Nonprobability sampling methods include convenience sampling , quota sampling , and purposive sampling — or judgment sampling, and snowball sampling. Random sampling, in its simplest and purest form, means that each member of the population has an equal and known chance at being selected.
In a large population, this becomes prohibitive for cost and technical reasons, so the actual pool of respondents becomes biased. This method is often preferable to simple random sampling, as you select members of the population systematically — that is, every Nth record. As long as there is no ordering of the list, the sampling method is just as good as random — only much simpler to manage.
The key is to ensure that the sample size is large enough to represent the population. One of the most cost-effective sampling methods, researchers choose this method as they can recruit the sample from the population that is close at hand, or convenient to them. It is up to the researcher to ensure that a large enough sample is chosen that can closely represent the population being studied.
An extension of this is judgment sampling, where the research selects a representative sample based on their judgment. There is another method of acquiring respondents called snowball sampling, where initial subjects refer others to take the survey. Survey bias can rear its ugly head many times during the creation of a survey.
From the population you choose unintentionally excluding key respondents, to ensuring you have a sample size that accurately reflects the total population. You can also create survey bias through the probability or non probability sampling method you select. This is called Sample Bias or Sampling Bias. Sample bias is when a sample is collected and, due to the method used, some members of the intended population have a lower probability of being included as others.
Ideally, it should include the entire target population and nobody who is not part of that population. You are doing research on working conditions at Company X. Your population is all employees of the company. 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. 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. 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.
You want to select a simple random sample of employees of Company X. You assign a number to every employee in the company database from 1 to , and use a random number generator to select numbers. 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.
All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected 6, 16, 26, 36, and so on , and you end up with a sample of people.
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.
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. 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. The company has female employees and male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of people.
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.
The company has offices in 10 cities across the country all with roughly the same number of employees in similar roles. Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing.
See editing example. 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. These counterparts are members of the second matched sample.
The variables that are commonly matched include gender, age, race, ethnicity, educational attainment, place of residence, political orientation, religion, job type, and wages or salary. Matching these variables helps to reduce sources of bias, although even careful matching may not result in samples free of bias. The possibility of bias from hidden sources always exists.
Purposive sampling is used when the research design calls for a sample of people who exhibit particular attributes. Generally, these attributes are rare or unusual and are typically not distributed normally that is, according to the "normal curve" in the larger population.
Purposive sampling is fraught with bias, some of which occurs as a result of the methods used to identify the members of a purposive sample. For example, if the research purpose requires studying veterans with traumatic brain injury TBI , then the sample must consist of ex-members of the military who have sustained a TBI and who identify themselves accordingly and agree to participate in the study.
Each of these attributes or conditions contributes a measure of bias to the sample, thereby limiting the level and type of conclusions that result from the study. Samples that act like public opinion polls are disseminated with the idea that they represent how members of a population will vote in a coming election, for example.
These samples must be highly representative of the population in order to make reliable forecasts. Actively scan device characteristics for identification. Use precise geolocation data. Select personalised content. Create a personalised content profile. Measure ad performance. Select basic ads. Create a personalised ads profile.
0コメント