Non probability

Sampling

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

Probability sampling involves random people selection, allowing you to make strong statistical

inferences about the whole group.

Non-probability sampling

Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data. 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.

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.

Types

There are four common types of non-probability sampling:

● Convenience sampling

● Quota sampling

● Snowball sampling

● Purposive (judgmental) sampling

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.

Disadvantages of non-probability sampling

● This sampling system has a significant flaw in that it is very subjective by nature because the investigator's convenience, beliefs, biases, and prejudices are the only factors that go into choosing the sample. For instance, let us say the researcher wants to carry out a study to find out how much the residents of a certain city make each month. The investigator may deliberately select to include only those persons who live in poorer neighborhoods and exclude those who reside in nicer neighborhoods if he wants to show that the city's standard of living has declined.

● If the sample size is very large, this method cannot be employed since the researcher is unable to personally choose a large number of units in a practical amount of time.

● An unrepresentative sample may emerge from the investigator making poor decisions due to a lack of experience or subject matter expertise. This could result in inaccurate findings from the study and conclusions.

● Unlike probability sampling, sample selection does not involve any probabilities, hence it is impossible to estimate the standard error.

Convenience sampling

Convenience sampling is a simple and easy way to get information compared to other sampling methods. Most of the time, simple and easy go well together. But you need to know what it is so you know when to use it and when not to. It is a type of sampling that doesn’t depend on chance and is often used in research studies. This sampling technique involves choosing people who are easy for the researcher to reach and get in touch with. Instead of picking people at random from a certain population, convenience sampling involves picking the people who are easiest for the researcher to get information from. Convenience sampling is often used when other types of sampling methods are hard or impossible to use because of time, cost, or other issues. Even though it can be a quick and easy way to get data, it can also have biases and limitations that can affect how well the results can be used in the real world and how reliable they are. Convenience sampling is defined as a method adopted by researchers where they collect market research data

from a conveniently available pool of respondents. It is the most commonly used sampling as it’s incredibly prompt, uncomplicated, and economical. Members are often readily approachable tobe a part of the sample. Researchers use various sampling techniques in situations where there are large populations. In most cases, testing the entire community is practically impossible because they are not easy to reach. Researchers use convenience sampling in situations where additional inputs are not necessary for the principal research. There are no criteria required to be a part of this sample. Thus, it becomes incredibly simplified to include elements in this sample. All components of the population are eligible and dependent on the researcher’s proximity to get involved in the sample. The researcher chooses members merely based on proximity and doesn’t consider whether they represent the entire population or not. Using this technique, they can observe habits, opinions, and viewpoints in the easiest possible manner.

Examples

● An example of convenience sampling is A new NGO that wants to establish itself in 20 cities. It selects the top 20 cities to serve based on the proximity to where they’re based.

● Another example of Convenience sampling based on location : Suppose you are researching why people visit Monroe Lake Recreation Area, a popular recreational

destination in your county. To gather insights, you stand in a parking area and approach people at random, asking them if they would be interested in participating in a

five-minute anonymous survey on their preferred recreational activities. To maximize the number of responses, you also create flyers with a scan able QR code and a shortened URL link. You place them at the Welcome Center and other locations around the lake.

When can we use convenience sampling?

Often, researchers can find themselves in a number of difficult tight spots at work:

● Having a low budget to conduct research or budgetary constraints .

● No need or desire for a representative sample to move forward with your research.

● You don’t have access to the full target population for a representative sample.

● A requirement to act quickly within a limited timeframe to meet a deadline. So, what do you do when these situations occur? Convenience sampling might be the best

solution to help you get the results you need, in the time and budget you have.

How does convenience sampling work?

If you’re curious on how to get started, it’s one of the simplest methods:

● Think about what you aim to achieve in your research.

● Confirm who would be the target population that would help your Research Think about where you could go to speak to these people in a convenient way

(e.g. would they be found in-person at a precise location, or online in a forum or

group?).

● Prepare your questions into a survey that asks questions for your research.

● Ask willing people for your convenience sample and give your survey to them.

That’s it. No need to explore the full population (if you have one) and divide them into sub-sections, get in touch with the sample ahead of time or find a representative sample. Places

you could use include your workplace, a mall, a high street, an online interest group, a club, etc.

Advantages of convenience sampling

Here are the advantages of adopting a convenience sampling approach:

Collect data quickly:- In situations where time is a constraint, many researchers choose this

method for quick data collection. The rules to gather elements for the sample are the least

complicated in comparison to techniques such as simple random sampling, stratified sampling,

and systematic sampling. Due to this simplicity, data collection takes minimal time.

Inexpensive to create samples:-The money and time invested in other probability sampling

methods are quite large compared to convenience sampling. It allows researchers to generate

more samples with less or no investment and in a brief period.

Easy to do research:-The name of this surveying technique clarifies how samples are formed.

Elements are easily accessible by the researchers so collecting members for the sample becomes

easy.

Low cost:-Low cost is one of the main reasons why researchers adopt this technique. When on a

small budget, researchers – especially students, can use the budget in other areas of the project.

Readily available sample:-Data collection is easy and accessible. Most convenience sampling

considers the population at hand. Samples are readily available to the researcher. They do not

have to move around too much for data collection. Quotas are met quickly, and the data

collection can commence even within a few hours.

Fewer rules to follow:-It doesn’t require going through a checklist to filter members of an

audience. Here, gathering critical information and data becomes uncomplicated. For instance, if

an NGO wants to survey women’s empowerment, they can go to schools, colleges, offices, etc.,

in their proximity and gather quick responses.

Disadvantages of convenience sampling

As with any sampling method, there will be some drawbacks:

Sampling bias:-As the sample is based on people who are willing at the time and place that the researcher is present, you won’t be gaining a range of people each time you’re collecting data. In addition, the research subjectively chooses people to ask if they would like to be a part of the research, so this could influence the final sample as well.

Selection bias:- Many researchers might point out that having a convenience sample may end up excluding demographic subsets from the results. Also, the volunteer nature of the participation means that people who are inclined to know about the subject or pro-topic may appear more represented in the data.

Unable to generalize data:-As the sample will be unrepresentative of the total population, you will find it hard to generalize about the population as a whole.

Low external validity:-If you do base research only on convenience sampling without replicating results or adding in an additional probability-based sampling method, your research

findings might lack credibility within the wider research industry.

Positivity bias:-You may end up having a positivity bias if the people you recruit are too close to you personally and know you want certain results, while people from your workplace may want To please the researcher in general. Breaking down results into demographic data may be

more difficult:- This is because you may have done data collection on the same type of person, based on where you’ve sampled from (for example, if you’re in an elderly home, you’ll have more participants that are older). This might lead to under-representation or over-representation in some population subgroups.

Snowball sampling

Snowball sampling, also known as chain-referral sampling, is a non-probability sampling method where currently enrolled research participants help recruit future subjects for a study. Snowball

sampling is often used in qualitative research when the population is hard-to-reach or hidden. It’s particularly useful when studying sensitive topics or when the members of a population are difficult to locate.

The process starts with a small group of initial respondents (seeds). These initial respondents then refer the researcher to other potential respondents they know within the target population.

Those respondents then refer the researcher to others, and so on. This process continues until the desired sample size is reached.

This sampling technique is called “snowball” because the sample group grows like a rolling snowball. Non-probability sampling means that researchers, or other participants, choose the sample instead of randomly selecting it, so not all population members have an equal chance of being selected for the study.

Example

● This sampling technique can be used for a population, where there is no easily available data like their demographic information. For example, the homeless or list of members of an elite club, whose personal details cannot be obtained easily.

● People with rare diseases are quite difficult to locate. However, if a researcher is carrying out a research study similar in nature, finding the primary data source can be a challenge.

Once he/she is identified, they usually have information about more such similar

individuals.

● People who belong to a cult or are religious extremists or hackers usually fall under this category. A researcher will have to use snowball sampling to identify these individuals and extract information from them.

Types of snowball sampling

Linear snowball sampling

This sampling technique can help you form a sample group by contacting one individual at a time. Each individual might refer you to an additional potential research subject, who then

suggests another individual. This chain can continue until you achieve the ideal sample size for the topic you're investigating.

Exponential non discriminative snowball sampling

In this type of sampling, you recruit the first subject of a target population. That first subject can then provide multiple other referrals for the study. Each new referral also generates multiple referrals until you have enough subjects for the study.

Exponential discriminatory snowball sampling

This technique starts with the recruitment of one research subject. This individual can recommend multiple potential subjects for the study. Among those referrals, you choose the most qualified candidates for your research. This snowball sampling method can help you select quality subjects for your research.

When we use snowball sampling

Rare or Hidden Populations: When studying groups that are hard to reach or are marginalized, such as specific minority communities, individuals with certain health conditions, or illegal activities.

Limited Access: In cases where direct access to the target population is difficult due to geographical, social, or cultural barriers.

Exploratory Research: When conducting preliminary or exploratory studies where little is known about the population and initial information is required to start the research.

Networked Communities: In researching interconnected or tightly-knit groups where one member's connections can lead to the discovery of others within the same network.

Small Population Size: When the population being studied is relatively small and traditional sampling methods might not yield enough participants for statistical significance.

Qualitative Studies: Particularly useful in qualitative research where in-depth information and diverse perspectives from the population are essential.

How does snowball ball sampling work

Identifying Initial Participants: Researchers begin by selecting a small number of individuals who fit the criteria for the study or belong to the target population. These individuals serve as the starting point.

Contacting and Engaging Initial Participants:

Researchers approach these initial participants, explain the study's purpose, and collect data from them. They also ask for referrals to other individuals who meet the study's criteria.

Expanding the Sample: The referred individuals become additional participants, and the process repeats. Each new participant is asked for further referrals, creating a "snowball effect"

as the sample size grows.

Iterative Process: This iterative process continues until the desired sample size is achieved or when data saturation occurs (meaning that no new information or participants are being added).

Data Collection: Throughout this process, researchers collect data from each participant using interviews, surveys, observations, or other methods suitable for the research objectives.

Analysis and Interpretation: Finally, researchers analyze the collected data to draw conclusions and insights based on the information gathered from the expanded network of participants.

Pros of snowball sampling

Quick sample sourcing: With subjects making referrals, researchers spend less time looking for subjects. This sample sourcing allows you to focus your time and energy on conducting research.

Cost-effective method: Much of the sourcing for snowball sampling comes from sample referrals, which means it takes less time to source a data population. This method can be cost-effective because you aren't actively seeking sources independently.

Access to target groups: Snowball sampling allows you to gain access to difficult target groups because you receive referrals from your primary sources. Certain groups are hard to contact or even locate unless you have source referrals.

Knowledge of population characteristics: These sampling methods can help you identify characteristics of the target population you might not have known. This knowledge can help you make connections between the population and your research.

Minimal source planning: With subjects making referrals, you can perform minimal source planning. Many subjects may contact you directly, or subjects can name other potential sources for you to contact.

Cons of snowball sampling

Wrong Anchoring: Anchoring in snowball sampling occurs when the initial participants ("anchors") heavily influence the subsequent selection process by referring to individuals who share similar characteristics or perspectives. This can lead to a biased sample that doesn't Represent the broader population accurately.

Not random :Instead of using a random sampling method, participants are recruited based on existing connections or referrals, leading to a non-random, purposive selection process. This can

introduce bias since individuals with specific characteristics or connections are more likely to be included, potentially limiting the sample's representativeness of the overall population.

Community biases: Community biases in snowball sampling refer to the potential for the

sample to be influenced by the specific characteristics, beliefs, or preferences of the community from which participants are drawn. This could result in a sample that doesn't reflect the diversity or variations present in the larger population, impacting the generalizability of the findings.

Quota sampling

Quota sampling is defined as a non-probability sampling method that relies on the non-random selection of a predetermined number or proportion of units, called a quota sampling. A quota is a fixed minimum or maximum number of a particular group of people allowed to do something. This means that quota sampling gives researchers some ability to control certain aspects of their sample selection. In controlled sampling, there are some limitations . Either way, because the researcher is in some ways controlling the sample, it cannot be applied to the wider population and there is a high risk of survey bias being introduced. Quota sampling is a sampling method in which researchers create a convenience sample involving individuals that represent a population. Researchers choose these individuals according to specific traits or qualities. They decide and create quotas so that the market research samples can be useful in collecting data. These samples can be generalized to the entire population. The final subset will be decided only according to the interviewer’s or researcher’s knowledge of the population.

Examples

● A cigarette company wants to find out what age group prefers what brand of cigarettes in

a particular city. They apply survey quota on the age groups of 21-30, 31-40, 41-50, and 51st+. From this information, the researcher gauges the smoking trend among the population of the city.

● A researcher wants to survey individuals about what smartphone brand they prefer to use. He or she considers a sample size of 500 respondents. Also, he or she is only interested in surveying ten states in the US. Lets we see how the researcher can divide the population by quotas Gender : 250 males and 250 females

Age : 100 respondents each between the ages of 16-20, 21-30, 31-40, 41-50, and 51+

Employment status : 350 employed and 150 unemployed people.

For example, out of the 150 unemployed people, 100 must be students.

Location: 50 responses per state

Depending on the type of research, the researcher can apply quotas based on the sampling frame.

It is not necessary for the researcher to divide the quotas equally. He or she divides the quotas as

per his or her need as shown in the example where the researcher interviews 350 employed and

only 150 unemployed individuals.

How to perform quota sampling

Probability sampling techniques involve a significant amount of rules that the researcher needs to

follow to form samples. But, since quota sampling is a non-probability sampling technique, there

are no rules for formally creating samples. There are four steps to form a quota sampling.

Following steps

Divide the sample population into subgroups

With stratified random sampling, the researcher bifurcates the entire population into mutually

exhaustive subgroups i.e the elements of each of the subgroups becomes a part of only one of

those subgroups. Here, the researcher applies random selection.

Figure out the weight age of subgroups

The researcher evaluates the proportion in which the subgroups exist in the population. He/she

maintains this proportion in the sample selected using this type of sampling method. Subgroup

analysis is crucial for tailoring treatments to specific patient groups, optimizing healthcare

outcomes.

example, if 58% of the people who are interested in purchasing your Bluetooth headphones

are between the age group of 25-35 years, your subgroups also should have the same percentages

of people belonging to the respective age group

Select an appropriate sample size

In the third step, the researcher should select the sample size while maintaining the proportion

evaluated in the previous step. If the population size is 500, the researcher can pick a sample of

50 elements.

The sample chosen after following the first three steps should represent the target population.

Conduct surveys according to the quotas defined

Make sure to stick to the predefined quotas to achieve actual actionable results. Don’t survey

quotas that are full and focus on completing surveys for each quota.

Characteristics of Quota sampling

There are some characteristics of quota sampling

● Aims to get the best representation of respondents in the final sample.

● Quotas replicate the population of interest in a real sense.

● The estimates produced are more representative.

● The quality of quota samples vary.

● Saves research data collection time as the sample represents the population.

● Saves research costs if the quotas accurately represent the population.

● It monitors the number of types of individuals who take the survey.

● The researcher always divides the population into subgroups.

● The sample represents the entire population.

● Researchers use the sampling method to identify the traits of a specific group of people.

When we can use quota sampling

In situations where researchers have specific criteria for conducting research, it allows the

selection of subgroups, due to which it becomes extremely convenient for researchers to obtain

desired results. A trait or characteristic can be the filter for subgroup formation.

The researcher uses this method when he/she has time constraints. Applying quotas gives the

researcher an idea of the whole population of interest in very little time.

Quotas are applied when the researcher is on a tight budget. Instead of researching a large

population, the researcher saves money by using a few quotas to get the whole picture of the

population.

Some research studies do not require pinpoint accuracy due to the nature of the research project.

It is ideal for applying to quota sampling for these studies.

Advantages of quota sampling

There are four advantages of quota sampling.

Saves time: Because of the involvement of a quota for sample creation, this sampling process is

quick and straightforward.

Research convenience: By using quota sampling and appropriate research questions,

interpreting information and responses to the survey is a much convenient process for a

researcher.

Accurate representation of the population of interest: Researchers effectively represent a

population using this sampling technique. There is no room for over-representation as this

sampling technique helps researchers to study the population using specific quotas.

Saves money: The budget required for executing this sampling method is minimalistic.

Disadvantages of quota sampling

Quota sampling also comes with some challenges

● Since quota sampling doesn’t use random selection and the researcher decides who is

included in the sample, it can lead to research biases like selection bias.

● It is not always possible to divide the population into mutually exclusive groups.

Specifically, people may belong to more than one group.

There are times when people cannot be clearly categorized, which impacts the data

collection process and can lead to omitted variable bias and information bias.

● As only specific characteristics of the population are taken into account when you stratify

your sample into subgroups, inaccuracy is very possible. For example, a study with

subgroups of gender identity and income may not accurately represent other traits like

age, ethnicity, or location in the final sample. This can also lead to information bias.