Which Sampling Method Does Not Require a Frame

Which Sampling Method Does Not Require a Frame

Sampling Methods | Types, Techniques & Examples

When you lot deport enquiry about a group of people, information technology’south rarely possible to collect information from every person in that group. Instead, you select a sample. The sample is the group of individuals who volition actually participate in the inquiry.

To draw valid conclusions from your results, you take to carefully decide how you will select a sample that is representative of the group as a whole. There are ii types of sampling methods:

  • Probability sampling
    involves random selection, allowing y’all to make strong statistical inferences almost the whole group.
  • Not-probability sampling
    involves non-random pick based on convenience or other criteria, allowing you to hands collect data.

Yous should clearly explain how yous selected your sample in the methodology section of your paper or thesis.

Population vs sample

First, you lot need to empathise the departure between a population and a sample, and place the target population of your inquiry.

  • The
    is the entire group that you want to draw conclusions near.
  • The
    is the specific group of individuals that you volition collect information from.

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

It can be very broad or quite narrow: maybe you want to make inferences about the whole developed population of your country; peradventure your research focuses on customers of a certain visitor, patients with a specific health condition, or students in a single schoolhouse.

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

If the population is very big, demographically mixed, and geographically dispersed, it might exist difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual listing of individuals that the sample will exist drawn from. Ideally, it should include the unabridged target population (and nobody who is not role of that population).

Example: Sampling frame
You are doing research on working conditions at Company X. Your population is all thousand employees of the company. Your sampling frame is the company’south HR database which lists the names and contact details of every employee.

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 blueprint. There are different sample size calculators and formulas depending on what you lot want to achieve with statistical analysis.

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    Probability sampling methods

    Probability sampling ways that every fellow member of the population has a chance of being selected. It is mainly used in quantitative research. If yous desire to produce results that are representative of the whole population, probability sampling techniques are the most valid selection.

    There are iv main types of probability sample.

    Probability sampling

    i. Simple random sampling

    In a unproblematic 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 hazard.

    Case: Simple random sampling
    Y’all want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from one to 1000, and use a random number generator to select 100 numbers.

    2. Systematic sampling

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

    Example: Systematic sampling
    All employees of the visitor are listed in alphabetical order. From the commencement x numbers, you randomly select a starting point: number half-dozen. From number vi onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

    If y’all use this technique, it is important to make sure that in that location is no subconscious pattern in the list that might skew the sample. For example, if the 60 minutes database groups employees by team, and team members are listed in order of seniority, there is a run 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. Information technology allows y’all draw more than 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 feature (e.chiliad. gender, age range, income subclass, chore 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.

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    Example: Stratified sampling
    The company has 800 female person employees and 200 male employees. Y’all desire to ensure that the sample reflects the gender residuum of the company, so yous sort the population into two strata based on gender. And so you lot use random sampling on each group, selecting 80 women and 20 men, which gives you lot a representative sample of 100 people.

    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 lot randomly select entire subgroups.

    If information technology is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, yous can also sample individuals from inside each cluster using one of the techniques in a higher place. This is chosen multistage sampling.

    This method is adept for dealing with large and dispersed populations, but at that place is more run a risk of mistake in the sample, equally in that location could be substantial differences between clusters. Information technology’s difficult to guarantee that the sampled clusters are really representative of the whole population.

    Example: Cluster sampling
    The company has offices in 10 cities across the country (all with roughly the aforementioned number of employees in similar roles). You don’t accept the capacity to travel to every office to collect your data, and so you lot utilize random sampling to select three offices – these are your clusters.

    Non-probability sampling methods

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

    This type of sample is easier and cheaper to access, only it has a higher chance of sampling bias. That means the inferences you can make well-nigh the population are weaker than with probability samples, and your conclusions may exist more than limited. If you employ a not-probability sample, you should still aim to arrive as representative of the population equally 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, simply to develop an initial agreement of a minor or under-researched population.

    Non probability sampling

    one. Convenience sampling

    A convenience sample simply includes the individuals who happen to be almost attainable to the researcher.

    This is an like shooting fish in a barrel and inexpensive way to gather initial data, only in that location is no way to tell if the sample is representative of the population, and then it can’t produce generalizable results.

    Example: Convenience sampling
    You are researching opinions about pupil support services in your academy, and then after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient fashion to gather data, only equally y’all just surveyed students taking the aforementioned classes every bit you at the same level, the sample is not representative of all the students at your university.

    two. Voluntary response sampling

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

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    Voluntary response samples are ever at least somewhat biased, as some people will inherently exist more likely to volunteer than others.

    Example: Voluntary response sampling
    You send out the survey to all students at your academy and a lot of students decide to consummate it. This tin can certainly give y’all some insight into the topic, just the people who responded are more likely to be those who have potent opinions near the pupil support services, so you can’t be sure that their opinions are representative of all students.

    three. Purposive sampling

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

    It is often used in qualitative research, where the researcher wants to gain detailed knowledge well-nigh a specific miracle rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have articulate criteria and rationale for inclusion.

    Example: Purposive sampling
    You desire to know more than about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

    iv. 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 go in contact with more people.

    Instance: Snowball sampling
    Yous are researching experiences of homelessness in your metropolis. Since there is no list of all homeless people in the metropolis, probability sampling isn’t possible. You meet one person who agrees to participate in the inquiry, and she puts you in contact with other homeless people that she knows in the area.

    Frequently asked questions nearly sampling

    What is sampling?

    is a subset of individuals from a larger population.

    ways selecting the group that you volition really collect information from in your research. For example, if you are researching the opinions of students in your university, y’all could survey a sample of 100 students.

    In statistics, sampling allows y’all to test a hypothesis virtually the characteristics of a population.

    Why are samples used in inquiry?

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

    What is multistage sampling?

    In multistage sampling, or multistage cluster sampling, yous 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 lot have advantage of hierarchical groupings (e.k., from land to metropolis to neighborhood) to create a sample that’south less expensive and time-consuming to collect information from.

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    Which Sampling Method Does Not Require a Frame

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