When understanding what drives customer behaviour, spreadsheets and pie charts can only take you so far. Marketers must turn to qualitative research to uncover the deeper why behind purchasing decisions, brand loyalty, or consumer hesitations.

But here’s the catch: rich, meaningful insights don’t just appear; they begin with asking the right people the right questions. As a marketer, understanding your audience goes far beyond numbers and metrics.

It’s about uncovering deeper motivations, emotions, and experiences: the kind of insights that only qualitative research can deliver. But to tap into those stories, you first need to connect with the right people. And that begins with knowing how to choose your sample.

Let’s define qualitative research

Qualitative research is a method used to explore people's beliefs, experiences, behaviours, and interactions. Unlike numbers-driven analysis, it focuses on depth rather than breadth, helping marketers gain a rich, nuanced understanding of the audience’s motivations and thought processes.

Qualitative data is typically gathered through interviews, focus groups, and observations to uncover the “why” behind consumer actions.

For businesses seeking a clearer picture of what drives decision-making, a reliable consumer research service in Singapore can offer invaluable support in conducting and interpreting this research.

Where quantitative research seeks patterns in the population, qualitative research dives into personal experiences, offering marketers detailed insights into the phenomenon of interest.

How qualitative is different from quantitative

The key distinction lies in what each approach tries to answer. Quantitative research answers the research question with measurable data, percentages, rankings, and statistics, often through surveys or polls.

Qualitative research, on the other hand, is exploratory. It uses open-ended questioning to explore behaviours, perceptions, and motivations.

Here’s a quick comparison:

• Quantitative: How many people prefer product A over B?
• Qualitative: Why do people prefer product A over B?

While quantitative studies often rely on probability sampling to ensure statistically significant results, sampling in qualitative research requires more flexibility. This allows the researcher to uncover rich insights from a smaller but more targeted sample.

Why qualitative research is crucial for marketing

In marketing, success depends on understanding what your customers do and why they do it. That’s where qualitative research shines. It helps uncover customer motivations, emotions, and needs that can’t be fully captured through numeric data alone.

Through qualitative data collection, marketers can:

• Test product concepts and brand messaging in real-world contexts.
• Explore unmet customer needs.
• Understand reactions to marketing campaigns.
• Identify user experience pain points.

Done right, qualitative sampling gives marketers access to participants who can provide detailed feedback, making campaigns more relevant, brands more human, and strategies more customer-centric.

How to decide on a sampling method for qualitative research

Choosing the best sampling method depends on several factors:

• The purpose of the study
• The research question
• Available time and resources
• Who your ideal participants are

Unlike probability sampling, sampling methods in qualitative research fall under non-probability sampling, meaning not everyone in the population has an equal chance of being selected. Instead, the researcher selects participants who are most likely to be informative.

Here are a few guiding questions to help you select the most appropriate type of sampling:

• Are you trying to explore a new topic or test a specific theory?
• Do you need experts, everyday consumers, or hard-to-reach populations?
• Is your focus on depth or diversity?

Your choice of sampling strategies shapes the data you collect. Let’s explore the most common types of sampling used in qualitative research, starting with the most accessible.

Find out the top methods to conduct consumer research here

Max out limited resources with convenience sampling

Convenience sampling is exactly what it sounds like: selecting easily identifiable and readily available participants. It’s often used in qualitative research when time, budget, or access is limited.

For example, you might survey employees within your company, ask friends or colleagues for input, or collect feedback at a local event. This sampling method doesn’t aim to represent the population fully, but it’s quick, low-cost, and can yield valuable preliminary insights.

Why you would (or wouldn’t) use convenience sampling

You would use convenience sampling when:

• You need fast, early-stage feedback.
• You’re conducting pilot studies.
• Resources or access are limited.
• You want a sample to test research instruments or methods.

You may not want to use it when:

• You need a representative sample.
• Your study involves sensitive topics or diverse opinions.
• The stakes of the decision are high (e.g., product launches or rebranding).

In short, while convenience sampling is helpful in the early stages of the research, it may not be ideal when making strategic decisions that require stronger data. As with all sampling techniques, it’s essential to identify the right method for the study.

Find respondents with snowball or referral sampling

Snowball or referral sampling relies on existing participants recruiting others who fit your criteria. It’s particularly useful when researching niche or hard-to-reach groups, such as industry experts, specific user demographics, or people with particular behaviours or experiences.

This sampling method is built on trust and social networks. For example, if you’re exploring online shopping habits among new mothers, you could begin with one participant who refers you to others within her network.

Why you would (or wouldn’t) use snowball sampling

You would use snowball sampling when:

• Your research focuses on small or specialised groups.
• You’re working with sensitive or personal topics.
• You need access to communities that are not easily reachable through traditional means.

You may not want to use it when:

• You’re aiming for diversity or broad coverage across demographics.
• You want to avoid groupthink or network bias.
• You require more control over the sample.

Though snowball sampling can be highly effective in qualitative research, its informal nature means the sample is unlikely to be representative of the population.

Find experts with purposeful or judgmental sampling

When the goal is to gather expert insight or learn from participants who can provide rich, relevant detail, purposeful sampling, also known as judgmental sampling, is one of the most effective sampling methods in qualitative research.

With this approach, the researcher selects individuals based on specific characteristics that align with the study's purpose. This type of sampling is particularly valuable when seeking perspectives from professionals, opinion leaders, or customers with in-depth experience.

Why you would (or wouldn’t) use purposive sampling

You would use purposive sampling when:

• You require targeted feedback from informed users.
• You want qualitative data that directly answers the research question.
• Your sampling strategies aim for relevance over randomness.

You may not want to use it when:

• You need broader representation across the population.
• You lack access to the desired experts.
• You want a quick-and-easy process like convenience sampling.

Unlike convenience sampling, which is typically driven by ease of access, purposive sampling is more intentional. The sample is carefully selected to ensure each participant can contribute meaningfully to the research. It’s ideal when the richness of the data is more important than how many people you speak to.

Handpick respondents with criterion sampling

Criterion sampling is a non-probability sampling technique in which the researcher selects individuals who meet pre-established criteria that are directly relevant to the study's focus. This sampling method ensures that every person in the sample shares a certain experience, behaviour, or demographic marker.

For example, a marketer researching reactions to a new parenting app might use criterion sampling to select mothers with children under five who regularly use mobile applications.

Why you would (or wouldn’t) use criterion sampling

You would use criterion sampling when:

• You’re studying a very specific user experience.
• You need participants who’ve encountered the phenomenon of interest.
• You’re focusing on a single segment of the population.

You may not want to use it when:

• You’re aiming for varied perspectives across different user groups.
• Your sampling techniques must adapt to broader demographic coverage.
• You lack the tools to identify participants who fit the exact criteria.

Criterion sampling is excellent for drawing clear insights from a focused group. However, because the sample is restricted, it is possible that findings may not be generalisable beyond the participants selected.

Cover all your bases with quota sampling

Quota sampling is a structured method that divides the population into subgroups and ensures each is proportionally represented in the sample. It’s often used when marketers want to compare responses across demographics such as age, gender, or income level.

This sampling method is particularly useful when diversity is critical, but probability sampling is not feasible. For example, if you want feedback from 100 consumers, you might set quotas to include 50 women and 50 men, ensuring balanced input.

Why you would (or wouldn’t) use quota sampling

You would use quota sampling when:

• You want to capture differences across key demographics.
• Your sampling in qualitative research needs to reflect multiple customer segments.
• You’re short on time but still want a more representative sample.

You may not want to use it when:

• You need truly random sampling (like simple random sampling).
• Your sample size is too small to split effectively.
• Your focus is on depth from a specific subgroup.

While quota sampling can’t claim the statistical purity of probability sampling, it balances structured representation with the flexibility of non-probability sampling, making it ideal for exploratory qualitative research across segments.

Go in-depth with theoretical sampling

Theoretical sampling is a dynamic sampling method used primarily in grounded theory studies. It focuses on selecting participants based on emerging themes, not initially, but during the process. As insights develop, the researcher adjusts the sampling strategy to explore areas that need more detail or clarification.

For instance, if a marketing team is exploring attitudes towards ethical fashion and early interviews reveal a common concern about supply chain transparency, they might choose new participants to speak to that concern in more depth.

Why you would (or wouldn’t) use theoretical sampling

You would use theoretical sampling when:

• Your qualitative data collection evolves during the study.
• You’re developing new theories based on the data.
• You need an in-depth exploration of themes as they arise.

You may not want to use it when:

• Your project has strict timelines or rigid structures.
• You need to define the sample upfront.
• You’re working on predefined commercial deliverables rather than academic theory.

Theoretical sampling allows for flexible, responsive sampling in qualitative research but requires experience, adaptability, and time. When managed properly, it offers rich insights for marketers seeking a specific understanding of complex customer behaviour.

Other qualitative sampling methods

Although non-probability sampling is typically associated with qualitative research, some probability sampling methods can still be useful when adapted thoughtfully. In mixed-method designs or large-scale qualitative studies, these approaches provide an additional layer of structure that helps broaden the sample's reach.

Cluster sampling

Cluster sampling involves dividing the population into separate groups or clusters, often by geography, organisation, or social group. Then, these clusters are selected randomly, and the participants within them are studied.

This sampling technique can be particularly helpful when conducting field studies across multiple locations. If you're gathering qualitative data on brand perception across different Singaporean districts, you might randomly select neighbourhood clusters and then interview residents from each.

You would use cluster sampling when:

• You have logistical or geographical constraints.
• Your resources are limited, and you're sampling within confined clusters.
• You want structured variation in a sample.

However, it may not be ideal if the sample is too homogenous within each cluster, limiting the diversity of the data.

Simple random sampling

Simple random sampling is the most straightforward type of sampling under probability sampling. It gives every individual in the population an equal chance of selection.

While this method is most common in quantitative research, it can be adapted for qualitative purposes, especially in large participant pools where bias needs to be minimised.

However, for smaller, insight-driven studies typical of qualitative research, this method often lacks the intentionality that sampling in qualitative research demands. The key is balance, random selection with purposeful filtering during qualitative data collection.

Stratified random sampling

Stratified random sampling divides the population into strata or subgroups based on shared characteristics and then randomly selects participants from each stratum. It allows for proportional representation, ensuring diversity within the sample.

For marketers conducting qualitative UX interviews, stratification by device usage ensures input from all relevant segments. Although often more resource-intensive, this sampling method is valuable when representativeness and structure are crucial, especially when paired with in-depth qualitative interviews or observation.

How to collect data for qualitative research sampling

Once you’ve decided on your sampling method, the next step is gathering the data. Effective qualitative data collection depends on techniques that enable the researcher to identify emotions, motivations, and perceptions in context.

There are three primary ways qualitative data is collected in marketing studies:

Interviews and focus groups

Interviews in-person, online, or phone-based allow the researcher to engage directly with the participants, asking open-ended questions that encourage honest and layered responses. They are especially useful one-on-one when exploring sensitive or complex topics.

Focus groups bring multiple people together, often from a specific customer segment, to discuss opinions in a moderated setting. These are invaluable when testing messaging, gathering initial reactions, or observing group dynamics.

Both are commonly supported by observation, where researchers study behaviour in natural or simulated settings. Observational sampling techniques are helpful when the phenomenon of interest is behavioural or difficult for respondents to articulate.

Written and visual documentation

In addition to spoken feedback, qualitative research often involves analysing written and visual documentation. This might include:

• User journals
• Open-ended survey responses
• Social media posts
• Photographs or video diaries
• Marketing collateral or packaging reviews

Qualitative data collection is particularly effective when real-world reflections must be captured over time.

Data collection challenges in qualitative research sampling

Even the most well-planned sampling methods in qualitative research face hurdles during execution. While qualitative approaches offer depth, they are also more vulnerable to bias, logistical issues, and emotional nuances that make data collection and interpretation more complex.

Here are some common challenges faced by researchers conducting sampling in qualitative research:

• Not everyone you need may be easily reachable or willing to participate. Expert users or niche groups often require additional effort or incentives.
• Qualitative data collection is often time-intensive, unlike large-scale surveys. Conducting in-person interviews, arranging focus groups, or analysing video journals demands planning and patience.
• Without clear criteria, the sample may become skewed or overly reliant on convenience sampling, limiting the reliability of the study.
• The data can be shallow or inconsistent if questions are unclear or the participants don't engage fully.

These issues underscore why carefully selecting and applying the right sampling method is essential for the success of qualitative research.

Best practices for qualitative research sampling methods

Following key best practices is important to get the most out of your sampling techniques. These not only improve the data quality but also protect the integrity of the study and its findings.

Staying ethical

Ethical considerations should underpin all types of sampling. Whether you're using purposive, quota, or snowball sampling, it’s essential to the research process that participants understand what they’re getting into.

• Always obtain informed consent.
• Protect participant confidentiality.
• Avoid deceptive practices or undue pressure.

Following ethical guidelines set by institutions like the Ministry of Health Singapore ensures your research is above board and respectful.

Choosing respondents

Who you select, and how, can make or break your findings. Your sampling method should align directly with the purpose of the study. If you aim for expertise, use purposeful sampling; quota sampling may be more suitable for comparing demographics.

Ask:

• Does the sample reflect the phenomenon of interest?
• Are there voices or segments being excluded unintentionally?

This alignment between the sampling strategy and the research question leads to richer insights and fewer blind spots.

Diversifying samples

Avoid echo chambers. In qualitative research, even small samples should strive for diverse perspectives. Maximum variation sampling, a form of purposeful sampling, seeks participants who can provide differing views, helping uncover nuances that more homogenous groups might miss.

Diversifying the sample ensures that your findings aren’t narrowly focused and are more likely to be representative of the wider customer base.

Formulating questions

Once your sampling strategy is in place, ensure your questions support it. Qualitative data is only as good as the prompts that draw it out.

• Use open-ended, non-leading questions.
• Be culturally sensitive and clear.
• Revisit and revise your guides as needed.

When you match thoughtful question design with the right sampling method, you maximise the likelihood that the data will be rich, honest, and actionable.

Assessing the effectiveness of the sampling methods

You’ve selected your sampling technique, gathered your qualitative data, and conducted interviews—but how do you know it worked?

To evaluate the success of your sampling method, ask the following:

• Did your sample reflect the population or group under study?
• Were the insights detailed and relevant to the research aims?
• Did any important voices go unheard?
• Was your sample size sufficient for the scope of the study?

Look back at the purpose of your project. If your sampling in qualitative research enabled you to identify meaningful themes, unique viewpoints, and unspoken assumptions, then your method likely succeeded.

Even with non-probability sampling, effectiveness depends on planning, adaptability, and reflection. In fact, evaluating the sample is just as important as designing it.

Conclusions

Choosing the right sampling in qualitative research is both a science and an art. Marketers who master sampling methods, understand context, and uphold ethical standards are better positioned to build meaningful connections and sharper, more resilient brands.

Milieu is one of the leading online survey software providers and market research agencies in Singapore, helping marketers navigate the complexities of qualitative research with confidence. We make it easier for teams to choose the right sampling methods by delivering timely, data-backed insights that reflect the real voices of today’s consumers.

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