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Targeted Sampling
Targeted Sampling

Targeted Sampling
Guide with Examples

Dive into the world of targeted sampling, as we explore its diverse types, techniques, and practical examples, enhancing your data collection skills for insightful, focused research outcomes.

In the intricate world of market research, the quest for accurate, reliable, and insightful data is of paramount importance. One methodology that has proven immensely beneficial in this pursuit is targeted sampling. This method allows researchers to zero in on specific segments within a population that are of particular interest to their study, thereby yielding more relevant and valuable data. However, like any research tool, it comes with its own set of nuances that can influence its effectiveness.

In this comprehensive guide, we will explore the diverse types of targeted sampling, delve into the techniques used in implementing them, and illuminate these concepts with practical examples.

What is a Targeted Sampling?

Targeted Sampling, also known as purposive or judgmental sampling, is a type of non-probability sampling technique where the researcher specifically selects individuals from the population who possess particular characteristics or knowledge that can provide insight on the research question. The uniqueness of this sampling technique lies in its objective: to focus on certain characteristics of a population that are of interest, which will best enable answers to the research question.

The importance of targeted sampling cannot be overstated in the world of research, where obtaining accurate, insightful data is paramount. In studies where specific subsets of a population are of interest, or when particular groups are hard-to-reach, targeted sampling proves invaluable. It allows for a depth of understanding that is often unachievable with broader, more general sampling techniques.


Purposive Sampling – by Prof. Essa (03m:00s)

Types of Targeted Sampling with Examples

  • Convenience Sampling

    Convenience sampling, also known as opportunity sampling, involves collecting data from individuals who are conveniently available and willing to participate. The focus here is not on selecting a sample that is representative of the entire population but rather on ease of access.

    Example: A new coffee shop trying to figure out which kinds of pastries to sell might ask each customer over a certain week what their favorite pastries are. While this method doesn’t capture the preferences of all potential customers, it does provide immediate and easily-accessible data for the coffee shop to base its initial menu on.
  • Probability Sampling

    Probability sampling ensures that each member of the population has an equal chance of being chosen for the study. This method is often employed when the population size is large, and the objective is to draw generalized conclusions.

    Example: A skincare company wanting to understand consumer satisfaction with their products might randomly select 500 buyers from their customer database for a survey. Since every customer has an equal chance of selection, the survey results can be confidently generalized to the entire customer base.
  • Cluster Sampling

    In cluster sampling, the researcher divides the total population into clusters, or groups, and then randomly selects clusters to form the sample. This technique is especially useful for large, geographically dispersed populations.

    Example: Suppose a multinational clothing brand wants to analyze fashion trends. Instead of surveying individuals worldwide, they could divide the world into clusters (countries or regions) and randomly select a few clusters to survey. This way, they can gather insights about global fashion trends without having to survey every region.
  • Non-selection Sampling

    Non-selection sampling deliberately excludes a portion of the population that is not relevant to the research objectives. This method ensures that data collection is more focused and relevant.

    Example: A software development firm that’s testing a new feature in its latest app version might only collect feedback from users who have updated the app, excluding those who haven’t, as their feedback wouldn’t be relevant to the new feature.
  • Systematic Sampling

    In systematic sampling, a fixed interval is set, and then data is collected at those intervals. This method ensures that the sample is spread across the entire population.

    Example: A fitness club looking to improve its facilities might select every 10th entry from their member sign-in log for a feedback survey. This would ensure a wide range of members are included, covering different days and times.
  • Sampling of Patches

    Sampling of patches refers to the method where the researcher selects specific ‘patches’, or segments, of a population that are believed to be representative of the whole.

    Example: Suppose a car manufacturer wants to test the market response to a new electric vehicle model. They could select patches in a city (like eco-conscious neighborhoods) to offer test drives and collect feedback. The assumption is that these patches will provide insights reflective of the broader city’s opinion.
  • Sampling of Subpopulations

    This method involves focusing on certain subpopulations within the larger population, usually based on specific characteristics or behaviors.

    Example: An online education platform wishing to create courses for senior citizens might focus their research on the subpopulation of users over 60 years old. By targeting this specific subpopulation, the platform can better understand their learning needs and preferences.
  • Sampling by Boundary

    Sampling by boundary involves studying individuals on the ‘fringes’ or ‘boundaries’ of a specific demographic or behavioral group. This method can be especially helpful in trend forecasting.

    Example: A digital marketing firm trying to understand emerging trends in social media might study users who are early adopters of a new platform or feature, as these ‘boundary’ individuals often signal broader shifts in user behavior.
  • Stratified Sampling

    Stratified sampling involves segregating the population into non-overlapping subgroups, or strata, based on a common characteristic, then randomly selecting members from each subgroup. This method ensures that each subgroup is adequately represented in the sample.

    Example: Imagine a tech company wanting to evaluate customer satisfaction across different product lines (like laptops, smartphones, and tablets). They could divide their customer base into strata based on the product purchased and randomly select customers from each stratum for a survey.
types of targeted sampling

Benefits of Targeted Sampling

Suitability for Specialized Research Subjects

One of the key strengths of targeted sampling is its suitability for specialized research subjects. Research studies often aim to investigate very specific questions, requiring data from highly specific groups within the population. In such instances, targeted sampling can prove invaluable in that it enables the researcher to specifically select the individuals that will best answer the research question. For example, a study looking into the experiences of war veterans would ideally need to gather data from individuals who have served in the military during wartime. With targeted sampling, the researcher can intentionally select such individuals for the study, thus obtaining data that is highly relevant and insightful for the research question.

Efficiency in Resources

Another advantage of targeted sampling is its efficiency in terms of both time and resources. In targeted sampling, the researcher focuses on a specific subset of the population. This reduces the time and resources needed for data collection as the researcher does not have to cover the entire population. Additionally, in targeted sampling, the researcher may already have an idea of where and how to find the target sample, further enhancing the efficiency of the process. For instance, a researcher looking to study the impact of an intervention program on drug addiction might specifically target rehabilitation centers, thus making the data collection process more streamlined and efficient.

In-depth Data Collection

With targeted sampling, researchers can gain an in-depth understanding of a specific group or phenomenon. Because the sampling method is concentrated and focused, it allows for a deeper exploration of the issues at hand. For instance, a researcher investigating the impacts of a particular industrial pollutant on local wildlife would benefit from targeted sampling by focusing specifically on the affected species within the polluted area. By zeroing in on this particular group, the researcher can obtain detailed data that helps to comprehensively understand the pollutant’s impacts.

Useful for Preliminary Research

Targeted sampling can also be an excellent tool in preliminary research, where the objective is to understand a problem better before carrying out a more extensive study. By focusing on a specific subset of the population, researchers can gain initial insights and feedback that can guide the development of larger, more detailed studies.

Access to Hard-to-Reach Populations

Certain populations are challenging to study due to their characteristics or circumstances. These may include marginalized communities, people with rare conditions, high-ranking officials, or people living in remote locations. Targeted sampling can be a practical approach in such cases, where the focus is on a specific group that is otherwise hard to reach or study.

High Level of Control

Another major benefit of targeted sampling is the high level of control it gives the researcher over the sample selection. This can be particularly useful when studying sensitive topics or when access to potential participants is restricted. The researcher can use their discretion and understanding of the research topic to choose the most suitable participants.

How is Targeted Sampling Used in Data Analyses?

Targeted sampling is often used in data analysis when researchers want to focus on specific segments of a population or when dealing with unique research scenarios that make traditional probabilistic sampling techniques less feasible or practical. In targeted sampling, researchers specifically choose groups or individuals to be part of their study based on certain characteristics or criteria, which allows them to delve deeper into these particular segments. The data collected from this subset is then analyzed to draw conclusions that are specific to these groups, rather than attempting to generalize these findings to the entire population.

This technique is especially common in qualitative data research and analysis, where the goal is often to gain a detailed understanding of a specific phenomenon rather than make generalizations about a broader population. Because the data obtained from targeted samples can be particularly rich and comprehensive, it allows for detailed analysis of themes, patterns, and nuances that might be overlooked in a broader survey. By focusing on a select group, researchers can often gain a more detailed and nuanced understanding of their behaviors, attitudes, and experiences.

At the same time, targeted sampling can be used in quantitative research to examine specific hypotheses about particular subgroups within a population. For instance, a researcher might use targeted sampling to select a group of individuals with a specific medical condition, then analyze data from this group to examine patterns and outcomes related to the condition.

Tips on Using Targeted Sampling

  1. Clearly Define Your Research Question – The success of targeted sampling begins with a clearly defined research question. The technique is most effective when the researcher has a specific population segment they want to study. Thus, the first step in using targeted sampling is to identify your research question and clearly specify the population segment you wish to investigate.
  2. Understand Your Target Population – Understanding the demographic and psychographic characteristics of your target population is crucial in targeted sampling. Researchers must have a thorough understanding of the population’s characteristics, behaviors, attitudes, and lifestyle patterns. This understanding will inform the sampling process and help ensure that the right individuals are included in the sample.
  3. Consider Access and Reachability – Targeted sampling can often involve hard-to-reach or specific segments of the population. Therefore, it is crucial to consider how accessible your target population is and devise a plan for reaching these individuals. This might involve collaborating with local community groups, utilizing online platforms, or leveraging professional networks.
  4. Ensure Ethical Considerations – As with any research, ethical considerations are paramount in targeted sampling. Researchers must ensure that participants are chosen freely, that they are fully informed about the study’s purpose and methodology, and that their data is handled confidentially and securely. Obtaining informed consent is a must, and researchers should consider potential ethical issues that may arise specific to their target population.
  5. Create a Robust Data Collection Plan – In targeted sampling, the data collection plan needs to be robust and tailored to the unique characteristics of the target population. Researchers should consider the most appropriate methods of data collection, whether it be surveys, interviews, observations, or a combination of these, and adapt these methods to suit the needs and preferences of the target population.
  6. Be Prepared for Challenges – Targeted sampling may come with a unique set of challenges, particularly when dealing with specific or hard-to-reach populations. Researchers should anticipate these challenges and be prepared to adapt their methods as necessary. This could involve being flexible with data collection times and locations, considering language and cultural barriers, and being sensitive to any personal or social issues that may affect participation.
  7. Ensure Sample Size Adequacy – While targeted sampling may not yield a sample that is representative of the broader population, it is still essential to ensure that the sample size is adequate for the study’s objectives. Researchers should consider the statistical power of their study and select a sample size that will allow them to detect the effects or associations they are interested in.
  8. Consider the Use of Mixed Methods – In some cases, targeted sampling can be effectively combined with other sampling methods in a mixed-methods approach. This can provide a more nuanced understanding of the research question and allow researchers to triangulate their findings.


In conclusion, targeted sampling stands as a unique and valuable tool in the research toolbox. By focusing on specific subsets of a population, it enables researchers to gain detailed insights into the experiences and characteristics of these groups. This method is particularly beneficial when studying hard-to-reach populations or specialized subjects, where more generalized sampling methods may fall short.

The various types of targeted sampling, including convenience, probability, cluster, systematic, stratified, non-selection, patches, subpopulation, boundary, and convenience sampling, each offer unique benefits, and cater to different research needs and contexts. The examples highlighted in this article demonstrate the versatility of these techniques across a range of research disciplines.

However, it’s crucial to remember the limitations of targeted sampling. While it provides an in-depth focus, the findings may not be generalizable to the broader population due to the non-random nature of the sampling. It’s also susceptible to researcher bias in the selection process, and ethical considerations must be taken into account, especially when dealing with sensitive or marginalized groups.

Ultimately, the use of targeted sampling should be guided by the research question, the nature of the population, and the resources available. With careful planning and execution, targeted sampling can provide rich, nuanced data that adds depth and specificity to our understanding of the world around us.

Learn about Survey Audience Targeting

FAQ on Targeted Sampling

What are the advantages of targeted sampling?

The advantages of targeted sampling include the ability to focus on specific segments of the population, which can provide more detailed and nuanced insights for the research question at hand. Targeted sampling is also often more efficient in terms of resources, as the sample size can be smaller and still provide valuable insights. Additionally, targeted sampling can be an effective way to reach and study groups that may be difficult to access with other sampling methods.

When should targeted sampling be used?

Targeted sampling should be used when the research question is about a specific, identifiable subset of a population. It's especially useful when these groups are hard-to-reach or when their characteristics are of particular interest for the study. For example, researchers might use targeted sampling when studying rare medical conditions, specific professional groups, or behaviors of a particular demographic segment.

How does targeted sampling differ from random sampling?

In targeted sampling, the researcher intentionally selects certain individuals or groups based on specific characteristics, whereas in random sampling, every individual in the population has an equal chance of being selected. This makes targeted sampling more useful for focused studies on specific subsets of the population, while random sampling is better for studies aiming for broad generalizations about the entire population.

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