January 30th , 2020 SAMPLING
Types of sampling: simple random, stratified random sampling, cluster sampling, systematic sampling, multi- stage random sampling, non-probability sampling: accidental sampling, quota sampling, purposive sampling
Sampling is defined as the process of selecting certain members or a subset of the population to make statistical inferences from them and to estimate characteristics of the whole population.
Sampling and types of sampling methods commonly used in quantitative research
A population is a group of individual units with some commonality.
The group from which the data is drawn is a representative sample of the population the results of the study can be generalized to the population as a whole.
Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study.
Simple Random Sampling
Does not rely on the use of randomization techniques to select members.
Modal instance sampling
Use of the Non-Probability Sampling Method
The Sampling Distribution
The Sample Size Explained in One Minute: From Definition to Examples and Research Tips
Bias in Sample Selection
Sampling & its 8 Types: Research Methodology
Research methods sampling lesson 2
Sampling is the process of choosing participants for a research study. .
Definition: Sampling is defined as the method by which some members of a larger group are selected. The usual goal is sample those members so that they are representative of the group as a whole.
- What is the purpose of selecting a smaller group of participants from a larger group?
- Do the women included on this list represent the larger group?
- Would you choose participants differently if this was your study? If so, how would you do it?
- What would happen if the investigator chose individuals from the list that she knew? Would this affect the results of the study?
The concept of sample is intrinsic to survey research
The method by which the sample is selected from a sampling frame is integral to the external validity of a survey: the sample has to be representative of the larger population to obtain a composite profile of that population.
There are methodological factors to consider when deciding who will be in a sample:
How will the sample be selected?
What is the optimal sample size to minimize sampling error?
How can response rates be maximized?
The survey methods discussed below influence how a sample is selected and the size of the sample.
There are two categories of sampling: random and non-random sampling, with a number of sampling selection techniques contained within the two categories.
Determining Sample Size
Data are collected in a standardized form
Draw a sample
- Staff resources
Two types of sample
- Nonrandom: statistical validity not a concern
surveyors tend to pick someone like themselves
convenience surveys (e.g.., in supermarkets, at tourist sites)
Unbiased, since everyone has equal chance of being selected
Sample can only be as good as the list from which it was drawn
Types of random surveys:
- simple random - select random number to start, every nth thereafter
- stratified sampling - divide population into subpopulations, then every nth
- cluster sampling - survey all units in a stratified area
Sampling frame - the list you draw your sample from
Generally, random sampling is employed when quantitative methods are used to collect data (e.g. questionnaires).
Random sampling allows the results to be generalized to the larger population and statistical analysis performed if appropriate.
The most stringent technique is simple random sampling.
Non-random sampling is commonly applied when qualitative methods (e.g. focus groups and interviews) are used to collect data, and is typically used for exploratory work.
Non-random sampling deliberately targets individuals within a population.
There are three main techniques.
(1) Purposive sampling: a specific population is identified and only its members are included in the survey; using our example above, the hospital may decide to survey only patients who had an appendectomy.
(2) Convenience sampling: the sample is made up of the individuals who are the easiest to recruit. Finally,
(3) Snowballing: the sample is identified as the survey progresses; as one individual is surveyed he or she is invited to recommend others to be surveyed.
Selecting Target Population
Before you can be able to have a sample for your survey, you need to define your target population first. If your survey goal is to know the effectiveness of a product or service, then the target population should be the customers who have utilized it. It is critical to select the most appropriate target population in order to satisfy the purpose of executing the survey.
Types of Probability Sampling
Simple Random Sampling
Simple random sampling is the easiest form of probability sampling. All the researcher needs to do is assure that all the members of the population are included in the list and then randomly select the desired number of subjects.
The purest form of sampling under the probability approach, random sampling provides equal chances of being picked for each member of the target population.
Simple Random Sampling
- Simple random sampling is the most basic form of sampling
- Every member of the population has an equal chance of being selected
- This sampling process is similar to a lottery: the entire population of interest could be selected for the survey, but only a few are chosen at random
- Researchers often use random-digit dialing to perform simple random sampling. In this procedure, telephone numbers are generated by a computer at random and called to identify individuals to participate in the survey
Generally, random sampling is employed when quantitative methods are used to collect data (e.g. questionnaires). Random sampling allows the results to be generalized to the larger population and statistical analysis performed if appropriate.
- Stratified samples are used when a researcher wants to ensure that there are enough respondents with certain characteristics in the sample
Stratified random sampling is also known as proportional random sampling.
Systematic Random Sampling
In systematic sampling, every Nth name is selected from the list of the members of the target population. For instance, the sample will include the participants listed in every 10th from the list. That means the 10th, 20th, 30th and so on will be selected to become the members of the sample group.
Cluster Random Sampling
Cluster random sampling is done when simple random sampling is almost impossible because of the size of the population. Cluster sampling is generally used when it is geographically impossible to undertake a simple random sample
Mixed/Multi-Stage Random Sampling
This probability sampling technique involves a combination of two or more sampling techniques enumerated above. In most of the complex researches done in the field or in the lab, it is not suited to use just a single type of probability sampling.
- Common nonrandom sampling techniques include convenience sampling and snowball sampling
- Nonrandom samples cannot be generalized to the population of interest. Consequently, it is problematic to make inferences about the population
- In survey research, random, cluster, or stratified samples are preferable
Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.
In contrast with probability sampling, non-probability sample is not a product of a randomized selection processes. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher.
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. .
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.
Types of Non-Probability Sampling
Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time consuming.
Accidental, Haphazard or Convenience Sampling
Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher.
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).
- 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.
- 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." .
- Quota Sampling
Another non-probability method, quota sampling also identifies strata like stratified sampling, but it also uses a convenience sampling approach as the researcher will be the one to choose the necessary number of participants per stratum.
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.
Quota sampling is a non-probability sampling technique wherein the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota.
Quota sampling is a non-probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.
Step-by-step Quota Sampling
- The first step in non-probabilityquota sampling is to divide the population into exclusive subgroups.
- Then, the researcher must identify the proportions of these subgroups in the population; this same proportion will be applied in the sampling process.
- Finally, the researcher selects subjects from the various subgroups while taking into consideration the proportions noted in the previous step.
- The final step ensures that the sample is representative of the entire population. It also allows the researcher to study traits and characteristics that are noted for each subgroup.
- Purposive Sampling
As the name suggests, purposive sampling means the researcher selects participants according to the criteria he has set. This is only used when you are confident enough about the representativeness of the participant regarding the whole target population.
- 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.
- 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.
Chain Referral Sampling
Snowball sampling is a non-probability sampling technique that is used by researchers to identify potential subjects in studies where subjects are hard to locate.
Advantages of Snowball Sampling
Consecutive sampling is very similar to convenience sampling except that it seeks to include ALL accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that makes the sample a better representation of the entire population.
Sequential Sampling Method
Advantages of Sequential Sampling
Judgmental sampling is more commonly known as purposive sampling. In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind.
Judgmental sampling is a non-probability sampling technique where the researcher selects units to be sampled based on their knowledge and professional judgment.
Example of Judgmental Sampling
Determining Sample Size
The adequacy of a sample is determined according to how widely and diversely investigators select their subjects for saturating categories of an emerging substantive and a formal theory. In addition, a comparative analysis is conducted of differential levels of involvement in the same substantive activity.