3+ Sampling techniques Assignments Help
Sampling techniques are the procedures of drawing a sample from a population. It is difficult to collect data from the whole population. Therefore, a representation of the population which is known as the sample is selected. A group of people who actually participate in a research are known as a sample. For the results obtained in the study to be accurate, the sample should be precise and population representative. There are two types of sampling techniques; probability and non-probability sampling. Are you looking for sampling techniques assignment help? Worry no more! We got you covered!
Sampling techniques: Population and sample
One needs to understand the difference between the population and sample. One should identify the target population the research. A population is a collection of people or entities where conclusions are drawn. Sample is portion f the population where data is collected. The population should be well defined in terms of geographical location, age, gender, ethnic group, among others. For example, a research done for African Americans stay at home mothers in California. The population will be all mothers in California while the sample will be African American stay at home mothers.
The type of research determines the population to be studied. Research can focus on other populations such as customers in a market, patients with a specific health condition, high school students or even an entire country.
Sampling techniques: Sampling Frame
This is the actual sample space from which a sample will be drawn. It should include the entire target population. For example, a research on learning environment at School Y. The population contains 2000 students. The sampling frame will be the school’s database which contains the list of names and details for every student.
Sampling techniques: Sample size
It involves the number of individuals who should be contained in a sample. There are various ways of coming up with the sample size. There is use of sample size calculators and mathematical formulas depending on what analysis you intend to achieve. Factors such as size of the population and research design, also influence the sample size
Sampling techniques: Probability sampling techniques
This is whereby every member in the sample has a chance of being selected. It is a more accurate choice in choosing a sample since it eliminates selection bias. Selection bias is whereby some entities have a higher chance of being selected from the population than others.
It is mostly used in quantitative research. Also, the sample obtained will be a representative of the whole population. There are four types of probability sampling which include simple random sampling, systematic sampling, stratified sampling and cluster sampling
Simple random sampling
This is whereby every member in the population has an equal chance of being selected. Each member is drawn randomly without any preference or bias. It can be done using a computer algorithm for selecting the members randomly. First, all the members are assigned random numbers, then the computer algorithm can do a random selection. If the population is not large, the sample can be hand-picked randomly.
It is similar to simple random sampling. There is only a slight difference. The first member of the sample is randomly selected. Then the other members of the sample are selected using a predetermined formula. Therefore, the members are chosen with regular intervals instead of being selected randomly.
For example, all students are listed in an alphabetical order. From the first 10 numbers, one number is selected randomly: number 4. From number 4 onwards, every 10th person is selected: (4,14,24, 34,..) until you obtain a sample of 50 members.
It is important that you identify there is no hidden pattern on the data in the database. For example, in an employee’s database and the list is according to seniority, there is a chance that some members of certain seniority might be skipped.
It is dividing the population into strata. Strata are groups of individuals who have common characteristics such as age, gender, job level, race, among others. It allows the researcher to draw a precise conclusion since every member of the subgroup is represented. After the division of the population into strata, either simple random sampling or systematic sampling is done to obtain the sample.
In a student population, students have different characteristics according to age, income and ethnicity. For example, from a population of 1000 students, there are 600 females and 400 males. 500 students from high income generating families, 300 from medium income generating families and 200 from low income generating families. There are 700 students who are white, 200 are African Americans, 100 are Latins and Asians. The sample is selected from each stratum.
It involves dividing the population int subgroups known as clusters. The clusters have similar characteristics to the entire sample. Instead of sampling individuals from each group, they are randomly selected from the entire subgroups.
When the clusters are large, multistage sampling can be conducted. This is whereby a sample is drawn form the large cluster, then sample is obtained from that sample. The technique is more suitable when the population is large and widely dispersed. It is difficult to guarantee that sampled clusters are representative of entire population.
For example, a bank has 10 branches in numerous cities in the country. For cluster sampling, only 4 can be picked where sampling will be done. These 4 are the clusters. Would you like help with such or similar assignments? Click order button at our platform.
Sampling techniques: Non probability sampling technique
This is whereby not every individual has a chance of being selected to be a sample. Some of the individuals have no chance of being selected. It is cheaper but it is biased. The inferences drawn from these sampling is less accurate compared to probability sampling technique. They are often used in qualitative research. They include; convenience sample, purpose sampling, voluntary response sampling and snowball sampling.
It is mainly based on convenience as the name suggests. It is whereby the researcher selects the sample depending on the available members. The method is cheap and easy since you only have to collect data from the convenient members. The sample may not be a representative of the population.
For example, a student conducts a research on learning environment. They decide to give a survey on fellow students after class. This is a type of convenient sampling since only the conveniently available members have been selected to be sample of the students in the school.
Voluntary response sampling
It is mainly based on ease of access. Individuals from the population volunteer themselves to be in the sample instead of being selected or chosen directly by the researcher. The method is also biased since some individuals are more likely to volunteer to a study than others. They volunteer themselves through responding to a public online survey.
For example, sending out a survey to all the students at the university and a lot of students choose to complete the survey. It is highly likely that those who responded to the survey had strong opinions regarding the student support services. Also, it is also highly likely that those who have a difference in opinion did not bother to fill in the survey. It also does not mean that the ones who filled the survey are representative of the entire population.
It is also referred to as judgement sampling. The researcher uses their expertise to select the sample that will serve the purpose if the research. It is commonly used in qualitative research whereby the researcher seeks to obtained detailed knowledge on a specific phenomenon.
It Is can also be used where the population is small and specific. An effective purposive sample should have a clear criteria and rationale for inclusion. For example, the researcher wants to know opinions of disabled students at the university. A number of students with different support needs will be selected so as to give their experiences with students’ services.
It is also referred to as chain referral sampling. It is whereby the existing study subjects recruit future subjects from their acquaintances. T is mostly done where the subjects under research are hard to obtain and find. The sample size of the snowballs gets bigger and bigger due to additional recruits of subjects.
For example, a researcher is conducting a research on experiences of homelessness in the city. There are no lists or pre existing database on homeless people, probability sampling will be difficult. When the researcher meets one person, they identify another and that other under identifies another and it becomes a chain referral procedure.
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