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Non-probability Sampling – Types, Methods and Examples

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Non-probability Sampling

Definition:

Non-probability sampling is a type of sampling method in which the probability of an individual or a group being selected from the population is not known. In other words, non-probability sampling is a method of sampling where the selection of participants is based on non-random criteria, such as convenience, availability, judgment, or quota.

Non-probability Sampling Methods

Non-probability Sampling Methods are as follows:

Convenience Sampling

This method involves selecting individuals or items that are easily accessible or convenient to the researcher. For example, a researcher conducting a study on college students may select participants from their own class or dormitory because they are convenient to access.

Snowball Sampling

This method involves selecting individuals who know other individuals who meet the criteria for the study. The researcher starts with a few participants and then asks them to refer others who may be interested in participating. This method is often used in studies where the population is difficult to access or identify.

Quota Sampling

This method involves selecting a sample that matches the characteristics of the population. The researcher sets quotas for each characteristic (such as age, gender, or occupation) and selects participants who fit into those quotas. This method is often used in market research studies.

Purposive Sampling

This method involves selecting individuals or items that meet specific criteria or have specific characteristics that the researcher is interested in studying. For example, a researcher studying the experiences of cancer survivors may purposively select individuals who have undergone chemotherapy.

Volunteer Sampling

This method involves selecting individuals who volunteer to participate in the study. This method is often used in studies where the population is difficult to access or identify.

How to Conduct Non-probability Sampling

To conduct a non-probability sampling, you should follow these general steps:

• Identify the target population: Identify the population you want to study. This can be a specific group of people, a geographic location, or any other defined population.
• Determine the sampling method: Choose the non-probability sampling method that is most appropriate for your study. Consider the advantages and disadvantages of each method and select the one that fits your research question and resources.
• Determine the sample size: Determine the appropriate sample size based on your research question, the available resources, and the sampling method you choose.
• Recruit participants : Recruit participants using the selected non-probability sampling method. For example, if you are using convenience sampling, you might approach people in a public place to participate in your study.
• Collect data: Collect data from the selected participants using the appropriate research methods, such as surveys, interviews, or observations.
• Analyze the data: Analyze the data collected from the sample to draw conclusions and make generalizations about the population.

Examples of Non-probability Sampling

• Convenience Sampling: In this type of sampling, participants are chosen because they are easy to reach or are readily available. For example, a researcher may choose to survey the first 100 people who enter a shopping mall.
• Quota Sampling : Quota sampling is a type of non-probability sampling in which participants are selected to ensure that the sample reflects the characteristics of the population in terms of certain traits. For example, if a researcher wants to conduct a study on the opinions of men and women about a certain product, they may select a sample that has an equal number of men and women.
• Purposive Sampling: In this type of sampling, participants are selected based on specific criteria such as age, gender, occupation, or experience. For example, a researcher may choose to interview only CEOs of Fortune 500 companies to study their leadership style.
• Snowball Sampling: Snowball sampling is a type of sampling in which the initial participants in a study are asked to refer others who they know that fit the criteria of the study. For example, a researcher may ask a person who has experienced homelessness to refer others they know who have experienced homelessness for a study on homelessness.
• Judgmental Sampling : In judgmental sampling, the researcher selects participants based on their own judgment about who would be the most appropriate for the study. For example, a researcher may select participants for a study on the effects of a new cancer drug based on their experience with the disease and their likelihood of benefiting from the treatment.

Applications of Non-probability Sampling

Here are some applications of non-probability sampling:

• Exploratory Studies: Non-probability sampling is commonly used in exploratory studies where the focus is on generating new ideas and insights rather than testing hypotheses. Exploratory studies often use a small sample size, and non-probability sampling is used to identify potential patterns or trends.
• Pilot Studies: Non-probability sampling is also used in pilot studies, which are small-scale studies conducted to evaluate the feasibility and potential outcomes of a larger study. Pilot studies often use a convenience sample or purposive sampling to identify potential issues or areas of improvement before conducting the larger study.
• Qualitative Research : Non-probability sampling is commonly used in qualitative research where the focus is on gaining an in-depth understanding of a particular phenomenon or context. Qualitative research often uses purposive sampling to identify participants who have the knowledge or experience needed to provide rich and detailed insights.
• Rare Populations : Non-probability sampling is used in studies of rare populations, where it may be difficult to obtain a large enough sample using a random sampling method. Snowball sampling is often used in studies of rare populations to identify potential participants through referrals from existing participants.
• Convenience Sampling : Non-probability sampling is also used in studies where the sample size is not a critical factor, and the focus is on convenience and efficiency. Convenience sampling is often used in market research, opinion polls, and customer satisfaction surveys.
• Ethnographic Research: Non-probability sampling is commonly used in ethnographic research, which involves studying the social and cultural practices of a particular group or community. Ethnographic research often uses purposive sampling to identify participants who can provide insights into the cultural practices and beliefs of the group being studied.
• Case Studies: Non-probability sampling is often used in case studies, which involve in-depth analysis of a single individual, organization, or event. Case studies often use purposive sampling to select the individual or organization that is most relevant to the study.
• Action Research: Non-probability sampling is also used in action research, which involves developing solutions to practical problems in real-world settings. Action research often uses purposive sampling to identify participants who can provide input and feedback on the proposed solutions.
• Behavioral Research: Non-probability sampling is used in behavioral research where the focus is on understanding human behavior, attitudes, and beliefs. Behavioral research often uses purposive sampling to identify participants who can provide insights into the behavior being studied.
• Historical Research: Non-probability sampling is used in historical research, which involves studying events and phenomena that occurred in the past. Historical research often uses purposive sampling to identify participants who have knowledge or experience relevant to the historical event or phenomenon being studied.

Purpose of Non-probability Sampling

The main purpose of non-probability sampling is to obtain a sample that is more convenient and practical than a random sample, particularly in situations where a random sample is not feasible, practical, or affordable. Non-probability sampling methods are often used in exploratory research, qualitative research, or in situations where researchers want to study a specific group or population.

When to use Non-probability Sampling

Here are some situations where non-probability sampling may be appropriate:

• Small or hard-to-reach populations: When the population of interest is small or difficult to access, non-probability sampling may be the only feasible option.
• Exploratory research: Non-probability sampling may be used in exploratory research studies where the objective is to generate hypotheses or insights for further investigation.
• Convenience sampling : This type of non-probability sampling is commonly used when the researcher selects the most convenient participants available, such as those who are nearby or easily accessible.
• Expert or judgmental sampling: When the researcher is interested in studying a specific group of individuals with specialized knowledge or expertise, expert or judgmental sampling may be used.
• Quota sampling: In quota sampling, the researcher identifies relevant characteristics of the population of interest and selects participants based on those characteristics in order to ensure a representative sample.

Characteristics of Non-probability Sampling

Here are some characteristics of non-probability sampling:

• Non-random selection : In non-probability sampling, the selection of participants is non-random and based on subjective criteria, such as convenience, availability, or judgment.
• Limited generalizability: Since non-probability sampling does not provide a representative sample of the population, the findings obtained from the sample may not be generalizable to the population as a whole.
• Biased sample: Non-probability sampling can result in a biased sample, which means that the sample is not representative of the population, leading to inaccurate or misleading conclusions.
• No sampling frame : Non-probability sampling does not require a sampling frame, which is a list of all the individuals or units in the population, making it easier and cheaper to conduct the sampling process.
• Subjective judgment: Non-probability sampling requires subjective judgment in selecting participants, which can introduce researcher bias and reduce the objectivity of the research findings.
• Less precision : Non-probability sampling generally provides less precision and accuracy compared to probability sampling methods, which may lead to lower statistical power and weaker inferential conclusions.

Advantages of Non-probability Sampling

Advantages of Non-probability Sampling are as follows:

• Easy to conduct: Non-probability sampling is relatively easy to conduct as it does not require a sampling frame or complex statistical calculations.
• Cost-effective: Non-probability sampling is usually less expensive than probability sampling methods as it does not require a large sample size or specialized equipment.
• Convenient: Non-probability sampling can be convenient as it allows researchers to select participants based on their availability or willingness to participate.
• More suitable for exploratory research : Non-probability sampling is more suitable for exploratory research where the focus is on generating new insights or hypotheses rather than making statistical inferences.
• Better for studying rare phenomena: Non-probability sampling can be more effective for studying rare or hard-to-reach populations, such as drug users or people with specific medical conditions, where a probability sample may be difficult to obtain.
• Allows for more diverse samples: Non-probability sampling can allow for a more diverse sample as it does not require strict randomization, allowing for the inclusion of participants who may not have been included in a probability sample.

Disadvantages of Non-probability Sampling

Disadvantages of Non-probability Sampling are as follows:

• Limited generalizability : Non-probability sampling does not provide a representative sample of the population, so the findings obtained from the sample may not be generalizable to the population as a whole.
• Difficulty in estimating sampling error : Non-probability sampling does not allow for the calculation of sampling error, which is the degree to which the sample estimates differ from the true population values.
• Difficult to replicate: Non-probability sampling can be difficult to replicate as the selection of participants is based on subjective criteria, making it challenging to obtain similar results in subsequent studies.
• Limited statistical power: Non-probability sampling generally provides less precision and accuracy compared to probability sampling methods, which may lead to lower statistical power and weaker inferential conclusions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Non-probability sampling: what it is and how to do it right

Last updated

14 May 2023

Reviewed by

Miroslav Damyanov

The process of choosing individuals to participate in a survey or an experiment is known as sampling. Getting the right sample requires careful thought and planning, as there are lots of ways to design, distribute, and collect data from surveys and experiments in ways that make extrapolating useful insights difficult, if not impossible.

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• What is non-probability sampling?

Sampling can be categorized as either probability or non-probability sampling. 

In probability sampling, you randomly select participants from your population, with every participant having an equal chance of being selected. In non-probability sampling, you choose non-random criteria upon which to base your sampling choices from a larger population—not everybody gets a chance at being selected. 

There are four main subtypes of non-probability sampling (and variations of these subtypes) that researchers can commonly use in business and academic settings.

Convenience sampling

Convenience sampling involves engaging participants that are most convenient for you to access. For example, suppose you're looking to survey people's political opinions about a topic and decide to go door-to-door in your neighborhood to ask questions. In that case, you'd be creating a convenience sample. 

One type of convenience subtype is consecutive sampling, in which a researcher first gathers their convenience sample and engages in research. When they complete it, they continue to recruit and engage other respondents who fit the study's screening criteria, forming secondary and tertiary convenience samples and studying them consecutively.

Purposive sampling

Also known as judgmental sampling, purposive sampling is a method by which a sample is selected based purely on the researcher’s knowledge and credibility. Several types of purposive sampling exist, including:

Critical case sampling

In critical case sampling, you're making a judgment call about which small group of participants or cases are most important to the study of your subject.

Deviant case sampling

Here, you're looking for the most extreme case representing a particular subject you're trying to study.

Expert sampling

When you engage in expert sampling, you gather a sample of those individuals with the greatest expertise relevant to your subject.

Homogenous sampling

If you're investigating an attribute that a group has in common, you may wish to assemble a group that strongly resembles each other in one or more aspects.

Maximum variation sampling

To look at a subject from all possible perspectives, you build a sample that’s as diverse as possible.

Typical case sampling

In a typical case sample, you're looking for participants who exemplify what the average subject would look like when it comes to a particular subject or phenomenon.

Quota sampling

Quota sampling involves selecting a sample representative of the population from which you're trying to collect feedback. For example, suppose you're surveying an audience of sports fans, a third of whom like teams A, B, and C, respectively. No matter how many people you choose to sample, you'd draw participants equally from the three different groups of sports fans. 

However, it's important to note that in quota sampling, you're not randomly drawing participants from different subgroups. You're using some non-random attribute(s), such as proximity to you, to determine who participates.

Snowball sampling

Snowball sampling involves members of hard-to-reach populations. In such a sample, you start by engaging one member of this population willing to engage in your survey or experiment and ask them to introduce you to others in their group. Typically, researchers who've studied indigenous populations with little outside contact with the developed world must use this sampling method.

• What are the benefits and drawbacks of probability sampling and non-probability sampling?

Before determining which non-probability sampling method to use, it's important to understand what the difference between probability and non-probability sampling means for your research. As stated above, in probability sampling, you're randomly drawing participants from a population. When you do so, you're eliminating many forms of bias that may be found in the results.

Probability sampling doesn’t remove all forms of bias from a research project . For example, you could inadvertently exclude members from your research if the list of individuals you sample (your sampling frame) differs from the population. But there are far fewer potential biases when you use a probability sample than when you use a non-probability one. 

However, probability sampling takes more effort and time than most non-probability samples. For example, say you're surveying a population and your sample includes 1,000 individuals, but only ten percent respond to your initial inquiry. To complete the research, you would need to spend time and money tracking down and encouraging the remaining participants to respond.

When you use a non-probability sample, you may find it easier to recruit willing participants. If you offer $5 coupons in a high-traffic area to participate in a survey, your results may not greatly reflect local area attitudes. But chances are you'll have a high participation rate. 

With non-probability sampling, there are many forms of bias you may introduce to your study, including:

Healthy user bias

In studies regarding health and health interventions, healthy users are more likely to opt-in to these studies. This overrepresentation will skew results if the sampling frame isn’t weighted appropriately.

Non-response bias

If many respondents fail to participate and you form your conclusions based on those who do, the absence of those participants may skew the results.

Pre-screening bias

You may also introduce bias into a study based on how you pre-screen participants. If you advertise a study about weight loss, you may attract more people who are motivated to lose weight than the general population.

Self-selection bias

Respondents opting into particular studies may share characteristics that skew the data. For example, marijuana enthusiasts may volunteer to take a survey about attitudes toward marijuana at higher rates than members of the population at large, which may skew the results.

Undercoverage bias

Some population members are less likely to participate due to logistical issues. You might have difficulty recruiting participants in rural areas with inconsistent Internet access, resulting in under coverage of certain population segments.

Despite the risks of introducing biases in your research, there are many instances when using non-probability sampling rather than probability samples makes sense.

• When should you use non-probability sampling?

The best way to determine which sampling method to use is to examine your study and determine your desired outcomes. For example, if you're looking to study participants who typically don't respond to studies, you may have to resort to snowball sampling by necessity. Or, say you need to obtain feedback from a population, but only those with a specific attribute provide detailed feedback. You may want to oversample from that group to get the practical insights you need.

If you choose to use non-probability samples, you'll want to minimize the biases you introduce to the study to the greatest extent possible. Make sure that your screening process, research description, and questions don't create biases and skew results. 

Oversample certain subgroups to avoid under-coverage. And make sure you spend appropriate time and resources recruiting participants to ensure that you attract the number of participants and level of engagement you need for your study.

• What are some examples of non-probability sampling?

Many common examples of non-probability sampling can be found in our day-to-day lives. Whenever you receive a customer feedback survey on a receipt, a company uses non-probability sampling. Political organizations that go door-to-door soliciting opinions are engaging in non-probability sampling. An employee survey that excludes managers from participation is another example. 

Many businesses also use non-probability sampling when beta testing , conducting focus groups , or sending surveys to their entire customer base.

What is the difference between probability and non-probability sampling?

In probability sampling, every member of a population has an equal and non-zero chance of being selected for a study. In a non-probability sample, certain population members have a zero chance of being selected.

Is stratified sampling an example of non-probability sampling?

Stratified sampling is an example of probability sampling. In a stratified sample, a population is subdivided into different non-overlapping subpopulations known as strata. When sampling, a researcher randomly selects each element (aka member) of strata. If the populations overlap or elements aren’t chosen randomly, the researcher uses non-probability sampling.

What is random sampling versus non-random sampling?

In random sampling, each population element has an equal chance of being selected in the final. By contrast, certain elements are more likely to be selected than others in a non-random sample.

Is simple sampling non-probability?

Simple sampling (also known as simple random sampling) is an example of probability sampling, not non-probability sampling. In simple random sampling, a researcher chooses random elements from the sampling frame.

Does probability sampling or non-probability sampling lead to statistically significant results?

Statistical significance doesn’t depend on the type of sampling selected. Rather it depends on the effect size and the sample size. The effect size is the size of the difference in outcomes between two samples. The sample size or the number of participants in a study determines the amount of collected information, which affects the precision or level of confidence in the sample estimates. 

The bigger the sample size, the more likely it is to find a statistically significant difference between the study groups. However, researchers should always perform a sample size calculation in advance to avoid wasting resources in over-recruiting, which may also unnecessarily inflate the study results.

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Non-Probability Sampling: Types, Examples, & Advantages

When we are going to do an investigation, and we need to collect data, we have to know the type of techniques we are going to use to be prepared. For this reason, there are two types of sampling : the random or probabilistic sample and the non-probabilistic one. In this case, we will talk in-depth about non-probability sampling. Keep reading!

What is non-probability sampling?

Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.

Non-probability sampling is a method in which not all population members have an equal chance of participating in the study, unlike probability sampling . Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.

LEARN ABOUT: Survey Sampling

Types of non-probability sampling

Here are the types of non-probability sampling methods:

Convenience sampling

Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population. Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample.

Consecutive sampling

This non-probability sampling method is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive sampling technique gives the researcher a chance to work with many topics and fine-tune his/her research by collecting results that have vital insights.

Quota sampling

Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample , not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.

Judgmental or Purposive sampling

In the judgmental sampling method, researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity.

Snowball sampling

Snowball sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample.

LEARN MORE: Simple Random Sampling

Non-probability sampling examples

Here are three simple examples of non-probability sampling to understand the subject better.

• An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample.
• In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling.
• Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study.

When to use non-probability sampling?

• Use this type of sampling to indicate if a particular trait or characteristic exists in a population.
• Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.
• Researchers use it when they have limited time to conduct research or have budget constraints.
• When the researcher needs to observe whether a particular issue needs in-depth analysis , he applies this method.
• Use it when you do not intend to generate results that will generalize the entire population.

LEARN MORE: Population vs Sample

Advantages of non-probability sampling

Here are the advantages of using the non-probability technique

• Non-probability sampling techniques are a more conducive and practical method for researchers deploying surveys in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers, however, if done correctly, it can produce similar if not the same quality of results and avoid sampling errors .
• Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

Select your respondents

Difference between non-probability sampling and probability sampling:

LEARN ABOUT:   Sampling Frame

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Now you know non-probability sampling is a great tool to extract information from a specific population. If you are a student or belong to a branch in which academic activities are developed, QuestionPro Audience is for you.

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• Non-probability Sampling Methods

What Is Non-probability Sampling? Types, Examples, and Best Practices

What Is Non-probability Sampling?

Types of non-probability sampling with examples, 1. convenience sampling.

• Consecutive Sampling (also known as total enumerative sampling): Selecting all available subjects meeting criteria until the desired sample size is reached.
• Self-Selection Sampling (also known as volunteer sampling):  Participants voluntarily opt-in, as with online surveys.

2. Quota Sampling

• Proportional quota sampling : Uses proportional numbers to represent segments in the wider population.
• Non-proportional quota sampling: Determines only the minimum sample size per stratum, still providing deep insights into each segment.

3. Snowball Sampling

4. Purposive Non-probability Sampling

• Heterogeneity Sampling:  Selects participants with diverse characteristics to capture a comprehensive understanding of the population's heterogeneity.
• Homogeneous Sampling:  Focuses on selecting participants with similar traits or experiences to facilitate in-depth analysis of a specific subgroup.
• Deviant Sampling:  Targets individuals who deviate from the norm or exhibit unique characteristics, allowing researchers to explore outliers or uncommon phenomena.
• Expert Sampling:  Involves selecting participants based on their expertise or specialized knowledge in a particular domain, ensuring the sample comprises individuals with valuable insights.

When Would It Be Preferable To Use A Non-probability Sample?

• Exploratory Research:  When the focus is on understanding phenomena or exploring new areas without the need for generalizability.
• Limited Resources:  When time, budget, or access to the population is constrained, non-probability sampling offers a cost-effective alternative.
• Hard-to-Reach Populations:  For studying populations that are difficult to locate or access, such as undocumented immigrants or individuals with rare conditions.
• Pilot Studies:  To test research instruments, procedures, or hypotheses before conducting larger-scale studies.
• Qualitative Research:  Non-probability sampling is often preferred in qualitative research, where the emphasis is on understanding individual perspectives and experiences rather than generalizability.

Why Do Researcher Gravitate Towards This Method?

• Swift and Convenient:  One of the primary draws is the speed of data collection. Non-probability samples can be formed swiftly, enabling surveys to be launched, executed, and completed in shorter timeframes.
• Cost-effectiveness:  These methods minimize expenses related to participant recruitment, data collection, and analysis. Geographically concentrated samples further reduce travel costs.
• Participant Accessibility:  Non-probability sampling enables researchers to reach populations that may be difficult to access through traditional probability sampling methods , especially marginalized or hard-to-reach groups.
• Reduced Respondent Burden:  Techniques like volunteer sampling, where participants opt-in for surveys, reduce the need for follow-up efforts and persuasion of non-respondents, leading to more complete and accurate data.

What Is The Issue With Non-probability Sampling?

• Selection Bias:  This approach relies on assumptions about the similarity between the sample and the population, which can lead to self-selection bias and inaccurate generalizations.
• Non-coverage Bias:  Some population segments may be systematically excluded from non-probability samples, resulting in non-coverage bias. For example, individuals without internet access may be left out of web panel samples.
• Difficulty in Quality Assessment:  It is challenging to evaluate the quality of a non-probability sample because the probability of selection for each unit is unknown, making it difficult to estimate sampling error and reliability accurately.

Best Practices For Non-probability Sampling

• Know Your Audience:  Understanding the target population is crucial. This insight guides sample selection to ensure it accurately represents the group under study.
• Combine Methods:  Enhance sampling effectiveness by integrating various methods. For example, combine stratified and snowball sampling for diverse and comprehensive samples.
• Use Data Analysis Techniques:  Employ rigorous techniques like weighting or propensity score matching to correct biases in the sample, enhancing the validity of findings.
• Be Transparent in Reporting:  Acknowledge the limitations of non-probability sampling in research reports. Transparent reporting fosters trust and credibility in the findings.
• Verify Your Findings:  Validate results by comparing them with existing data or studies. This step enhances the reliability of conclusions drawn from the sample.

For a deeper dive into survey sampling methods, visit https://tgmresearch.com/survey-sampling-methods.html to enhance your understanding of this essential aspect of market research.

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• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Non-Probability Sampling: When and How To Use It Effectively

In this blog, we’ll dive into what non-probability sampling is, how it differs from other approaches like probability sampling, when to use it, and the benefits/drawbacks to consider.

Gathering consumer data is essential to understand trends, behaviors, and preferences. However, that consumer data needs to accurately reflect the audience/population you’re going after if you’re looking to make business decisions that would be in your customers’ best interest; sampling methods play a crucial role in this.  Sampling methods are the ways insights teams can select/access a subset of individuals from a larger population to represent their current or ideal audience. One sampling approach is non-probability sampling - a technique that offers flexibility and specific advantages.

Table of Contents: 

• An introduction to non-probability sampling
• When to use non-probability sampling
• Exploring different types of non-probability sampling
• How technological innovation is shaping non-probability sampling

Real-world examples

• Advantages and disadvantages of non-probability sampling

An introduction to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">Non-probability sampling , as the name implies, does not leave sample selection up to chance (or, to natural probability). Instead, it is a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279984">non- dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279981">random dropdown#toggle" data-dropdown-menu-id-param="menu_term_305279981" data-dropdown-placement-param="top" data-term-id="305279981"> selection dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280009">methodology in which a researcher selects participants based on certain criteria; this criteria might be the research team’s best judgement of who should participate in their research study or simply who’s available at the time. As such, not every individual in the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280002">target population has an dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279986">equal chance of being chosen when using a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling approach.

Non-probability vs. probability sampling methods

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">Non-probability sampling : Relies on more intentional selection criteria - such as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279973">quota sampling, in which a research team plans ahead for a certain percentage of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents to come from a specific demographic or group of people.

Probability sampling: Rooted in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279981">random selection , ensuring that each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280003">member of the population has a known (and often equal) chance of being part of the sample. Because it is dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280006">probability-based , this type of sampling is ideal for researchers seeking statistical representativeness and generalizable conclusions. Examples of probability sampling techniques include dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279990">simple dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279977">random sampling , dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280024">cluster sampling , and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280000">stratified sampling .

Back to Table of Contents

When to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279999">use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

Knowing there are both probability and non-probability approaches, you might now be wondering how to choose between different sampling techniques and when to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279999">use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling in particular . Below are just a few common instances where dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling often proves to be valuable:

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280007">Exploratory research : dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">Non-probability sampling is good for early-stage, exploratory dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280001">research studies to gain preliminary insights or to further refine research questions.

Limited resources: Researchers may want to choose dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling when time or budget constraints make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279977">random sampling unrealistic or inefficient.

Specific or hard-to-reach populations: dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">Non-probability sampling is good for accessing dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280030">specific groups rather than the general dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279988">entire population (such as a car brand looking for feedback from Gen X women who drive an SUV).

Qualitative studies: Lastly, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling is best when individual perspectives and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280017">in-depth understanding through qualitative feedback are more of a priority over statistical generalization.

Exploring different dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279982">types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

Now knowing when you might dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279999">use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279999"> dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279979">non-probability sampling dropdown#toggle" data-dropdown-menu-id-param="menu_term_305279979" data-dropdown-placement-param="top" data-term-id="305279979"> methods , let’s now explore some of the different approaches you can take when leveraging this sampling method to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279995">collect data .

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279974">Convenience sampling : Subjects are chosen based on accessibility and availability - aka, who’s conveniently available when it comes time for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279993">data collection .

Purposive or dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279985">judgmental sampling : The researcher handpicks individuals based on their knowledge and expertise on the research topic; researchers need to be careful about potential dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280004">sampling bias when going this route.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279973">Quota sampling : A researcher sets quotas to ensure certain dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279997">subgroups are represented proportionately in the sample (i.e. setting regional, age, and gender quotas so that a certain percentage must come from the North, South, East, or West; from each generational group; and from each gender profile).

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279975">Snowball sampling : Researchers start by picking a few initial dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents that meet their study’s criteria. They then rely on those dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents to provide dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280016">referrals for others to take the same survey. This is useful for hidden populations, where existing participants best know who else to recommend for the study (perhaps members of a niche hobby or community).

How technological innovation is shaping dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

Finding a qualified dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279978">sample size for your research objective can be tricky, though innovations in technology are making the process somewhat simpler.

Reach and convenience The internet and digital/ online survey platforms allow researchers to quickly access a wide pool of potential survey participants through spaces like online forums and targeted advertisements. This opens up possibilities for reaching specific dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279997">subgroups and expanding the scope of studies.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280021">Social media as a sampling pool: dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280021">Social media is a gold mine for researchers seeking out real consumer voices; these platforms offer near-instant access to seemingly endless volumes of opinions, comments, and user data. Researchers can tap into these pools for sentiment analysis, new and emerging trends, and even direct recruitment of participants for studies focused on specific online communities.

Automation and online accessibility: Online market research tools and platforms make it much easier to streamline the sampling process. Researchers can use pre-defined quotas for quick setup or automated libraries of questions in their dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling approach.

While technology facilitates wider audience reach, researchers need to be careful with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279996">self- dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279998">selection dropdown#toggle" data-dropdown-menu-id-param="menu_term_305279998" data-dropdown-placement-param="top" data-term-id="305279998"> bias which is where only certain types of individuals choose to participate online, skewing results (those with more free time or those particularly passionate about a certain topic). This is less likely to occur with probability sampling, since everyone has an dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279986">equal chance of participating.

Below are a few examples of what dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling might look like in a real-world context.

Clothing brand example

Let’s consider that a small, local clothing brand is developing a new activewear line and wants to hear from their likely brand buyers on when, where, and how often they might wear items from this new line. They decide to reach out to nearby gyms and fitness studios to recruit participants who would be willing to provide their valuable feedback (as individuals that prioritize fitness) via an online survey sent over email. In exchange, the brand offers these dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents 15% off one of their brand items. The brand gathers feedback from their judgmental/ dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279976">purposive sampling technique around fitness apparel preferences - from fabric types, sleeve length, color options, and more. Because the goal here is to inspire new product development , it’s more about the quality of their feedback rather than statistical analysis, so the non-probability sampling method is a good choice.

Political opinions example

In light of an upcoming election, a political researcher wants to interview individuals in their local county. They want to be sure to get a mix of responses from different demographic, socioeconomic, and political backgrounds. To do so, the researcher leverages online dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279973">quota sampling . With this approach, a survey is sent out to a group of willing participants, however they must fall into certain quota groups (Democrat, Republican, make a certain amount per year, etc.). These quotas are based on the general population’s census, providing dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279991">representative sampling out of a smaller dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280008">subset of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents (as surveying the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279988">entire population is not feasible). Once a quota group ‘fills up’ (aka, there are enough dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents to represent that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280029">group of people based on the total dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279978">sample size for the survey), no other dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents from that quota group will qualify to complete the survey - they will be ‘screened out’ and their data will not be collected. As a result, the political researcher is able to gather dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280017">in-depth insights into their county’s voting perspectives, representative of the wider nation’s population.

Advantages and disadvantages of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

By now you’re an expert on what dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling is, when to use it, and how it works in real-world examples. However, there are some specific pros and cons to consider when choosing this sampling approach:

Advantages of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

Speed and efficiency dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279984">Non- dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279981">random dropdown#toggle" data-dropdown-menu-id-param="menu_term_305279981" data-dropdown-placement-param="top" data-term-id="305279981"> selection of a sample group removes the need to build a detailed dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279983">sampling frame , thus reducing the time and effort needed to capture responses compared to probability sampling methods.

Cost-effective Because researchers can choose dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents using their own judgement or by who is conveniently available, it can be much more cost effective for those with budget constraints.

Ideal for niche populations dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">Non-probability sampling is the best approach for harder to reach groups where no complete list of individuals is accessible or where it’s impossible to capture the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279988">entire dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280014">population dropdown#toggle" data-dropdown-menu-id-param="menu_term_305280014" data-dropdown-placement-param="top" data-term-id="305280014"> of interest . It allows voices of these niche markets to be heard, that are often overlooked in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279977">random sampling .

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280015">Disadvantages of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling

Increased bias potential The most obvious downside to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling is the increased risk for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280004">sampling bias . Because you aren’t randomly recruiting participants based on a formula/non-biased approach, researchers' own biases are more likely to influence the sampling outcome - potentially over or underrepresenting certain viewpoints.

Less representativeness of the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280031">whole population Since not everyone has an dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279986">equal chance of being selected (as they do with probability sampling), the sampling group will not mirror the actual dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280002">target population - making it harder to predict real-world behaviors. This is why this sampling approach is usually better suited for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280007">exploratory research .

Limits generalization of findings Similar to the above, researchers using a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling approach are limited in what findings they can justifiably make. They can’t draw ‘universal’ or ‘general’ conclusions about their findings.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279994">Non-probability sampling dropdown#toggle" data-dropdown-menu-id-param="menu_term_305279994" data-dropdown-placement-param="top" data-term-id="305279994"> techniques are good for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279989">market researchers to keep in mind in certain surveying scenarios. It’s best used in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280007">exploratory research or when budgets are tighter, as it doesn’t typically allow for statistical comparison or data generalizations. One caveat is that using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279972">non-probability sampling with quotas in place does allow for statistical analysis, so long as the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305280027">questionnaire quotas used are representative of the broader population (e.g. the US census).

To learn more about sampling approaches with quantilope (a panel agnostic platform allowing you to reach any online dropdown#toggle" data-dropdown-placement-param="top" data-term-id="305279980">respondents you wish), get in touch below!

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Non-Probability Sampling

In non-probability sampling (also known as non-random sampling) not all members of the population have a chance to participate in the study. In other words, this method is based on non-random selection criteria. This is contrary to probability sampling , where each member of the population has a known, non-zero chance of being selected to participate in the study.

Necessity for non-probability sampling can be explained in a way that for some studies it is not feasible to draw a random probability-based sample of the population due to time and/or cost considerations. In these cases, sample group members have to be selected on the basis of accessibility or personal judgment of the researcher. Therefore, the majority of non-probability sampling techniques include an element of subjective judgement. Non-probability sampling is the most helpful for exploratory stages of studies such as a pilot survey.

The issue of sample size in non-probability sampling is rather ambiguous and needs to reflect a wide range of research-specific factors in each case. Nevertheless, there are some considerations about the minimum sample sizes in non-probability sampling as illustrated in the table below:

Sizes of non-probability sampling [1]

The following is the list of the most popular non-probability sampling methods and their brief descriptions:

 Non-probability sampling methods

Advantages of Non-Probability Sampling

• Possibility to reflect the descriptive comments about the sample
• Cost-effectiveness and time-effectiveness compared to probability sampling
• Effective when it is unfeasible or impractical to conduct probability sampling

Disadvantages of Non-Probability Sampling

• Unknown proportion of the entire population is not included in the sample group i.e. lack of representation of the entire population
• Lower level of generalization of research findings compared to probability sampling
• Difficulties in estimating sampling variability and identifying possible bias

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.

John Dudovskiy

[1] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th edition, Pearson Education Limited

• Non-Probability Sampling: Definition, Types, Examples, Pros & Cons

There are two types of sampling techniques; probability sampling, and non-probability sampling. While you can calculate the probability of a member of the population being selected in probability sampling, it is impossible in non-probability sampling.

For instance, a researcher may be able to calculate that a member has a 10% chance of being selected to participate in the study, while another has 35%.

But in non-probability sampling, each member has an equal chance of being selected even though the chance of participation is not guaranteed.

In this article, we are going to discuss the concept of non-probability sampling, its advantages and disadvantages, and where it can be used.

What is Non-probability Sampling?

Non-probability sampling is defined as a method of sampling in which samples are selected according to the subjective judgment of the researcher rather than through random sampling. Unlike probability sampling, each member of the target population has an equal chance of being selected as a participant in the research because you cannot calculate the probability of selecting anyone.

Non-probability sampling is commonly used in qualitative or exploratory research and it is conducted by observation.

This is because non-probability sampling is a less difficult technique and the outcome depends largely on the expertise of the researcher. 

This sampling technique is also used by researchers to save cost or time, especially when it is impossible to use random probability sampling.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

What are the types of Non-probability Sampling?

1. convenience sampling.

This is one of the non-probability sampling techniques where the samples that are readily available in the entire population get selected by the researcher. Convenience sampling is used by researchers because the samples are easy to recruit, and not necessarily because the researcher considers selecting a sample that represents the entire population.

In research, it is important to test the sample that will represent the targeted population. But, in some cases where the population is too large, the researcher may not be able to conduct a test for the entire population. This is why researchers focus on convenience sampling. It is also the most common non-probability sampling method because it is cost-efficient and time-saving.

For example, you ask your students to complete a survey after each of your classes with them. However, the response from your students’ survey does not represent the whole school population.

2. Consecutive sampling

Consecutive sampling is similar to convenience sampling in method, although there are a few differences. In this type of non-probability sampling, the researcher selects a person or a group from the population and conducts research with them over a period of time. 

Thereafter, the result from the research is analyzed and then the researcher goes on to another group from the population and conducts another research if necessary. The consecutive sampling technique gives the researcher an opportunity to study diverse topics and gather results with vital insights.

Learn About: Sampling Bias: Definition, Types + [Examples]

3. Quota sampling

To understand quota sampling, let us look at this example. A researcher wants to study the career growth of the employees in an organization with 400 employees. To better understand the population, the researcher will select a sample from the population to represent the total employees or population. 

If the researcher is interested in a particular department within the population the researcher will use quota sampling to divide the population into strata or groups. So quota sampling is the division of the larger population into strata according to the need of the research.

For example, if there are 400 women and 100 men, So you will have to select 40 women and 10 men to represent the strata.

4. Judgmental or Purposive sampling

In a judgmental sampling technique, the samples are selected based on the credibility and knowledge of the researcher. This means that only those deemed fit by the researcher are selected to participate in the research. 

It is worthy of note that purposive or judgmental sampling is not scientific and it can easily accommodate influence or bias from the researcher. For example, if you want to conduct research about the experience of disabled employees in your large organization, you can select people with special needs in a few departments. Although they serve the purpose, they do not represent your entire employees.

5. Snowball sampling

Snowball sampling is useful for finding samples that are difficult for the researcher to locate. Researchers make use of snowball sampling techniques when their sample size is not readily available and also small. 

So this is carried out like a referral program where the researcher finds suitable members and solicits help in finding similar members so as to form a considerably good sample size. 

For example, If you want to research the experience of homeless people, considering there is no data to determine their numbers, you can meet one and ask for an audience. If one person agrees, you can ask to be introduced to other homeless people.

Example of Non-probability Sampling

Let us consider some of the examples of non-probability sampling based on three types of non-probability sampling. (quota sampling, 

Example 1 (Quota Sampling)

We have earlier established that quota sampling is a method of grouping your sample into strata or groups.

Let us assume that a researcher wants to examine the differences in male and female students of a school with a 20,000 population. To derive a true representative of the larger population from the sample (students), the number of students that the researcher will include in the sample would be based on the proportion of male and female students. 

If there are 8000 male students and 12,000 female students. The researcher will select 1200 female students and 800 male students which is proportional to their number. This is the concept of quota sampling.

Example 2 (Convenience Sampling)

A convenience sampling technique is simply one where the people you select for inclusion or as participants in your research sample are those who are most available. Using the example of the 20,000 university students above, let us assume that the researcher is only interested in achieving a sample size of maybe 300 students. 

To achieve this, the researcher can stand at one of the main entrances to the lecture rooms or hall, where students passing by can be easily invited to take part in the research. Once the 300 mark is gotten, the researcher may close the door, administer the survey and leave. 

It is a very convenient way of gathering sampling participants but is not a good representative of the entire population.

Example 3 ( Purposeful or Judgmental sampling)

Purposeful sampling focuses on the judgment of the researcher and the aim of the research in selecting the sample group. If the aim of the research is to launch beauty products that cater to people with vitiligo, the researcher will then select a few people with this condition as the sample group for the research.

The few people might not entirely be the best representative for the population but they will serve the purpose of the research which is the aim of this technique.

When to use Non-probability Sampling?

• Use this type of sampling to indicate if a particular trait or characteristic exists in a population.
• Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.
• Researchers use it when they have limited time to conduct research or have budget constraints.
• When the researcher needs to observe whether a particular issue needs in-depth analysis, he applies this method.
• Use it when you do not intend to generate results that will generalize the entire population.

Read: Research Bias: Definition, Types + Examples

Advantages of Non-probability Sampling

The following are the advantages of non-probability sampling: 

• It is a more practical and conducive method for researchers that deploy surveys into the real world. Also, non-probability sampling can produce or interpret data in the form of numbers if properly done.
• Responses are faster and cheaper because the sample is familiar to the researcher. So it saves time and resources. 
• With non-probability sampling, you can easily connect with your target population especially in an online community.
• Non-probability sampling is also easy to use and you can also use it when you cannot conduct probability sampling perhaps because of a small population. 

What are the Disadvantages of Non-probability Sampling?

• A major disadvantage of non-probability sampling is that the researcher may be unable to evaluate if the population is well represented.
• The researcher may be unable to calculate the intervals and the margin of error . This is why most researchers opt for probability sampling first.    
• You may also have an unclear sample size because there is no way to measure the boundaries of the relevant population to your research.

What is the Difference between Probability and Non-probability Sampling?

Both probability sampling and non-probability sampling are techniques used to sample members of a population and select them to participate in a study. However, both types of sampling techniques have differences in their processing.

The first thing you should know is that while non-probability sampling gives every member of a population an equal chance of being selected but not everyone has an equal chance of participating in a study, probability sampling does not. This is because probability sampling can be calculated while non-probability sampling cannot. Although everyone has a chance of participating, not everyone has a chance of being selected.

Read: What is Participant Bias? How to Detect & Avoid It

In probability sampling, you can predict the chances a member has of being selected through calculation. Also, probability sampling is based on random selection while non-probability sampling is based on the judgment of the researcher which could be subjective.

Probability sampling is used when the researcher wants to eradicate sampling bias while non-probability sampling does not consider the impact of sampling bias. Non-probability sampling is best considered when your population has similar characteristics while the probability sampling technique is best used when the characteristics of the population are diverse.

Lastly, it is easier to find members to participate in a non-probability sampling because they have similar traits. However, it is not so easy to find suitable participants in a probability sampling because of the need to be diverse.

If you want to conduct research that gives everyone a fair opportunity of participation, then you should consider non-probability sampling. Also, if you are working with a stringent budget, and need to work with a lesser time frame, you should also consider using the non-probability sampling technique.

Furthermore, it is important that you use the right sampling technique for the right research. This is why you should be familiar with the requirements for your study before conducting a survey.

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Non-Probability Sampling: Definition, Types

What is non-probability sampling.

The probabilities do not have to be equal for a method to be considered probability sampling . For example, one person could have a 10% chance of being selected and another person could have a 50% chance of being selected. It’s non-probability sampling when you can’t calculate the probabilities at all .

Advantages and disadvantages

A major advantage with non-probability sampling is that—compared to probability sampling—it’s very cost- and time-effective. It’s also easy to use and can also be used when it’s impossible to conduct probability sampling (e.g. when you have a very small population to work with).

One major disadvantage of non-probability sampling is that it’s impossible to know how well you are representing the population . Plus, you can’t calculate confidence intervals and margins of error . This is the major reason why, if at all possible, you should consider probability sampling methods first.

Despite the disadvantages, survey data collection costs have risen dramatically in recent years, resulting in many researchers and polling companies abandoning expensive probability-based samples for less expensive non-probability methods (Wisniowski et al., 2020).

Types of Non-Probability Sampling

Many specific advantages and disadvantages exist for different types of non-probability sampling. You’ll find more information about each method below (click on a name to read more about a specific method’s advantages and disadvantages).

• Convenience Sampling: as the name suggests, this involves collecting a sample from somewhere convenient to you: the mall, your local school, your church. Sometimes called accidental sampling, opportunity sampling or grab sampling.
• Haphazard Sampling : where a researcher chooses items haphazardly, trying to simulate randomness. However, the result may not be random at all and is often tainted by selection bias .
• Purposive Sampling : where the researcher chooses a sample based on their knowledge about the population and the study itself. The study participants are chosen based on the study’s purpose. There are several types of purposive sampling. For a full list, advantages and disadvantages of the method, see the article: Purposive Sampling.
• Expert Sampling : in this method, the researcher draws the sample from a list of experts in the field.
• Heterogeneity Sampling / Diversity Sampling : a type of sampling where you deliberately choose members so that all views are represented. However, those views may or may not be represented proportionally.
• Modal Instance Sampling : The most “typical” members are chosen from a set.
• Quota Sampling : where the groups (i.e. men and women) in the sample are proportional to the groups in the population.
• Snowball Sampling : where research participants recruit other members for the study. This method is particularly useful when participants might be hard to find. For example, a study on working prostitutes or current heroin users.

Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Everitt, B. S.; Skrondal, A. (2010), The Cambridge Dictionary of Statistics , Cambridge University Press. Wisniowski, A. et al. Integrating Probability and Nonprobability Samples for Survey Inference. Journal of Survey Statistics and Methodology, Volume 8, Issue 1, February 2020, Pages 120–147, https://doi.org/10.1093/jssam/smz051

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• What is non-probability sampling: Definition, types & examples

An essential component of every research study is sampling. Selecting the best sampling strategy for your particular research issue is crucial since it might improve the validity of your findings . Many sampling techniques fall into two categories as probability sampling and non-probability sampling.  

The most significant difference between the above two sampling methods is whether the sampling is based on randomness . The non-probability sampling is typically used when access to an entire population is limited or unnecessary . This article will explain the definition of non-probability sampling technique, examples of non-probability sampling, and the advantages and disadvantages of non-probability sampling. 

• What is non-probability sampling?

Non-probability sampling is a subset of sample selection. It uses non-random methods to choose a group of subjects for a study. This sampling is used when the population characteristics cannot be individually identified or are unknown. It does not depend on randomness. 

In Layman's terms, non-probability sampling is a method where the researcher chooses samples based on personal assessment instead of randomly. With non-probability sampling, research participants don’t all have an equal chance of being selected.

This method is more dependent on the researcher’s aptitude for choosing components for a sample. The sampling results may be skewed, making it difficult for all components of the population to participate evenly in the sample. The most beneficial uses of this sampling are in exploratory investigations like pilot surveys.

• Probability vs. non-probability sampling

Non-probability sampling does not concentrate on precisely representing all members of a large population , in contrast to probability sampling and its methodologies. The researcher's subjective assessment is the basis for non-probability sample selection.

What’s the difference between non-probability sampling and probability sampling?

As non-probability sampling methods are not arbitrary, not every person in the population has an equal chance of participating in the study . Here are the differences between probability sampling vs. non-probability sampling: 

• Random vs. deliberate selection: Probability sampling uses randomization to choose a sample rather than making a conscious decision. On the other hand, non-probability sampling approaches choose objects or people for the sample depending on the researchers' objectives , knowledge , or experience . 
• Full population knowledge vs. changing population knowledge: Nonprobability sampling doesn’t need to know the identities of every person in the population before selection. On the other hand, probability sampling techniques should know to choose a representative sample size. 
• Depth vs. objectivity : Probability sampling aims to be objective in the sample selection method. Non-probability sampling, on the other hand, aims to go deeper into an area.
• Unequal chance vs. equal chance: Each person in the population has an equal and fair chance of being chosen to be a part of the smaller sample when using probability sampling. In contrast, individuals don’t have an equal chance of being chosen in non-probability sampling. 
• Non-probability sampling methods

The list of non-probability sampling methods

Choosing the most suitable non-probability sampling method for your research is crucial to continue your study correctly.  As previously pointed out, there are several types of non-probability sampling for the data collection process: 

1. Convenience sampling

A popular non-probability sampling method is convenience sampling , in which individuals are selected based on convenience and availability . It is a simple and inexpensive approach to assemble a sample of individuals and conduct a survey to collect data. It is typically employed for rapid user surveys or pilot testing . 

2. Consecutive sampling

Consecutive sampling is the research practice using sample participants who readily satisfy the inclusion criteria . You carry out the study till you come to a particular conclusion. Samples are picked depending on availability . Each result is examined before moving on to the following sample or topic.

3. Quota sampling

Quota sampling is dependent on a predetermined criterion . It chooses the population’s representative sample. The proportion of traits or features in the sample should match that of the population. Until precise ratios of specific data are obtained or adequate data in various categories are gathered, elements are chosen. 

4. Snowball sampling

When the population is highly uncommon and unknown , snowball sampling is employed. Therefore, you will enlist the assistance of the first element you choose for the population. Then, you can ask him to suggest other components that will match the requirements of the sample. As a result, the population grows like a snowball due to this referral strategy.  

5. Purposive sampling

Purposive sampling entails actively deciding what the sample needs to contain and selecting participants in accordance with that decision. In this approach, you may determine what the sample needs to contain to achieve the study’s goals using your knowledge of the population and your comprehension of the research's purpose.

• Non-probability sampling examples

Researchers will employ the non-probability sampling method when resources or time are lacking. This sampling technique can be used to determine whether a particular issue needs in-depth investigations or when you don’t want the results to be generalizable to the entire population. Here are some examples of this sampling method. 

• In practice, the sample chosen at a company for researching the career ambitions of 300 workers should include an equal representation of men and women. Consequently, there have to be 150 men and 150 women. Since this is improbable, the researcher chooses the groups or strata using quota sampling. 
• Researchers also use this sampling to study a specific patient ailment or rare disease . To create a subjective sample for the study, researchers might ask individuals to provide references for others with their condition. Researchers may seek help from patients to refer to other subjects suffering from the same disease. 
• If there is no information on the number of homeless people , you can ask one to interview them about their experiences. If one person accepts, you can request to be introduced to other homeless individuals. This is a snowball sampling method example. 
• Advantages and disadvantages of non-probability sampling

The non-probability sampling method frequently reveals the prevalence of a particular feature or set of traits in a community. This technique is usually used by researchers who want to undertake qualitative research, exploratory, or pilot studies . This widely used sampling method has disadvantages and many advantages.

Advantages of non-probability sampling

• Researchers can more quickly and effectively conduct surveys in the real world by using this sampling approach. Since non-probability sampling needs less time and money than probability sampling, it is frequently more affordable . 
• In many situations, non-probability sampling is more practical since the researcher may choose volunteers who are conveniently accessible .
• Non-probability sampling allows for greater flexibility since it allows researchers to choose participants based on particular qualities or aspects that are not accessible with probability sampling. 
• Researchers can save time using this sampling method because they don’t need to create a sample frame or gather data on the entire population. 

Disadvantages of non-probability sampling

• Non-probability sampling can have considerable sample biases since the sample is not randomly selected, and this can result in the over or representation of particular population segments. 
• Non-probability sampling is less likely to yield outcomes that represent the genuine population fairly. It may reduce the study’s external validity. 
• The results of non-probability sampling are less generalizable to the broader population since they are less representative.
• Key points to take away

In conclusion, you should think about non-probability sampling if you want to conduct research that provides everyone an equal chance to participate . Additionally, you might consider adopting the non-probability sampling approach if your budget is limited and you have a short time. 

Non-probability sampling differs from probability sampling in that probability sampling involves random selection, whereas non-probability sampling does not. In this article, we have shared the definition of non-probability sampling, its advantages and disadvantages, the differences between non-probability and probability sampling, and examples of non-probability sampling. Reading this article will give you detailed information about non-probability sampling.

Sena is a content writer at forms.app. She likes to read and write articles on different topics. Sena also likes to learn about different cultures and travel. She likes to study and learn different languages. Her specialty is linguistics, surveys, survey questions, and sampling methods.

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The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does. Does that mean that nonprobability samples aren’t representative of the population? Not necessarily. But it does mean that nonprobability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. We are able to estimate confidence intervals for the statistic. With nonprobability samples, we may or may not represent the population well, and it will often be hard for us to know how well we’ve done so. In general, researchers prefer probabilistic or random sampling methods over nonprobabilistic ones, and consider them to be more accurate and rigorous. However, in applied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling. Here, we consider a wide range of nonprobabilistic alternatives.

We can divide nonprobability sampling methods into two broad types: accidental or purposive . Most sampling methods are purposive in nature because we usually approach the sampling problem with a specific plan in mind. The most important distinctions among these types of sampling methods are the ones between the different types of purposive sampling approaches.

Accidental, Haphazard or Convenience Sampling

One of the most common methods of sampling goes under the various titles listed here. I would include in this category the traditional “man on the street” (of course, now it’s probably the “person on the street”) interviews conducted frequently by television news programs to get a quick (although nonrepresentative) reading of public opinion. I would also argue that the typical use of college students in much psychological research is primarily a matter of convenience. (You don’t really believe that psychologists use college students because they believe they’re representative of the population at large, do you?). In clinical practice,we might use clients who are available to us as our sample. In many research contexts, we sample simply by asking for volunteers. Clearly, the problem with all of these types of samples is that we have no evidence that they are representative of the populations we’re interested in generalizing to – and in many cases we would clearly suspect that they are not.

Purposive Sampling

In purposive sampling, we sample with a purpose in mind. We usually would have one or more specific predefined groups we are seeking. For instance, have you ever run into people in a mall or on the street who are carrying a clipboard and who are stopping various people and asking if they could interview them? Most likely they are conducting a purposive sample (and most likely they are engaged in market research). They might be looking for Caucasian females between 30-40 years old. They size up the people passing by and anyone who looks to be in that category they stop to ask if they will participate. One of the first things they’re likely to do is verify that the respondent does in fact meet the criteria for being in the sample. Purposive sampling can be very useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern. With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to overweight subgroups in your population that are more readily accessible.

All of the methods that follow can be considered subcategories of purposive sampling methods. We might sample for specific groups or types of people as in modal instance, expert, or quota sampling. We might sample for diversity as in heterogeneity sampling. Or, we might capitalize on informal social networks to identify specific respondents who are hard to locate otherwise, as in snowball sampling. In all of these methods we know what we want – we are sampling with a purpose.

Modal Instance Sampling

In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the “typical” case. In a lot of informal public opinion polls, for instance, they interview a “typical” voter. There are a number of problems with this sampling approach. First, how do we know what the “typical” or “modal” case is? We could say that the modal voter is a person who is of average age, educational level, and income in the population. But, it’s not clear that using the averages of these is the fairest (consider the skewed distribution of income, for instance). And, how do you know that those three variables – age, education, income – are the only or even the most relevant for classifying the typical voter? What if religion or ethnicity is an important discriminator? Clearly, modal instance sampling is only sensible for informal sampling contexts.

Expert Sampling

Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area. Often, we convene such a sample under the auspices of a “panel of experts.” There are actually two reasons you might do expert sampling. First, because it would be the best way to elicit the views of persons who have specific expertise. In this case, expert sampling is essentially just a specific subcase of purposive sampling. But the other reason you might use expert sampling is to provide evidence for the validity of another sampling approach you’ve chosen. For instance, let’s say you do modal instance sampling and are concerned that the criteria you used for defining the modal instance are subject to criticism. You might convene an expert panel consisting of persons with acknowledged experience and insight into that field or topic and ask them to examine your modal definitions and comment on their appropriateness and validity. The advantage of doing this is that you aren’t out on your own trying to defend your decisions – you have some acknowledged experts to back you. The disadvantage is that even the experts can be, and often are, wrong.

Quota Sampling

In quota sampling, you select people nonrandomly according to some fixed quota. There are two types of quota sampling: proportional and non proportional . In proportional quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each. For instance, if you know the population has 40% women and 60% men, and that you want a total sample size of 100, you will continue sampling until you get those percentages and then you will stop. So, if you’ve already got the 40 women for your sample, but not the sixty men, you will continue to sample men but even if legitimate women respondents come along, you will not sample them because you have already “met your quota.” The problem here (as in much purposive sampling) is that you have to decide the specific characteristics on which you will base the quota. Will it be by gender, age, education race, religion, etc.?

Nonproportional quota sampling is a bit less restrictive. In this method, you specify the minimum number of sampled units you want in each category. here, you’re not concerned with having numbers that match the proportions in the population. Instead, you simply want to have enough to assure that you will be able to talk about even small groups in the population. This method is the nonprobabilistic analogue of stratified random sampling in that it is typically used to assure that smaller groups are adequately represented in your sample.

Heterogeneity Sampling

We sample for heterogeneity when we want to include all opinions or views, and we aren’t concerned about representing these views proportionately. Another term for this is sampling for diversity . In many brainstorming or nominal group processes (including concept mapping), we would use some form of heterogeneity sampling because our primary interest is in getting broad spectrum of ideas, not identifying the “average” or “modal instance” ones. In effect, what we would like to be sampling is not people, but ideas. We imagine that there is a universe of all possible ideas relevant to some topic and that we want to sample this population, not the population of people who have the ideas. Clearly, in order to get all of the ideas, and especially the “outlier” or unusual ones, we have to include a broad and diverse range of participants. Heterogeneity sampling is, in this sense, almost the opposite of modal instance sampling.

Snowball Sampling

In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are not likely to be able to find good lists of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them.

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Unit 12: Sampling. When enough is enough.

46 non-probability sampling.

Probably you’ve guessed it by now, right? If probability is random – NON colloquial random – the non-probability is non-random. Which is actually a lot closer to the concept of “random” as we understand it in ordinary, everyday, informal contexts.  I know – potentially so confusing! Just commit to memory that Random Reduces Bias [in terms of sampling strategies]. NON-random/probability actually adds bias. So. Let’s say you’re doing your deconstruction and you head back to the original – OP – primary research. Mosey on over to the “procedure” and “participants” sections. And see that they used one of the methods below (which, PS, they do NOT always state the “name” of the strategy so you have to recognize it without the label)… SHOULD YOU BELIEVE IT? I’m not saying that you shouldn’t because getting random samples is pretty darned difficult, but you should  definitely practice your critical thinking and add some of that healthy skepticism.

Learning Objectives

What is non-probability sampling?

• Non-Probability Sampling

Non-probability sampling   is a non-random sampling technique. This means that it does not give every individual in a population or sampling frame equal chances of being selected. This means that the sample may be less representative of the target population compared to random sampling.

Types of Non-Probability Sampling

Types of Non-Random or Non-Probability Sampling (a selection. Yes, there are more…):

Convenience Volunteer Snowball Quota Network Purposive/Purposeful Theoretical Construct

• Convenience
• Ex: When deciding who to pull from a sample group you decide to pull members of your family because they are the easiest and most convenient group of people to study.  ( Example from student Kailey Brown)

   2. Volunteer  

• Ex:  The mass emails students receive from the University of Iowa to participate in studies. Whoever wants to participate responds to the email. (Example from student Kailey Brown)

  3. Snowball 

• Ex: One friend finds a survey on how much coffee she drinks. She knows a lot of coffee drinker friends and wants them to participate. Then those friends want to bring in more people to contribute. (Kailey Brown)

  4 . Quota

• Ex: A researcher wants to mimic the population they are studying. The population is 70% people over 60. Therefore, the participants consisted of 70% people over 60.

  5. Network 

• Ex: Recruiting participants from social network sites. (Example from student Monica Bucholz)

 6. Purposive/ Purposeful 

• Ex: In a study about the experiences of adopted children, the researcher chooses to interview children who have been adopted (Example from student Wesley Woods)

 7. Theoretical Construct 

• Ex: In a study about adopted children, the researcher seeks to show the struggles many children go through. The researcher chooses to sample children who have had negative experiences.

Got ideas for questions to include on the exam?

Click this link to add them! [this course element is paused because ya’ll aren’t submitting many questions…]

… Unit 1 … Unit 2 …. Unit 3 … Unit 4 … Unit 5 … Unit 6 … Unit 7 … Unit 8 … Unit 9 … Unit 10 … Unit 11 … Unit 12 … Unit 13 … Unit 14 … Unit 15 … Unit 16 …

• Probability Sampling
• Considerations When Sampling
• On being skeptical [cuz they didn’t rep-re-sent]

non-random sampling technique

researcher chooses those in their sampling frame that are easiest to access

those who participate in the study choose on their own to do so

type of sampling is when someone starts the study and then refers more people to participate in it

like stratified sampling, quota sampling breaks up the sample into subcategories in ways the researcher determines

when a researcher utilizes their social network to recruit participants

researcher purposely and subjectively chooses participants based on how crucial the experiences they can share are to answering the researcher’s research questions

when the researcher selects participants that embody a theory they are interested in

Introduction to Social Scientific Research Methods in Communication (3rd Edition) Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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What is Non-Probability Sampling? Pros, Cons, and Examples

Before any survey can begin, researchers need to consider how they will sample survey participants. Typically, it’s not possible or feasible to survey the entire population of a survey. Therefore, a researcher selects a subset of the larger population. Thankfully, there are a number of options at their disposal. One method is non-probability sampling. In this blog, we look at how it works, different types of non-probability sampling, how it differs from random sampling, and non-probability sampling advantages and disadvantages.

Create your FREE Non-Probability Survey, Poll, or Questionnaire now!

Non-Probability Sampling Definition

Non-probability sampling methods recognize that not everyone will have the chance to take a survey. This is the opposite of probability sampling, which aims to ensure that everyone in the population has an equal chance of receiving a survey. 

To better understand the difference between non-probability sampling vs probability sampling, consider a store owner surveying his customers. He has a customer database of 5,000. Since he can’t survey them all, he decided to survey 10% of them. With probability sampling, which requires all customers to have an equal chance of participating, he uses a number generator (1 – 5,000) to select 500 customers at random that correspond with the numbers generated. Therefore, anyone from the population could have been selected, reducing potential survey bias.

With non-probability sampling, he doesn’t care that everyone has an equal chance of being chosen, he simply wants to survey 500 customers. So, he sends an online survey to the first 500 customers in his database. Or, he could survey each customer that comes into his store until he reaches 500. Either way, 4,500 people did not have the chance to receive a survey which can increase the chances of survey bias. We’ll discuss the pros and cons of this shortly.

Non-Probability Sampling Methods

As mentioned earlier, there are different ways to go about obtaining a non-probability sample. So, how many types of non-probability sampling are there? Generally, a researcher will select one of three non-probability sampling techniques.

1. Convenience Sampling 

This is the quickest and easiest type of non-probability sampling. With convenience sampling, as the name implies, all that matters is convenience. This means the results are typically going to be less than scientific and therefore not applicable to the wider population. For example, a college student wants to learn about alcohol consumption among undergraduates, so she surveys people in her dorm because they’re easily accessible to her (i.e. convenient). 

However, students in dorms are probably less likely to drink compared to those living off campus due to dorm rules, their age, and so on, so it’s not a representative sample. Her dorm may also be all female, leaving out all male students. 

It’s important to note that there are two other subtypes of convenience sampling: Consecutive sampling, in which results are analyzed following each survey and the surveying continues until a conclusion can be reached, and self-selection, in which volunteers sign up to be part of the survey. Read more about convenience sampling .

2. Quota Sampling

Quota sampling is similar to convenience sampling in that anyone convenient to the researcher can be surveyed. The one difference is that there are specific targets for the number of people that need to be surveyed (e.g. 50 men and 50 women). So, using the student in an all-female dorm example, she could survey 50 girls in her dorm but would then need to go to a male dorm and survey 50 boys as well. 

While this is still not the most scientific method, it at least gets a more diverse number of respondents from different subpopulations. Read more about quota sampling .

3. Purposeful Sampling

With the purposeful or purposive sampling method, the researcher uses their understanding of the survey’s purpose and their knowledge of the population to make a conscious decision on who should be included in the sample to serve the overarching goal. Then, the researcher selects the participants accordingly. He or she may opt to do this is a number of ways:

• Heterogeneity sampling , which aims to collect the widest range of opinions and perspectives on a given topic.
• Homogeneous sampling , which aims to collect opinions from like-minded participants (they may all be the same age, gender, race, religion, and so on).
• Deviant sampling , in which participants are selected based on an unusual or special trait.
• Expert sampling , in which specialists on a particular topic are sought out to inform the survey or validate the results of a previous survey.

4. Snowball Sampling

While this method is not common for a lot of surveys, snowball sampling is often put to use when a researcher needs to target specific groups that are hard to find or reach, or who may be hesitant to speak with them. Often, the topic is sensitive or personal, such as studies about illegal immigrants, drug users, or those with rare health conditions. 

Therefore, researchers use a small pool of participants that they’ve found to “nominate,” through their social circle, others they know who fit the criteria. Often, incentives will be provided to the participants to entice them since they may not be forthcoming otherwise. Because topics are often sensitive, Simply Psychology states that researchers must take precautions to protect the privacy of potential subjects, keeping names anonymous and using online encryption techniques .

Advantages and Disadvantages of Non-Probability Sampling

Non-probability has both pros and cons. Here’s a rundown of both that researchers need to be aware of.

5 Pros of Non-Probability Sampling

• It’s a fast and inexpensive way to collect data. Little research is required prior to surveying as the researcher simply seeks out those easily within reach. If the researcher conducts non-probability sampling through an online platform, it becomes even easier as there are no geographical limits.
• It’s a great starting point in which to form quick hypotheses. Then, the researcher can determine if further probability sampling would be beneficial.
• Low response rates don’t factor in, as the researcher continues surveying until they’ve reached their desired sample size. Or, in the case of consecutive sampling, until they have enough data to reach a conclusion.
• It enables researchers to connect with under-represented or niche groups. This is usually accomplished through deviant sampling.
• Because these surveys can be conducted on a whim, opinions on current events and topics can be gathered in near real-time.

5 Cons of Non-Probability Sampling

• Participants receive surveys based on convenience or ease of access. This means there’s a high chance they may not be representative of the greater population. This undermines the validity of results.
• The researcher will likely be unable to calculate the margin of error. Margin or error in surveying refers to how much one can expect survey results to reflect the views from the total population.
• Samples may fill up with people who want to be part of research. This may be because they want the incentive or hold strong views that they want to share. This is common with self-selection sampling and snowball sampling methods.
• Samples sizes may be unclear because there is not a way to measure the boundaries of the relevant population in the study.
• Last but definitely not least, the biggest disadvantage is the potential for sampling bias . Because sample selection is deliberate, there is a big risk that a researcher’s personal views and opinions could easily influence the sample. For example, a researcher may only select people they feel comfortable with. Or, who fit within a particular demographic. Either way, this can greatly impact results. 

Non-Probability Sampling with SurveyLegend Online Surveys

Online surveys are a great way to conduct non-probability sampling. As noted earlier, sure you can stand on a street corner or within a store and survey those who pass by or stop in. Or, you can cast a wider net by sending out online surveys. Since the “who” is not as important as the sample size, you can send out surveys to anyone – and as many as you like until you reach the desired sample size. If you do have some specifics, for example, needing a certain number of men or women, or a particular age group, you can qualify respondents with some eligibility questions based on demographics . 

With SurveyLegend, our online surveys are easy to create and easy on the eyes. You can add pictures to surveys which boosts engagement, triggers memory, and crosses language barriers. Below is an example of one of our surveys with images. This has been designed to match the student drinking survey we highlighted at the start of this blog.

A few things you’ll note:

• The survey begins with a welcome page describing the goal of the survey and includes a survey image.
• The first demographic question includes a qualifier. This way the researcher will know when he or she has collected enough of each type of sample.
• Because gender identity can be a sensitive topic – but an important one particularly for today’s younger generations – multiple choices are offered along with a “prefer not to answer” option.
• If a participant selects that they don’t drink, the questionnaire uses survey logic to immediately take them to the thank you page.
• If a participant selects that they do drink, the survey continues with more questions.
• Various types of survey questions are used to engage participants:  Multiple choice , sliding scale , thumb ratings , emojis , picture questions* , and an open-ended question.

Once again, this survey is live so try it out now. You can also refresh and retake it as many times as you like, as well as view live results.

Non-probability sampling is a quick and easy way to collect data. While there are multiple types of non-probability sampling, they all have one thing in common: They are not random. So, despite the ease of conducting them, there is the potential for survey bias. It’s up to each researcher to weigh the pros and cons of non-probability sampling. Then, it’s time  to determine whether it’s the right method for the study. Whether you choose this type of sampling or another technique, SurveyLegend has you covered. We let you start for free, and have dozens of beautiful and responsive online survey templates from which to choose.

Do you use non-probability sampling when surveying? What do you feel are the biggest pros and cons of this method? Let us know in the comments!

Frequently Asked Questions (FAQs)

Non-probability sampling is a quick, easy, and inexpensive way to survey a subset of a larger population. To collect data, a subjective (or non-random) method is used.

There are four main methods of non-probability sampling with some subtypes. They are convenience sampling (with subsets consecutive and self-selection sampling); quota sampling; purposeful sampling (with subsets heterogeneous, homogeneous, deviant, and expert sampling); and snowball sampling. Read more about all types of survey sampling methods .

Because the participants are not surveyed completely at random, with some selection criteria determined by the researcher, there can be the risk of survey bias. Because of this, non-probability sampling is often used for non-scientific or fun surveys, or as a starting point before diving deeper with probability sampling.

*Image credits: DCStudio, gpointstudio, rawpixel, prostooleh , and racool_studio on Freepik .

Jasko mahmutovic.

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Non-Probability Sampling: Methods, Types, Advantages

Kate williams.

Last Updated:  

29 May 2024

Table Of Contents

• Non-Probability Sampling
• Probability vs Non-Probability
• When to Use
• Best Practices

What’s the secret behind groundbreaking research? Don’t you think it is a smooth amalgamation of intuition and methodology? Sampling is the cornerstone of research, adding credibility to the bits and pieces of the puzzle we try to solve. And, within the realm of sampling, there is probability sampling and non-probability sampling. This blog will look deeper into the meaning, types, methods, and all you need to know about the latter.

But before we begin, let’s look at some familiar terminology.

Sampling is a systematic process to select a subset of individuals or items from a larger population. It employs mathematical and statistical techniques to ensure that the chosen samples represent the characteristics of the population of interest accurately. It allows researchers to gather insights, draw conclusions, and make predictions about the population without the exhaustive effort and resources required to study every individual or element within it.

Sampling is like taking a smart peek at a big group without talking to everyone. Just imagine talking to a few million people and drawing a conclusion! Impossible right?

Researchers use sampling methods to select a subset of individuals or items from a larger population, enabling them to draw conclusions about the entire group.

Moreover, you can use advanced platforms such as SurveySparrow to streamline the sampling process. You can create surveys, collate data, analyze it, and act upon the insights gained. Data collection and analysis have never been easier

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Probability Sampling vs. Non-Probability Sampling

1. Selection: In probability sampling, individuals or items are chosen purely by chance, ensuring every member has a fair shot. Non-probability sampling, on the other hand, lacks randomness; selections are often based on convenience or judgment.

2. Representation: Probability sampling tends to create highly representative samples, reflecting the entire population accurately. In non-probability sampling, representation might not be as precise, potentially missing key traits of the larger population.

3. Bias: Probability sampling boasts low bias, providing unbiased results when executed correctly. Non-probability sampling, due to its non-random nature, might introduce biases, skewing the findings.

4. Accuracy: Probability sampling yields high accuracy, leading to reliable results. Non-probability sampling offers moderate to low accuracy; findings might lack precision due to the absence of randomness.

5. Applicability: Probability sampling is versatile and suitable for various research contexts. Non-probability sampling, while creative, is limited in its applicability and often employed in specific situations where randomness isn’t mandatory.

What is Non-Probability Sampling?

Now, let’s delve deeper.

Nonprobability sampling is a method where samples are chosen without ensuring that every individual or item in the population has an equal or known chance of being selected. Unlike probability sampling, which relies on random selection, non-probability sampling methods are based on the researcher’s judgment, convenience, or specific criteria.

These methods can include convenience sampling, judgmental sampling, quota sampling, and snowball sampling, among others. While non-probability sampling offers flexibility and creativity in research, the lack of randomness can lead to potential biases and reduced accuracy in the obtained results.

While non-probability sampling offers flexibility and creativity in research, the lack of randomness can lead to potential biases and reduced accuracy in the obtained results.

Types of Non-Probability Sampling

Non-probability sampling methods are diverse and offer unique approaches to gathering data. Here are some common types you should know:

1. Convenience Sampling

Convenience sampling involves selecting individuals or items that are most accessible to the researcher. It’s a quick and straightforward method, often used for preliminary research or studies with limited resources.

Let’s say you want to survey smartphone usage in a busy city center.  So, you approach people passing by, collecting responses from those readily available. While convenient, this method may not represent the broader population’s smartphone habits, as it primarily captures the views of urban dwellers.

2. Judgmental Sampling

Judgmental sampling relies on the researcher’s expertise to select specific individuals or items based on their knowledge of the population. It is subjective and can be influenced by the researcher’s biases.

Consider a marketing expert analyzing consumer preferences for a new product. Using their expertise, they select specific focus groups based on age, income, and shopping behavior. By choosing participants relevant to the study, the expert gains insights tailored to the target market, albeit with the risk of personal bias influencing the selection.

3. Quota Sampling

Quota sampling divides the population into subgroups or quotas based on specific characteristics such as age, gender, or occupation. Researchers then select samples from each quota, ensuring proportional representation from different segments.

For instance, if you divide the population into age brackets and select respondents from each category until quotas are met, it makes the findings more comprehensive.

4. Purposive Sampling

Purposive sampling involves selecting specific individuals or items for a particular purpose, often due to their expertise or unique characteristics. Researchers choose samples deliberately to meet the study’s objectives.

5. Snowball Sampling

Snowball sampling is commonly used when studying hard-to-reach or hidden populations. It starts with an initial participant who refers to other potential participants, creating a ‘snowball’ effect. This method helps researchers access populations that are not easily accessible.

When to Use Non-Probability Sampling

Understanding when to use non-probability sampling methods requires careful consideration of research goals, available resources, and the nature of the study population. It’s not like you find one easy and then decide to go with it.

Use it when:

1. You are exploring

When you are in the initial stages of a study and need quick insights, non-probability sampling, especially convenience sampling, proves invaluable. Its speed and simplicity are ideal for exploratory research, providing initial data to shape further investigation.

2. You have limited resources

When you have limited time, budget, or access to a diverse population, methods such as quota or convenience sampling become practical choices. These methods offer feasible solutions without draining valuable resources.

3. You go into qualitative studies

Qualitative research often aims for depth rather than breadth.  Methods like purposive or snowball sampling allow researchers to select participants based on specific traits or experiences, enhancing the richness of qualitative data.

4. You try to understand social phenomena

When studying social behaviors, attitudes, or phenomena that are difficult to quantify, non-probability sampling methods excel. They allow researchers to delve deep into human experiences, capturing nuances that might be overlooked in structured, probability-based approaches.

5. You are conducting pilot studies

Non-probability sampling methods are commonly used in pilot studies. Researchers use convenience or judgmental sampling to test methodologies, questionnaires, or hypotheses before committing to large-scale, resource-intensive studies. This helps in refining research strategies before full-scale implementation.

Advantages of Non-Probability Sampling

It offers swift, flexible, and ethically sound advantages, catering to specific research requirements.

• Cost-effectiveness: It is a budget-friendly way of collecting data.
• Time Efficiency: Offers quick insights due to rapid implementation
• Flexibility: Participant selection is tailored based on specific criteria.
• Exploratory Focus: Ideal for generating hypotheses and insights swiftly.
• Access to Hidden Populations: Reaches elusive or marginalized groups effectively.
• Qualitative Depth: Enhances depth in qualitative research studies.
• Ethical Considerations: It respects participant privacy in sensitive research contexts.

Best Practices for Non-probability Sampling

#1 define clear objectives.

Clearly outline research goals and questions to guide the sampling process. Specific objectives enhance the relevance of participant selection.

#2 Understand the Population

Thoroughly grasp the characteristics of the target population. This understanding informs the selection criteria, ensuring the chosen sample is representative.

#3 Use Multiple Methods

Combine different non-probability sampling techniques strategically. Employing various methods enhances the diversity of perspectives and enriches the study’s findings.

#4 Minimize Bias

Acknowledge potential biases and take steps to minimize them. Also, be aware of researcher biases and implement techniques to reduce their impact on participant selection.

#5 Ensure Transparency

Be clear and transparent. You must document the sampling methods and rationale. Transparent reporting enhances the study’s credibility and allows for critical evaluation by peers.

#6 Validate Results

Lastly, validate the findings through comparison with existing data or cross-referencing with other research studies. This enhances the reliability of conclusions drawn from the samples.

Non-probability sampling shines for its flexibility and speed. By understanding the nuances of different methods and adhering to best practices, you can generate meaningful insights, especially in exploratory or qualitative studies. But again, while it’s handy for quick insights, it has limitations like potential sampling biases . Use it judiciously by recognizing its strengths and mitigating its weaknesses.

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Understanding Probability vs. Non-Probability Sampling Methods

Table of Contents

Have you ever wondered how researchers ensure their studies reflect the wider population, or how they choose who gets to participate in a survey? The answer lies in the art and science of sampling methods . In the world of research, these methods are the unsung heroes that determine the quality and credibility of any study. Let’s dive into the fascinating world of probability and non- probability sampling methods and uncover how they can either make or break a research project.

What is Probability Sampling?

Imagine you’re at a party where everyone has an equal chance of winning a prize in a lucky draw. That’s what probability sampling is like in research. It’s a method where every member of the population has a known, non-zero chance of being selected for the study. This is crucial for studies aiming to draw conclusions about a population because it allows researchers to calculate the likelihood of each outcome, giving us statistics we can trust.

Random Sampling: The Gold Standard

Random sampling is the purest form of probability sampling. Picture a lottery where each ticket has an equal chance of being drawn. In research, this could involve using a random number generator to pick participants from a list. It’s simple, unbiased, and easy to understand.

Systematic Sampling: Order in Randomness

Systematic sampling introduces a bit of order to the randomness. It’s like choosing every 10th person who walks through a door. You start off randomly, then follow a fixed interval. This method ensures a spread across the population, reducing the risk of clustering.

Stratified Sampling: The Divide and Conquer Approach

Stratified sampling is a bit like organizing a school photo where students are grouped by height to get a balanced picture. Here, the population is divided into subgroups, or ‘strata’, and samples are taken from each stratum. This ensures all categories of the population are represented proportionally.

Cluster Sampling: Sampling in Bunches

Imagine picking whole basketball teams instead of individual players. That’s cluster sampling . It involves dividing the population into clusters and then randomly selecting entire clusters for the study. It’s efficient and cost-effective, especially when dealing with a large, spread-out population.

Non-Probability Sampling: When Randomness is Not an Option

Sometimes, random selection isn’t practical or necessary. Non-probability sampling steps in when researchers are exploring new areas or when the focus is more on qualitative insights than statistical precision. Here, the extent to which the sample represents the population is unknown, and that’s okay for the research goals.

Convenience Sampling: Grabbing What’s Closest

Convenience sampling is like grabbing the nearest book to prop up a wobbly table—it’s about what’s easily available. Researchers use this method when they need to gather data quickly and conveniently, often leading to a sample that’s not representative of the population.

Judgment Sampling: The Expert’s Choice

Judgment sampling relies on the researcher’s expertise to select participants. Think of a chef handpicking the best ingredients for a dish. It’s subjective, but when researchers have a deep understanding of the subject matter, they can identify participants who provide the most valuable insights.

Quota Sampling: Filling Categories to the Brim

Quota sampling is like filling seats in a theater section by section. Researchers decide on the proportion of different groups to include and then non-randomly select participants to fill those quotas. It’s a way to ensure diversity in the sample without relying on randomness.

Snowball Sampling: Rolling Along Connections

Snowball sampling is akin to networking at a social event. You start with a few contacts and ask them to introduce you to others. This method is especially useful when seeking participants who are hard to locate or reluctant to participate, such as members of a subculture or individuals with a rare condition.

Choosing the Right Sampling Method

The decision between probability and non\-probability sampling hinges on the research question and objectives. Probability sampling is the go-to for generalizable, quantitative studies, while non-probability sampling shines in exploratory or qualitative research. Budget, timeframe, and the availability of a complete list of the population also play crucial roles in this choice.

Impact on Research Outcomes

The sampling method can significantly influence the study’s findings. A well-chosen sample leads to accurate and credible results, while a poorly selected sample can skew the study, leading to inaccurate conclusions. Thus, understanding and selecting the appropriate sampling method is essential for any credible research endeavor.

Sampling methods are the backbone of research, determining the validity and applicability of study results. By grasping the nuances of probability and non-probability sampling, we can better appreciate the efforts that go into producing reliable data and making informed decisions based on that data.

What do you think? How might the choice of sampling method affect the credibility of a study in your view? Can you think of a situation where non-probability sampling would be more beneficial than probability sampling?

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Research Methodologies & Methods

1 Logic of Inquiry in Social Research

• A Science of Society
• Comte’s Ideas on the Nature of Sociology
• Observation in Social Sciences
• Logical Understanding of Social Reality

2 Empirical Approach

• Empirical Approach
• Rules of Data Collection
• Cultural Relativism
• Problems Encountered in Data Collection
• Difference between Common Sense and Science
• What is Ethical?
• What is Normal?
• Understanding the Data Collected
• Managing Diversities in Social Research
• Problematising the Object of Study
• Conclusion: Return to Good Old Empirical Approach

3 Diverse Logic of Theory Building

• Concern with Theory in Sociology
• Concepts: Basic Elements of Theories
• Why Do We Need Theory?
• Hypothesis Description and Experimentation
• Controlled Experiment
• Designing an Experiment
• How to Test a Hypothesis
• Sensitivity to Alternative Explanations
• Rival Hypothesis Construction
• The Use and Scope of Social Science Theory
• Theory Building and Researcher’s Values

4 Theoretical Analysis

• Premises of Evolutionary and Functional Theories
• Critique of Evolutionary and Functional Theories
• Turning away from Functionalism
• What after Functionalism
• Post-modernism
• Trends other than Post-modernism

5 Issues of Epistemology

• Some Major Concerns of Epistemology
• Rationalism
• Phenomenology: Bracketing Experience

6 Philosophy of Social Science

• Foundations of Science
• Science, Modernity, and Sociology
• Rethinking Science
• Crisis in Foundation

7 Positivism and its Critique

• Heroic Science and Origin of Positivism
• Early Positivism
• Consolidation of Positivism
• Critiques of Positivism

8 Hermeneutics

• Methodological Disputes in the Social Sciences
• Tracing the History of Hermeneutics
• Hermeneutics and Sociology
• Philosophical Hermeneutics
• The Hermeneutics of Suspicion
• Phenomenology and Hermeneutics

9 Comparative Method

• Relationship with Common Sense; Interrogating Ideological Location
• The Historical Context
• Elements of the Comparative Approach

10 Feminist Approach

• Features of the Feminist Method
• Feminist Methods adopt the Reflexive Stance
• Feminist Discourse in India

11 Participatory Method

• Delineation of Key Features

12 Types of Research

• Basic and Applied Research
• Descriptive and Analytical Research
• Empirical and Exploratory Research
• Quantitative and Qualitative Research
• Explanatory (Causal) and Longitudinal Research
• Experimental and Evaluative Research
• Participatory Action Research

13 Methods of Research

• Evolutionary Method
• Comparative Method
• Historical Method
• Personal Documents

14 Elements of Research Design

• Structuring the Research Process

15 Sampling Methods and Estimation of Sample Size

• Classification of Sampling Methods
• Sample Size

16 Measures of Central Tendency

• Relationship between Mean, Mode, and Median
• Choosing a Measure of Central Tendency

17 Measures of Dispersion and Variability

• The Variance
• The Standard Deviation
• Coefficient of Variation

18 Statistical Inference- Tests of Hypothesis

• Statistical Inference
• Tests of Significance

19 Correlation and Regression

• Correlation
• Method of Calculating Correlation of Ungrouped Data
• Method Of Calculating Correlation Of Grouped Data

20 Survey Method

• Rationale of Survey Research Method
• History of Survey Research
• Defining Survey Research
• Sampling and Survey Techniques
• Operationalising Survey Research Tools
• Advantages and Weaknesses of Survey Research

21 Survey Design

• Preliminary Considerations
• Stages / Phases in Survey Research
• Formulation of Research Question
• Survey Research Designs
• Sampling Design

22 Survey Instrumentation

• Techniques/Instruments for Data Collection
• Questionnaire Construction
• Issues in Designing a Survey Instrument

23 Survey Execution and Data Analysis

• Problems and Issues in Executing Survey Research
• Data Analysis
• Ethical Issues in Survey Research

24 Field Research – I

• History of Field Research
• Ethnography
• Theme Selection
• Gaining Entry in the Field
• Key Informants
• Participant Observation

25 Field Research – II

• Interview its Types and Process
• Feminist and Postmodernist Perspectives on Interviewing
• Narrative Analysis
• Interpretation
• Case Study and its Types
• Life Histories
• Oral History
• PRA and RRA Techniques

26 Reliability, Validity and Triangulation

• Concepts of Reliability and Validity
• Three Types of “Reliability”
• Working Towards Reliability
• Procedural Validity
• Field Research as a Validity Check
• Method Appropriate Criteria
• Triangulation
• Ethical Considerations in Qualitative Research

27 Qualitative Data Formatting and Processing

• Qualitative Data Processing and Analysis
• Description
• Classification
• Making Connections
• Theoretical Coding
• Qualitative Content Analysis

28 Writing up Qualitative Data

• Problems of Writing Up
• Grasp and Then Render
• “Writing Down” and “Writing Up”
• Write Early
• Writing Styles
• First Draft

29 Using Internet and Word Processor

• What is Internet and How Does it Work?
• Internet Services
• Searching on the Web: Search Engines
• Accessing and Using Online Information
• Online Journals and Texts
• Statistical Reference Sites
• Data Sources
• Uses of E-mail Services in Research

30 Using SPSS for Data Analysis Contents

• Introduction
• Starting and Exiting SPSS
• Creating a Data File
• Univariate Analysis
• Bivariate Analysis

31 Using SPSS in Report Writing

• Why to Use SPSS
• Working with SPSS Output
• Copying SPSS Output to MS Word Document

32 Tabulation and Graphic Presentation- Case Studies

• Structure for Presentation of Research Findings
• Data Presentation: Editing, Coding, and Transcribing
• Case Studies
• Qualitative Data Analysis and Presentation through Software
• Types of ICT used for Research

33 Guidelines to Research Project Assignment

• Overview of Research Methodologies and Methods (MSO 002)
• Research Project Objectives
• Preparation for Research Project
• Stages of the Research Project
• Supervision During the Research Project
• Submission of Research Project
• Methodology for Evaluating Research Project

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