A Guide to Qualitative Research Data and Sampling Techniques

A qualitative research study is a form of research that seeks to understand and explain social phenomena, individual experiences, and behaviors through non-numerical data. Identifying an appropriate data source and employing effective sampling techniques poses a significant challenge to many students and emerging researchers, particularly in the global south. This calls for an in-depth understanding of the two broad concepts. 

Sampling techniques in research involves selecting a subset of individuals or cases from a large population to study, as it is often impractical or impossible to study an entire population. The choice of a particular sampling technique is vital as it directly impacts the generalizability and validity of the findings. The relevance of these techniques is concerted in their ability to balance practical considerations, such as time and resources, with the need for meaningful insights, ultimately facilitating the generation of accurate and applicable knowledge from a subset of the larger population.

Forms Of Qualitative Research Data

Qualitative research data consists of non-numerical information that helps researchers gain a deeper understanding of the underlying meanings, patterns, and nuances within specific context. Qualitative data can come from varied sources, such as documents, archival records, direct and participant observation, physical artifacts, and interviews.

Qualitative research data consists of non-numerical information that helps researchers gain a deeper understanding of the underlying meanings, patterns, and nuances within specific context.

Document: Document data refers to written or printed materials that provide information about a specific phenomenon or event. This type of data can be useful for researchers to gain insights into their research topic's historical, social, cultural, and political context. For example, a researcher studying the Civil Rights Movement may analyze historical documents such as letters, speeches, and newspaper articles to understand the movement's context.

Archival Records: Archival records are historical records that have been preserved in archives, museums, or libraries. These records can include personal documents, government records, photographs, and other materials that provide researchers with valuable insights into the past. Researchers studying history or social movements may use archival records to examine primary sources of information that shed light on their research topic.

Direct observation: Direct observation involves researchers observing and recording behavior and interactions in real-time. This type of data can provide researchers with insights into human behavior in natural settings, such as homes, schools, and workplaces. For instance, a researcher studying classroom dynamics may observe and record teacher-student interactions to gain insights into student engagement.

Participant Observation: Participant observation involves researchers actively participating in their research activities or events. This approach allows researchers to gain a more in-depth understanding of the culture, norms, and practices of the group they are studying. For example, a researcher studying a religious community may attend religious services and participate in community activities to gain insights into their beliefs and practices.

Physical Artifacts: Physical artifacts are physical objects that provide insights into a particular phenomenon or culture. Examples include art, clothing, tools, and other artifacts that can provide insights into the beliefs, values, and practices of a group or culture. A researcher studying the culture of a particular region may analyze traditional clothing or artwork to gain insights into their cultural practices.

Interview: Interview data involves asking questions to individuals about their experiences, attitudes, beliefs, and perceptions. Interviews may take on either a structured or unstructured format and may be conducted in-person, over the phone, or online (i.e., zoom etc.). Researchers may use an interview to gain insights into individuals' subjective experiences and perspectives on a particular topic. For instance, a researcher studying mental health may conduct interviews with individuals to gain insights into their experiences with mental illness.

Qualitative data can come from varied sources, such as documents, archival records, direct and participant observation, physical artifacts, and interviews.

Audiovisual: Audiovisual data is a type of qualitative research data involving recorded visual and auditory information. It aims to understand subjective experiences, attitudes, and beliefs. It captures the richness and complexity of human experience and provides insights into behaviour's social, cultural, and psychological aspects. It provides a more holistic view of the research topic by observing facial expressions, tone of voice, and body language. Analyzing and interpreting audiovisual data can be complex and requires specialized tools such as QDA Miner for coding, transcription, and analysis. It is valuable in providing rich and detailed insights into human experiences and behavior but requires careful planning, preparation, and analysis.

12 Key Sampling Techniques in Qualitative Research

To collect qualitative data, researchers may use a variety of sampling techniques to select participants for their study. However, selecting an appropriate sampling technique significantly enhances the accuracy and reliability of research results. The following are twelve key sampling techniques that students and emerging researchers could consider:

i. Convenience sampling: This is a sampling technique that ensures the selection of participants who are readily available and easily accessible to the researcher. Instead of using random and systematic techniques, convenience sampling relies on the convenience and proximity of potential subjects. For instance if a researcher aims to study the opinions of shoppers in a mall, they might approach individuals who are easily accessible within the mall environment. While convenient for the researcher, it is important to recognize that the sample may not fully represent the diverse perspectives within the entire community.

ii. Purposive sample technique: This entails choosing participants based on certain characteristics or criteria relevant to the phenomenon under study. Purposive sampling is often used when the researcher wants to select participants who have knowledge or experiences relevant to the research questions. For instance, a researcher studying the experiences of first-generation college students might purposefully select participants who are the first in their families to attend college, ensuring a target exploration of the phenomenon. This technique may result in a sample that is not representative of the population, but it allows for the selection of participants with specific insights.

iii. Snowball or chain sampling: This technique is a multistage sampling method that starts with a few people and grows through referrals. The initial respondents are selected using alternative methods, such as purposive or random sampling. Subsequent respondents are then identified based on directions by the initial respondents. For example, a researcher investigating the social dynamics of a specific subculture might start with one participant and ask that participant to refer others within the same subculture.

iv. Theory-based or operational construct sampling: This involves selecting participants based on a theoretical framework or operational definition of the construct being studied. Theory-based sampling is often utilized when researchers seek to secure a sample that accurately represents the construct. Theoretical sampling represents an innovative aspect of the grounded theory method, a systematic research approach that constructs concepts and theory from data. For instance, during a grounded theory study on the challenges faced by remote workers, the researcher might initially interview individuals with flexible work arrangements. As new themes emerge, the sampling criteria may be adjusted to include participants facing different challenges, leading to the refinement of emerging theories.

v. Quota Sampling: This sampling involves establishing predetermined quotas for certain characteristics, such as age, gender, or occupation. The goal is to ensure diversity in the sample and represent different segments of the population according to specified criteria. For instance, a researcher interested in understanding perspectives on climate change might set quotas for age groups and geographical regions, ensuring a representative sample.

vi. Maximum Variation Sampling: This sampling aims to include participants with a range of perspectives or experiences related to the phenomenon being studied. This method helps researchers capture the diversity inherent in the subject matter. In a study on workplace dynamics, a researcher might purposefully select participants with diverse job roles, levels of experience, and backgrounds to ensure a comprehensive understanding of the organizational culture.

vii. Criterion Sampling: This sampling involves selecting participants who meet specific criteria relevant to the research question, Researchers establish clear criteria and choose participants based on their alignment with those criteria. For example, in a study on the impact of a training program, participants might be selected based on their successful completion of the training, ensuring the inclusion of individuals directly affected by the intervention.

viii. Critical Case Sampling: This sampling focuses on selecting cases that are considered critical or pivotal to understanding the central aspects of the phenomenon. These cases are chosen because they are expected to provide essential insights or challenges to existing theories. If researching the effects of a new educational policy, the researcher might select a school where the policy has had a significant impact, expecting that insights gained will be crucial to understanding the broader implication.

ix. Homogeneous sampling: This involves selecting participants who share a common characteristic or experience. Homogeneous sampling is often used when the researcher wants to compare different perspectives or experiences related to the research question. This technique allows for the identification of patterns and themes within the sample. In a study of cultural practices, the researcher might choose participants from a specific ethnic group to explore shared cultural traditions and values.

x. Heterogeneous Sampling: This involves selecting participants with diverse characteristics or experiences. This method is chosen when researchers aim to capture a broad range of perspectives and variations within the study population. A researcher interested in understanding perceptions of artificial intelligence might purposefully select participants from diverse backgrounds, including technology experts, ethicists, and the general public. 

xi. Sequential Sampling: This involves adding participants over time and it is commonly used in longitudinal or iterative research enabling the researcher to gather data at different points to capture changes, patterns, or developments over time. Researchers employing sequential sampling adapt their sampling strategies based on emerging insights, allowing for a dynamic and iterative research process. For example, in a longitudinal study on the career trajectories of professionals in a rapidly evolving industry, the researcher may collect data from participants at different stages of their careers over several years.

xii. Expect Sampling: This involves selecting participants who are considered experts in the field of study. This method is employed when researchers seek specialized knowledge or insights from individuals with a high level of expertise in a particular subject area. For instance, researchers investigating the impact of climate change policies on urban planning, may select participants such as renowned environmental scientists, experienced urban planners, and specialized environmental policymakers.

Successful qualitative research is like crafting a masterpiece; it requires skill, precision, and a thoughtful approach.

Conclusion and Insights

Successful qualitative research is like crafting a masterpiece; it requires skill, precision, and a thoughtful approach. Just as an artist carefully selects colors, textures, and techniques to create a meaningful work of art, a qualitative researcher must thoughtfully choose research methods, sampling techniques, and data sources that align with the research objectives. By aligning what you want to discover with how you plan to study it, you unlock the door to insightful and meaningful exploration, turning your research into a work of art.

Implications for Students and Emerging Researchers

Emerging researchers must take keen note of the following in selecting an appropriate data source and a sampling technique.

• Students must develop a keen awareness of cultural sensitivities and ethical guidelines, ensuring the research work respects the rights and perspectives of participants.
• Students must actively seek opportunities to diversify their research skills by engaging in workshops, online courses, and hands-on experiences to deepen their understanding of various data sources and sampling techniques.
• Students must foster mentorship relationships with experienced researchers and collaborate with peers. Learning from others’ experiences can provide valuable insights and practical tips. This could be done in the form of Journal Clubs like the various AuthorAID Journal Clubs
• Before embarking on the full-scale study, conduct a pilot study to validate your data source and sampling techniques. This practice allows potential researchers to identify pertinent challenges and make necessary adjustments.

Stephen Bannah is currently a member of the AuthorAID Ghana Hub where he supports national partnerships for research. He is also a Master of Philosophy in Accounting student at the University of Ghana exploring the issues of climate change and its disclosure. He is also a researcher at the Dataking Research Lab. Stephen also holds a BSc in Accounting Education at the University of Education, Winneba.