“Not everything that can be counted counts, and not everything that counts can be counted“ (Albert Einstein)
Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data (Punch, 2013).
Qualitative research can be used to: (i) gain deep contextual understandings of the subjective social reality of individuals and (ii) to answer questions about experience and meaning from the participant’s perspective (Hammarberg et al., 2016).
Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research focuses on thematic and contextual information.
In This Article
Characteristics of Qualitative Research
Reality is socially constructed
Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013).
The primacy of subject matter
The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).
Variables are complex, interwoven, and difficult to measure
Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables, making them difficult to measure quantitatively.
However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013).
Emic (insider’s point of view)
The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).
Emic is used to describe how participants interact, communicate, and behave in the context of the research setting (Scarduzio, 2017).
Why Conduct Qualitative Research?
In order to gain a deeper understanding of how people experience the world, individuals are studied in their natural setting. This enables the researcher to understand a phenomenon close to how participants experience it.
Qualitative research allows researchers to gain an in-depth understanding, which is difficult to attain using quantitative methods.
An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022).
This helps to further investigate and understand quantitative data by discovering reasons for the outcome of a study – answering the why question behind statistics.
The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively (Busetto et al., 2020).
To design hypotheses, theory must be researched using qualitative methods to find out what is important in order to begin research.
For example, by conducting interviews or focus groups with key stakeholders to discover what is important to them.
Examples of qualitative research questions include:
- How does stress influence young adults’ behavior?
- What factors influence students’ school attendance rates in developed countries?
- How do adults interpret binge drinking in the UK?
- What are the psychological impacts of cervical cancer screening in women?
- How can mental health lessons be integrated into the school curriculum?
Design: How to Conduct Qualitative Research?
There are four main methods used to collect qualitative data: observations, interviews, focus groups, and ethnography.
This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.
In participant observation, the researcher actively participates in the situation/events being observed.
In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020).
Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).
However, awareness of an observer’s presence may influence participants’ behavior.
Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013).
Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.
Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias.
Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).
The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias.
Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).
A shared narrative is built between participants to capture a group experience shaped by a shared context.
The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.
Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.
Ethnography is a methodology used to study a group of people’s behaviours and social interactions in their environment (Reeves et al., 2008).
Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.
The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization.
Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.
In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work.
This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.
Qualitative Data Analysis Methods
There are different methods that can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study.
The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007).
Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).
This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously.
There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding.
Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed.
Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.
Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013).
This research method can be used to analyze data in different formats, which can be written, oral, or visual.
The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012).
Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.
The following six steps provide a guideline for how to conduct qualitative content analysis.
- Define a Research Question: To start content analysis, a clear research question should be developed.
- Identify and Collect Data: Establish the inclusion criteria for your data. Find the relevant sources to analyze.
- Define the Unit or Theme of Analysis: Categorize the content into themes. Themes can be a word, phrase, or sentence.
- Develop Rules for Coding your Data: Define a set of coding rules to ensure that all data are coded consistently.
- Code the Data: Follow the coding rules to categorize data into themes.
- Analyze the Results and Draw Conclusions: Examine the data to identify patterns and draw conclusions in relation to your research question.
Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).
In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.
Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.
Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.
- Define the Research Question: Develop a relevant research question to frame the analysis.
- Gather Data and Establish the Context: Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
- Analyze the Content: Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
- Review the Results: Reflect on the findings to examine the function of the language, and the meaning and context of the discourse.
Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts.
Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).
Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question.
In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns.
This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.
Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive.
An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.
Phases of Thematic Analysis
Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data.
|Phase||Procedure for each step|
|1. Gather and transcribe data||Gather raw data, for example interviews or focus groups, and transcribe audio recordings fully|
|2. Familiarization with data||Read and reread all your data from beginning to end; note down initial ideas|
|3. Create initial codes||Start identifying preliminary codes which highlight important features of the data and may be relevant to the research question|
|4. Create new codes which encapsulate potential themes||Review initial codes and explore any similarities, differences, or contradictions to uncover underlying themes; create a map to visualize identified themes|
|5. Take a break then return to the data||Take a break and then return later to review themes|
|6. Evaluate themes for good fit||Last opportunity for analysis; check themes are supported and saturated with data|
Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).
Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.
To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset.
Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.
A guideline for the main procedural steps for conducting template analysis is outlined below.
- Familiarization with the Data: Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
- Preliminary Coding: Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
- Organize Themes: Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
- Produce an Initial Template: Develop an initial template. This may be based on a subset of the data.
- Apply and Develop the Template: Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes.
- Finalize Template: Finalize the template, then apply it to the entire dataset.
Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.
Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.
Frame analysis encourages researchers to organize and manage their data using summarization.
This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns.
Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.
Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.
- Familiarization with the Data: Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
- Develop a Theoretical Framework: Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
- Indexing: Apply the framework systematically to the entire study data.
- Summarize Data in Analytical Framework: Reduce the data into brief summaries of participants’ accounts.
- Mapping and Interpretation: Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.
How do you Prevent Bias in Qualitative Research?
To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).
The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking.
Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.
A common strategy must be defined at the beginning of the coding process (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.
Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings.
Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).
Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.
Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).
This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000).
Reflexivity in qualitative research
Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research.
Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.
Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.
These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014).
Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking.
I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic.
My research aspirations are to help to develop interventions to help smokers quit.
Establishing Trustworthiness in Qualitative Research
Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.
Credibility in Qualitative Research
Credibility refers to how accurately the results represent the reality and viewpoints of the participants.
To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).
To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985).
Transferability in Qualitative Research
Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).
Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017).
Dependability in Qualitative Research
Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.
Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).
Confirmability in Qualitative Research
Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.
Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).
This enables readers to understand the reasoning behind the decisions made.
Audit Trails in Qualitative Research
An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data.
The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.
A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).
Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail.
Advantages of qualitative research
Discovery of unexpected data
Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.
This allows unexpected data to emerge, which can lead to further research into that topic.
Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.
This enables researchers to investigate new opportunities while firmly maintaining their research goals.
Behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically.
Limitations of qualitative research
Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.
Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes.
The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.
Consequently, interpretations of data from another researcher may vary greatly.
The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.
Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.
Therefore, results are only applicable to a small group of the population.
Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:
- Situational variables: different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
- Participant characteristics: this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
- Experimenter effect: experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis.
What sample size should qualitative research be?
The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).
Are surveys qualitative or quantitative?
Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.
The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.
In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.
What are the ethical considerations of qualitative research?
Before conducting a study, you must think about any risks that could occur and take steps to prevent them.
Participant Protection: Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants.
Transparency: Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data.
Confidentiality: You need to consider how to maintain the confidentiality and anonymity of participants’ data.
What is triangulation in qualitative research?
Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings.
Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).
Why is qualitative research important?
Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.
In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.
Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.
What is coding data in qualitative research?
Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.
These labels may be words or phrases which represent important (and recurring) patterns in the data.
This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.
What is the difference between qualitative and quantitative research?
Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.
This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.
In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.
Quantitative data may be collected using closed-ended questionnaires or experiments.
What is trustworthiness in qualitative research?
Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.
Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.
Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).
What is data saturation in qualitative research?
Data saturation is a methodological principle used to guide the sample size of a qualitative research study.
Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis.
The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.
Why is sampling in qualitative research important?
In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.
This is because quantitative research aims to provide generalizable conclusions that represent populations.
However, the aim of sampling in qualitative research is to gather data that will help the researcher to understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.
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