Theoretical Sampling | What Is It & How To Do It

Introduction

Theoretical sampling is a process used within grounded theory research and other qualitative studies to guide qualitative data collection and analysis. It involves choosing data sources based on the evolving concepts and categories that emerge during the study. Instead of deciding on a fixed sample at the start, researchers select participants, events, or documents as patterns and themes begin to form. By following this strategy, the researcher ensures that the data gathered will help sharpen and refine the emerging theory.

It also allows for flexibility in developing insights from emerging data analysis, since adjustments can be made as understanding of the topic grows. The primary goal is to gather information that directly informs the theoretical framework under development. This approach contrasts with traditional sampling, which often relies on predetermined methods rather than ongoing, data-driven decisions.

What is theoretical sampling in grounded theory research?

Theoretical sampling in grounded theory analysis involves choosing data sources as new concepts begin to take shape. In theoretical sampling, the researcher makes decisions based on what early findings suggest. For example, if interviews reveal that a particular condition seems to influence how individuals respond to a situation, the next step might be to speak with more participants who share that condition. If another pattern appears midway through the data collection process, sampling shifts again to gather information about that pattern. This iterative process continues until there are no significant new observations that add to the emerging categories.

A key difference from standard sampling methods is the emphasis on ongoing refinement. Instead of outlining every aspect of the sample at the start, the study evolves as data are gathered. Each piece of information guides the path forward. As categories solidify, the researcher pinpoints gaps that need more detail. This approach reduces the chance of missing factors that might only become clear once the work is underway. It also requires a willingness to adjust plans as the picture of the topic expands. Grounded theory builds on these adjustments, aiming to link findings directly to the developing framework.

Theoretical sampling takes place alongside coding and analysis. Emerging categories inform the decision about what to investigate next. Researchers note patterns, categorize them, and look for connections. They then compare new data against these categories to see if they hold up or need adjustment. This comparison ensures that each component of the study is continuously tested. As the focus narrows, new data might support an existing category or show a need for refinement. Once a category no longer produces novel findings, it may be close to reaching theoretical saturation.

The flexibility of theoretical sampling helps address potential blind spots. It allows a deeper view of the core processes that influence the topic, without locking into a rigid sampling plan that overlooks unexpected findings. Because grounded theory is built step by step from the information collected, theoretical sampling supports the process by directing attention to areas of inquiry that will clarify and strengthen the emerging framework.

Why conduct theoretical sampling?

Theoretical sampling addresses the evolving nature of grounded theory. It allows researchers to respond to developing ideas as soon as they appear, instead of adhering to a single plan throughout the project. By collecting and examining data in response to the initial patterns that emerge, researchers maintain a close link between what they observe and the categories that begin to take shape. The process offers a method for refining questions and gathering details that matter to the theory under construction. Below are three reasons why this approach can be useful when conducting a grounded theory study.

Encourages flexible data collection

Traditional sampling methods often involve setting up a strict plan before any data are collected. Once the data collection begins, there is limited room to adjust. Theoretical sampling, on the other hand, leaves space to refine decisions as the work unfolds. If early findings suggest an unexpected angle or a new category, the researcher can redirect attention to that aspect without discarding earlier material. This flexibility helps address lines of inquiry that might otherwise be missed. It also allows the researcher to stay open to variations in participant experiences. By moving from one set of data to the next based on what has already been uncovered, researchers can follow a thread of evidence that directly informs the questions guiding the study.

Links emerging categories with real-time evidence

Grounded theory relies on the idea that concepts and categories develop directly from the data at hand. Theoretical sampling supports this by ensuring that the collection of new data is driven by these developing categories. If a category appears unclear or incomplete, the researcher can seek out additional participants or sources to clarify its properties. If a pattern seems well-established, the project can move on to investigate another area of interest. This step-by-step process keeps the researcher focused on gathering information that directly affects how the theory takes shape. Rather than collecting data for data’s sake, every new interview, observation, or document is carefully chosen. This approach can strengthen the evidence supporting each category, since the data gathered are targeted rather than random or overly broad.

Reduces the risk of overlooking important factors

Grounded theory studies aim to build frameworks that reflect the reality of the research context. A fixed sampling plan may not account for new factors that emerge partway through data collection. Theoretical sampling reduces this risk by allowing the researcher to redirect their efforts as soon as new points surface. If a fresh theme appears, time and resources can be allocated to investigate it further. This helps prevent overlooking influences that were not predicted at the beginning. It also promotes a more direct connection between the data and the developing framework. By adjusting who or what is studied based on ongoing findings, theoretical sampling supports a process that remains open to information that may reshape how the researcher understands the topic.

Example of theoretical sampling

A practical illustration can help show how theoretical sampling might work when analyzing qualitative data through a grounded theory method. Imagine a study examining how new nurses adjust to frequent changes in workplace protocols. The researcher begins with a few initial interviews of nurses who have been on the job for less than a year. Early findings show that sudden announcements of changes cause confusion, especially when nurses do not receive clear guidance. In these interviews, many participants mention that support from immediate supervisors makes a difference in how quickly they adapt. The researcher notes this emerging idea—about the effect of supervisor guidance—and decides to investigate it further.

From there, the researcher targets another group: nurses who report having very hands-on supervisors. These participants describe feeling more confident about each change because their supervisors offer step-by-step clarifications. However, a few of these participants also mention frustration when supervisors are not consistent or when directions differ from one day to the next. This raises the possibility that constant oversight might sometimes create dependence, rather than true autonomy in handling changes. The researcher recognizes a potential secondary category: the perception of autonomy. It appears that some new nurses appreciate close supervision, while others feel that certain aspects of it keep them from learning on their own.

Based on this observation, the researcher looks for a third group: nurses who work in settings with minimal direct oversight. Some of these participants describe learning protocols through trial and error, relying on written guidelines, or turning to co-workers for help. Others say that this lack of direct supervision spurs them to learn quickly, but it also causes more stress. The researcher then refines the category of supervisor guidance, noting that it seems to interact with the concept of autonomy in different ways. At this stage, sampling decisions focus on balancing each piece of the emerging picture. The researcher gathers more information from settings with varying levels of oversight to see how these factors shape the nurses’ experiences.

By moving back and forth between the data and the developing categories, the researcher narrows down the main themes. Each new group of participants is chosen because their experiences shed light on the tentative categories. If another factor appears that might influence adaptation—such as the availability of peer mentorship—the researcher can look for nurses who have extensive peer support. This step ensures that categories develop from the most direct and relevant sources, rather than a predetermined set of participants.

This iterative research process continues until the researcher develops a sufficient understanding and explanation of the categories and how they are related. Through each shift in who is sampled, the researcher refines ideas about how supervisor guidance, autonomy, and peer support affect the experience of adapting to new protocols. By selectively gathering information that speaks to specific patterns in the data, theoretical sampling remains tied to the emerging structure of the study. Each new interview or observation is rooted in the goal of clarifying and expanding the categories that ground the theory.

When to use theoretical sampling

The theoretical sampling method is most effective in studies that aim to develop a grounded theory or inductive model, particularly when the final shape of the theory is not known at the outset. Researchers often begin with some ideas or sensitizing concepts but keep these open for modification as patterns emerge.

This approach suits studies where the subject matter is not thoroughly mapped or when existing theories do not fully address the questions at hand. It also works well in contexts that are expected to produce many different perspectives or experiences, since there is greater need to gather data in a way that follows the direction of early insights.

One indicator that it may be time to employ theoretical sampling is the discovery of a tentative category that needs further clarification. Suppose a researcher notices repeated references to a certain interaction or event during initial interviews. This might point to an important category that could inform the larger theory. Instead of following a fixed plan, the researcher can seek out participants or data sources closely tied to that interaction. The goal is to understand how it appears across different situations and whether it connects with other emerging categories.

Another situation where theoretical sampling proves useful is when the study deals with processes that unfold over time. Examples might include how people adapt to organizational changes, how individuals cope with chronic conditions, or how group members negotiate roles in shifting circumstances.

In these cases, the researcher might begin by interviewing a broad set of participants, then realize that a certain phase or transition is pivotal. If so, the researcher can focus on data sources most likely to shed light on that phase, adjusting as needed if new phases or aspects surface later. This adaptability often gives grounded theory studies a level of detail that might be missed in more static designs.

Theoretical sampling also helps when the population under study is not uniform, and there is a chance that various subgroups will show different patterns. Early on, the researcher may not know who all these subgroups are. As soon as one becomes visible, it can be brought into the study. If another subgroup later proves to be important, that new segment can be explored as well. This process stops only when additional sampling does not add relevant information to the categories being constructed.

Some research designs place significant constraints on data collection, such as limited access to participants or sensitive topics that require special permissions. Even within these constraints, theoretical sampling can guide how to use the available time and resources.

Rather than interviewing a large, predetermined group, the researcher targets those most likely to offer insights on the categories that are emerging. This approach can be adjusted to fit fieldwork, interviews, observations, or archival research. Once patterns start to stabilize, the researcher shifts attention to checking those patterns against new data sources to see if they hold.

Researchers typically choose theoretical sampling when their aim is to build a theory grounded in direct observation or interview data. Its iterative nature supports a process where early findings shape subsequent steps, allowing the study to unfold in a way that follows the trail of evidence.

How to conduct theoretical sampling

Conducting theoretical sampling calls for a structured yet flexible process. The aim is to let the emerging categories guide the choice of data sources, so that each new piece of information refines the framework under development. Rather than sticking to a single plan, the researcher follows clues from the data and adjusts as new ideas come into focus. Below are three steps that outline how to put this approach into practice.

Start with open coding and initial sampling

Begin by collecting a small set of data, such as a few interviews or observations, and review them using an open coding process. Assign labels to any concepts that stand out, noting similarities or differences between participants and events. Look for anything that seems relevant to the main question of the study.

At this point, the sample may be broad or somewhat random, as the purpose is to gather enough variety to form initial impressions. The goal is to see which themes or issues appear relevant to your research question. Once you identify tentative categories, use them to set the direction of your next data collection activities. If a specific phenomenon shows up repeatedly in these early interviews, you may want to focus on finding more participants who can speak to that phenomenon in detail.

Iterate, compare, and refine

As new data come in, continue coding, compare them to your existing categories, and note any points that need more detail. If certain categories seem underdeveloped, target data sources that address those gaps. For instance, if participants consistently mention time constraints but you have not explored them fully, seek out settings or individuals who can offer perspectives on this issue.

During this phase, it helps to keep memos that track how each category is evolving. These memos include thoughts on why a category matters, how it connects to others, and what features still need better definition. Remain open to revising or merging categories if the data suggest that two concepts are linked, or if one idea is a narrower version of another. Each round of data collection and analysis should help tighten the focus of the study.

Aim for theoretical saturation

Continue until gathering more data does not lead to significant revisions of your categories. This stage, known as theoretical saturation, occurs when each category is detailed enough to explain what the data are showing. You begin to notice that participants offer views that fit comfortably within categories you have already outlined.

Consider any negative cases or outliers that challenge the categories. Investigating why these cases differ can help refine your ideas and confirm whether the categories are stable. When it becomes clear that a certain theme or pattern keeps showing up in the same way, further sampling in that area may not add new insights. At that point, sampling in another area can help ensure that the emerging framework covers all major angles of the topic. Once you reach a place where your model or framework is fully explained and captures your data, the theoretical sampling process is complete.