Simone Lawson
When conducting research on a group of people, it is often impractical to collect data from every individual in the group. Instead, researchers select a smaller subset of participants, known as a sample group, to represent the larger population. The process of selecting a sample group is called sampling, and it is a crucial aspect of research methodology. Sampling methods can vary, but the goal is typically to obtain a representative sample that accurately reflects the characteristics of the larger group. This could involve matching demographic categories such as sex, age, socioeconomic status, and more. The size of the sample group is an important consideration, as it can impact the validity and generalizability of the research findings. While there are established guidelines for determining sample sizes in quantitative research, qualitative research often involves more flexibility and uncertainty in sample size determination. Ultimately, the choice of sampling method and sample size depends on the specific research goals and the nature of the population being studied.
| Characteristics | Values |
|---|---|
| Sample size | 30 |
| Sampling method | Representative sampling, random sampling, systematic sampling, voluntary response sampling, quota sampling, cluster sampling, stratified sampling |
| Population | Depends on the study, can be a classroom, a country, or a warehouse |
| Sample group | Depends on the study, can be students, homeless people, consumers, or warehouse workers |
| Selection criteria | Random selection, non-random selection, self-selection, stratified random sampling, systematic random sampling |
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What You'll Learn
Representative sampling
Representative samples are widely used to collect data across different kinds of research as they are easy to conduct and cost-effective. If done properly, the results are just as accurate as a large-scale survey. Creating a representative sample is straightforward, but one must consider the size of the population or group being studied and how this will determine the size of the sample group to accurately reflect the views of the larger group. For instance, if studying the impact of the global financial crisis on middle and low-income families, determining the socioeconomic status of the sample can ensure the exclusion of high-income families, thereby creating an accurate and representative sample.
To create a representative sample, one must first define the population size, then determine the sample size, and finally, establish the characteristics of the sample. Stratified random sampling is often used in this process, where the population is divided into strata or subsets based on characteristics such as age, gender, income, ethnic markers, or geographical locations. This allows for the selection of individuals from each stratum or subset to create a representative sample. While this method is more time-consuming and costly, it yields higher-quality information. Representative sampling is crucial for obtaining accurate results, especially in marketing or psychology studies, as it collects results, insights, and observations that can be confidently relied on as a representation of the larger population.
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Random sampling
To ensure the validity of the findings, researchers must make sure that every individual selected participates in the study. A high response rate supports the validity of the results. Additionally, with a large enough sample size, a simple random sample can have high external validity, meaning it accurately represents the characteristics of the larger population.
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Systematic sampling
When deciding on a sampling method, researchers must consider various factors. Representative samples, which closely align with the entire population group, are usually ideal. However, they can be challenging to obtain, especially with large populations. Random sampling is a simpler alternative but comes with a higher risk of sampling error, which can lead to incorrect results. Systematic sampling is preferable to simple random sampling when there is a low risk of data manipulation.
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Probability sampling
At its core, probability sampling guarantees that every member of a target population has a known and non-zero chance of being selected to participate in the research. This reduces sampling bias and ensures that the sample closely mirrors the larger population. The larger the sample size, the more precise the results. This fidelity allows for strong statistical inferences, enabling researchers to make predictions and conclusions about the population with a high degree of confidence.
There are several different ways in which a probability sample can be selected. One commonly used method is simple random sampling (SRS), where each sampling unit of a population has an equal chance of being included in the sample. For example, if a researcher wanted to survey 30 students in a classroom with 15 males and 15 females, they could use SRS to select six students: three males and three females. This would ensure that the sample is representative of the larger group.
While probability sampling offers many advantages, it also has some challenges. One significant hurdle is the need for a complete list of the population, which can be difficult or impossible to obtain for large or dispersed populations. Additionally, probability sampling can be more complex, time-consuming, and costly compared to non-probability sampling methods. It is important to carefully consider the research objectives and practical constraints when deciding on a sampling method.
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Non-probability sampling
However, non-probability sampling comes with challenges. Since elements are chosen arbitrarily, there is no way to estimate the probability of any one element being included in the sample. This makes it difficult to assess whether the sample is representative of the population. As a result, non-probability sampling is more susceptible to biases, particularly sampling bias. This is because preconceptions of the researcher can be reflected in the sample, introducing large biases if these preconceptions are inaccurate.
There are several types of non-probability sampling methods. Quota sampling involves selecting a predetermined number or proportion of units, called a quota. The quota is based on subgroups with specific characteristics, such as individuals, cases, or organizations, and they are selected in a non-random manner. Proportional quota sampling is used when the population size is known, while non-proportional quota sampling is used when the size is unknown. Haphazard sampling assumes that all population units are alike, so any unit may be chosen for the sample. Judgment sampling involves selecting participants based on desirable characteristics that fulfil certain requirements.
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Frequently asked questions
A sample group is a subset of individuals from a larger population. Sampling is used in statistical analysis to gain insights and observations about a population group.
A representative sample is a sample group that accurately reflects the characteristics of a larger population. It is a technique used to obtain insights and observations about a targeted population group.
There are various sampling methods to select a representative sample. Probability sampling involves random selection, allowing for strong statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, making data collection easier.
In a group of 50 people, each person gets a number, and a random starting point is chosen—for example, 7. Then, every 4th person is selected: 7, 11, 15, 19, and so on. This is an example of systematic random sampling.
Representative samples are useful when dealing with large populations as they are smaller, manageable versions of the larger group. They can provide insights that closely align with the entire population group.
