give you insights into what your customers really think and feel. Now, I know what you’re thinking: “Transcribing interviews sounds like a lot of work. Let alone conducting all of them!” 

But here’s the good news—using Attest makes this step a pleasant breeze on a hot summer night. With Attest, you can send out surveys that dive deep into all the qualitative questions you’ve been itching to ask. Our platform is designed to capture rich, detailed responses in a way that is easy to search and analyze. 

This means you don’t have to worry about spending hours transcribing interviews. The responses are already there in writing, ready for you to analyze. This doesn’t just save time; it ensures accuracy. You’re getting the unfiltered voice of your customer, directly and conveniently. No more playing detective with hours of audio recordings.

2. Organize data and identify common patterns

Next, sift through your transcribed interviews, survey responses, and notes. Your goal here is to spot patterns or themes that crop up repeatedly.

This could be similar sentiments about a product feature or shared experiences with your service. Organizing data helps you identify themes that move from scattered bits of feedback to clear, common threads that tell a bigger story.

3. Using tools to make the process easier

There are plenty of software tools out there designed to help with qualitative data analysis. These tools can help you code your qualitative data, which means tagging parts of the text with keywords or themes, making it easier to organize and analyze textual data. They can save you a heap of time and help you stay accurate and consistent in your analysis.

That’s where Attest’s innovative Video Responses come into play, offering a seamless and impactful way to gather and analyze qualitative data directly from your target audience – all in the same platform as your quantitative data.

Here’s how we transform qualitative research:

• Easy to use : Attest’s platform lets you quickly add video questions to surveys, making it straightforward to collect in-depth feedback.
• Fast insights : With automated transcriptions, you can swiftly analyze video responses, identifying key themes without the wait.
• Reliable data : Attest ensures feedback comes from a diverse, representative audience, giving you confidence in the insights you gather.
• Rich context : Video responses capture the full spectrum of customer emotions and nuances, providing a deeper understanding than text alone.
• Seamless integration : Mix qualitative and quantitative data effortlessly, for a comprehensive view of your customer base.

As consumer behaviors and preferences continue to evolve at lightning speed, it’s products like Video Responses that will help brands win more based on decisions made with a deeper understanding of their customers. Jeremy King, CEO and Founder of Attest

4. Highlight context alongside data where relevant

Understanding the context in which feedback is provided is crucial in qualitative analysis. It’s not just about what your customers are saying; it’s also about why they’re saying it at that particular moment. This deeper layer of insight can significantly impact how you interpret and act on the data you collect.

Why context matters:

• Timing : Feedback given right after a new product launch can contain initial impressions that might evolve over time. Similarly, responses collected during a major sale or promotion might be influenced by the excitement or urgency of the moment.
• External factors : Consider the broader environment. For example, feedback during a major social event, a public holiday, or even a global crisis can be colored by the emotions and experiences of that time. This can shift priorities or change the way people interact with your brand.
• Customer journey stage : The stage of the customer journey at which feedback is given can also provide important context. Early-stage feedback might focus on first impressions and expectations, while later-stage feedback could offer deeper insights into user experience and satisfaction.

How to account for context in your qualitative analysis:

• Document the circumstances : When collecting data, make a note of the timing and any relevant external factors.
• Consider the source : Different platforms can also provide context. For instance, feedback from a public social media post might differ from what’s shared in a private survey due to the public nature of the medium.
• Use context to guide action : Let the context inform how you prioritize and respond to feedback. Initial excitement might warrant a quick thank-you message, while deeper, contextual insights might lead to product or service improvements.

5. Seek participant validation

Once you’ve got some preliminary findings, it’s a good idea to circle back to your participants. This could mean confirming your interpretations with them or diving deeper into certain areas.

This will help you be sure your analysis aligns with your respondents’ intended meanings and experiences. Plus, it shows respect for their contributions and can uncover even richer insights.

6. Compile a final report with a mix of data and visualization techniques

Finally, bring your analysis to life in a report that mixes clear, concise writing with visual elements like charts, graphs, and quotes.

Visualization helps make complex insights more accessible, engaging, and persuasive. Your report should not only present what you’ve found but also tell the story of how these research findings can influence decisions and strategies.

7. Put insights into action

The real value of qualitative data analysis lies in its application. Use the insights to inform decisions, refine strategies, and better meet your customers’ needs. This is where your analytical journey makes a tangible impact on your business.

Previously when we’ve had to do qualitative research, it’s taken months and months. Attest gets the information that we need quickly. By the very next day we’re able to implement some of the changes and then go back for round two. Simon Gray, Head of Marketing, Zzoomm

Qualitative data analysis looks at the human side of data. It offers insights that numbers alone can’t provide. But like all research methods, even qualitative data analysis methods have their strengths and weaknesses, especially when it comes to shaping a marketing plan that hits the mark.

Advantages of qualitative data analysis

Bringing qualitative data into your strategy brings about transformative advantages that can significantly transform how your business connects with your audience and adapts to the market. Without further ado, let’s look at the benefits it brings.

Qualitative data gives you truly rich insights

Want to go beyond meeting the explicit needs of your customers, and also address their unspoken desires and creating experiences that truly matter to them? Qualitative analysis offers an unparalleled depth of understanding by capturing the subtleties and complexities of customer behavior and sentiment. 

By engaging directly with your audience through interviews, focus groups, or social media interactions, you gain nuanced perspectives that quantitative data alone cannot provide. These rich insights enable you to craft marketing strategies and product innovations that resonate on a deeper level with your audience. 

Qualitative data is a lot more flexible than numbers

Numbers can be quite limiting. The benefit of qualitative analysis is that you’re not confined to a predetermined set of questions or outcomes. 

Instead, you have the freedom to explore new directions, probe interesting findings further, and let the data guide your research process. This flexibility means your research process can evolve in real-time, responding to unexpected insights or shifting market dynamics. 

Qualitative data is great for strategic decision-making

The insights gained from qualitative analysis can significantly inform strategic decision-making. By understanding the nuances of customer feedback, you can make informed and detailed choices about where to allocate resources, which product features to prioritize, and how to position your business in the market.

You can go beyond generic moves in the right direction and make sure you hit the nail on the head on the first try, instead of slowly creeping towards it.

Qualitative research data fuels innovation and differentiation

Businesses are always looking for ways to innovate, but where to look? It’s often less obvious and loud than you think. And innovation doesn’t always have to be massively disrupting or a big pivot. Sometimes small changes made by listening to your customers’ unmet needs and emerging desires will tell you everything you need to know for your next product launch.

Innovation that brings information in from customers is often much more to-the-point than innovation that comes from inside the business, where people tend to be focused on the product and possibilities around it a lot. But try a different approach every once in a while. Listen to the people that use your product, not just the ones who create it.

Qualitative research data will fuel a customer-centric culture

Qualitative data puts your customer’s voice front and center. It highlights their stories, opinions, and feelings, making your marketing strategy more empathetic and customer-focused. This will allow you to build stronger connections with your audience.

Not by any marketing gimmicks, creating online communities or carefully curated UGC campaigns, but by speaking directly to customers’ experiences and emotions. Using qualitative data across your organization brings transformative effects, deeply embedding a culture of attentiveness, adaptability, and unwavering focus on the customer at every level of your business.

This approach does more than just inform product development or marketing strategies—it reshapes the very foundation of how your business operates and interacts with the people it was created for. 

Disadvantages of qualitative data analysis

We’re not going to pretend that qualitative data analysis is something you can do on autopilot. But while qualitative data analysis brings its set of challenges, understanding these can help you navigate through them more effectively.

Moreover, with the right tools and strategies, the benefits you gain far outweigh any of the potential drawbacks we’ve listed below. Here’s a closer look at these challenges and how to turn them into opportunities:

Qualitative data analysis can time-consuming

Yes, qualitative analysis often* demands time and resources. The depth it requires—from collecting detailed narratives to transcribing and interpreting vast amounts of text—can seem daunting. However, this investment in time is what uncovers the nuanced insights that quantitative methods might miss.

*… but not always. With Attest’s Video Responses, you get reliable qual insights fast, alongside your quantitative data!

Qualitative data analysis is pretty subjective

Of course, the interpretive nature of qualitative data analysis does introduce the risk of subjectivity and bias. But ignoring all opinions and thoughts around your product or brand is arguably worse. What this challenge underscores all the more is the importance of a structured, systematic approach to analysis.

By implementing standardized procedures for coding and analyzing data, and employing tools that facilitate consistency across the process, you can mitigate the risks of subjective bias.

And if you involve a diverse team in the analysis process and make sure you pick a representative set of respondents, qualitative research can enable a deeper, more empathetic understanding of ALL your customers; experiences and perspectives.

Qualitative data analysis methods come with scaling issues

Qualitative data collection can indeed be tricky to scale and generalize across a broader market. But who said you can only do qualitative research with in-person interviews? With the right survey tool, like Attest, you can ask quantitative questions at scale, to an audience that is large and diverse.

Our participant audience consists of 125 million people spread across 59 countries, and once you send out a survey, results can come back in mere minutes or hours. So if scalability is holding you back, online surveys with video responses are the answer.

Unlock the full potential of qualitative data analysis with Attest. Gain actionable insights, bridge the gap between raw data and emotional intelligence, and make informed decisions. Discover how Attest can support your journey to deeper consumer understanding at Attest for insights professionals and learn about our commitment to data quality .

Customer Research Manager 

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A psychologist explains how the ‘lion’s gate portal’ can benefit you.

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Days like 8/8 can benefit you regardless of your belief in them as they create the perfect storm of ... [+] positivity, placebo and manifestation practice.

Research has confirmed time and again that the gaps between psychological science and spirituality are wide. While one uses treatment modalities developed through scientific rigor, the other banks on faith, belief and optimism.

Paradoxically, however, psychological healing often intersects with spirituality in the realm of practice. “Manifestation” exercises such as meditation and chanting, positive visualization, journaling and affirmations are prescribed in both spaces regularly and are often rooted in gaining more knowledge of and control over the subconscious and unconscious mind.

Research published in 2023 also indicates that certain psychological constructs, like being in a “flow state,” mirror spiritual experiences. The study further argues that incorporating spirituality into your life may enhance self-understanding and potential through self-belief, a goal therapists often set for clients they treat.

All of this is to say that there are many paths that lead to a desired destination. Whether you are a realist with elaborate plans for the future or you’re a spiritual soul building a deeper connection with the universe, manifestation exercises can help you break substantial ground on the journey you’re already on.

And while there is no perfect time to start this journey, many swear by certain fated days, meant to be more powerful and “bountiful” than others. Today is supposed to be one such day, marking the opening of the “Lion’s Gate portal.” Here’s the lore behind the popular legend.

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Trump vs. harris 2024 polls: harris widens lead—up 5 points in latest survey, harris vaults past trump as the bookies’ favorite to win presidential election, the astrological tale behind lion’s gate portal.

Spiritual practitioners claim the eighth of August to be the day the universe supposedly opens a cosmic gateway known as the Lion’s Gate Portal. With Sirius rising and the Sun in Leo, believers claim this is a magical window for transformation and manifestation, as if the universe itself is conspiring to grant all wishes.

For those who believe the lore, it presents a tantalizing chance to harness the universe’s supposed powers. Whether it’s celestial truth or just a fanciful story lacking scientific or cosmic corroboration, the intent to start manifesting in your life is never unuseful. Regardless of these beliefs, manifestation can always help people achieve their best potential.

Why Does Manifestation Work Well With Spirituality?

While they may use vastly different language, construct different arguments and are trying to prove different things—spiritual healing and psychological healing often coincide when it comes to execution. Here’s a psychologist’s take on why manifestation works in both worlds:

• The placebo effect of faith and positive outcomes. Research published in Philosophical Transactions of the Royal Society B suggests that belief systems, including spiritual practices, can impact physical health and well-being. Another study examining the Covid-19 pandemic found that patients with higher emotional, social, physical and spiritual resilience experienced less severe symptoms and recovered more quickly, illustrating how faith can influence outcomes through the placebo effect. And finally, in a 2020 article discussing the power of religious practices, psychiatrist Harold Koening notes: “Placebos have been used in medicine since antiquity and may have significantly improved health and quality of life when little was known about the causes of most illnesses. Many outcomes were likely due to the placebo effect, as available treatments were either unproven or later disproven.” In the same vein, practices like manifestation may rely on the placebo effect, where believing in positive outcomes creates a psychological environment that supports achieving those outcomes.
• The powerful role of self-efficacy. Prolific researcher Albert Bandura's work on self-efficacy highlights the power of one’s belief in their own ability to succeed. When individuals engage in manifestation practices, spiritual or not, they are essentially boosting their self-efficacy—which can lead to better performance and greater resilience in the face of challenges. This helps in building a positive self-image and enhances strength to take righteous actions towards one's ambitions.
• Principles of Cognitive Behavioral Therapy (CBT). CBT , a well-established psychological treatment modality, emphasizes the importance of changing negative thought patterns to improve mental health. Manifestation techniques, such as affirmations and visualization, align closely with CBT principles by encouraging individuals to focus on positive thoughts and outcomes, thereby reducing anxiety and self-sabotaging thoughts.

How You Can Harness The “Magic” Of Days Like 8/8

Whether ordained by the universe or not, there may not be a better time than now to channelize your mental and spiritual energy toward manifesting the goals you desire to achieve. Here’s why the efficacy of these tools can feel like magic:

• Meditation and visualization. Meditation and visualization are powerful tools that help individuals focus their intentions and reduce stress. Research led by epidemiologists at West Virginia University shows that regular meditation can enhance cognitive function and emotional regulation.
• Journaling. Writing down aspirations and goals can clarify intentions and create a tangible blueprint for success. Journaling has been shown to improve mental health by allowing individuals to process emotions and articulate their own thoughts.
• Environmental enhancements. Creating a conducive environment for manifestation, such as lighting candles or using fragrances, can enhance mood and focus. Research published in Scientia Pharmaceutica suggests that certain olfactory stimulation can positively affect mood and cognitive function.
• Affirmations. Repeating affirmations can reinforce positive beliefs and motivate individuals to pursue their goals. A 2015 study indicates that affirmations, when practiced consistently and spoken as if true, can improve performance and self-perception through a sense of achieving rewards.

While the myths surrounding events like the Lion’s Gate portal may blend astrological assumptions into daily life, the practice of manifestation itself holds significant psychological value at all times in life. The power of intention, belief and structured practice can have profound effects on cognitive health and personal growth. By understanding and harnessing these psychological techniques, individuals can achieve positive transformations, regardless of their spiritual beliefs.

Test your levels of spirituality by taking the science-backed Ego Dissolution Scale, here .

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