Sociology

 Sampling

In research, sampling refers to the process of selecting a subset of individuals or data points from a larger population to study or analyze. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population of interest. According to J. Simond, a “Sample is a collection of observation for which one has data to work with.”

Sampling is an important step in research because it allows researchers to generalize findings from the sample to the larger population. For example, if a researcher wants to study the prevalence of a disease in a particular country, it would be impractical to survey every single person in that country. Instead, the researcher could select a sample of individuals that is representative of the population and study them to estimate the prevalence of the disease in the population as a whole.

The quality of research findings depends on the quality of the sampling method used. There are many different sampling methods available, each with its own strengths and weaknesses. The choice of sampling method depends on the research question, the type of population being studied, and the available resources. Some common sampling methods used in research include simple random sampling, stratified sampling, cluster sampling, and convenience sampling.

Types of Sampling

Different types of sample designs are based on a couple of factors, such as the representation basis and elements selection technique. As regards the representation basis, the classification may be between probability and non-probability sampling. Probability sampling is based on the finite universe and is conducive to random selection. On the other hand, non-probability sampling is non-random sampling, because it is related to the infinite universe. As regards the other typology based on the element selection techniques, the sample may either be restricted or unrestricted. Different sample designs discussed above are shown in the chart given below.

Probability Sampling

There are several types of probability sampling in research. Here are some of the most common ones:

1. Simple random sampling: 

In this type of sampling, every member of the population has an equal chance of being selected. This is usually done by assigning each member a number and then using a random number generator to select the sample.

2. Stratified sampling: 

This type of sampling divides the population into subgroups or strata based on some characteristic (e.g., age, gender, income level). The researcher then selects a sample from each stratum in proportion to the size of the stratum in the population.

3. Systematic sampling: 

In this type of sampling, the researcher selects a starting point at random and then selects every nth member of the population to be included in the sample. For example, if the population is 1,000 and the sample size is 100, the researcher would select every 10th member of the population.

4. Cluster sampling: 

In this type of sampling, the researcher selects groups or clusters of individuals from the population and then selects a sample from within each cluster. This method is often used when the population is large and spread out geographically.

5. Multistage sampling: 

This type of sampling combines two or more sampling methods. For example, a researcher might use stratified sampling to select clusters of individuals and then use simple random sampling to select individuals within each cluster.

6. Multi-phase sampling: 

The process in this type of sampling is the same as in multi-stage sampling, i.e., primary selection, secondary selection, and so on. Multi-phase sampling is a type of sampling technique that involves using two or more sampling methods in a sequence. It is also known as multi-stage sampling or sequential sampling. While in multi-stage sampling, only the final sample is studied, in multi-phase sampling, all samples are researched.

All of these methods are designed to provide a representative sample of the population being studied, which can help to increase the accuracy and generalizability of the research findings.

Non-probability Sampling:

Non-probability samplings are those which do not employ the rules of probability theory, do not claim representativeness and are usually used for qualitative exploratory analysis. It is also used in those situations where there is no list of persons to be studied.

1. Convenience (or accidental) sampling: 

In this sampling, the researcher studies all those persons who are most conveniently available or who accidentally come in his contact during a certain period of time in the research.

For example, the researcher engaged in the study of university students might visit the university canteen, library, some departments, play-grounds, verandahs and interview a certain number of students.

In such sampling, representativeness is not significant; therefore, it is quick and economical. Convenience samples are best utilized for exploratory research when additional research will subsequently be conducted with a probability sample.

2. Purposive (or judgmental) sampling: 

The researcher purposely chooses persons who, in his judgment about some appropriate characteristic required of the sample members, are thought to be relevant to the research topic and are easily available to him.

For example, the manufacturers (cosmetics, oils, garments, etc.) select test market cities because they are viewed as typical cities with demographic profiles closely matching the national profile.

Thus, in this technique, some variables are given importance and it represents the universe but the selection of units is deliberate and based on prior judgement.

3. Quota sampling: 

This is a version of stratified sampling with the difference that instead of dividing the population into strata and randomly choosing the respondents, it works on ‘quotas’ fixed by the researcher.

For example, if there are 100 men and 100 women in a population and a sample of 20 are to be drawn to participate in a cola taste challenge, you may want to divide the sample evenly between the sexes—10 men and 10 women.

4. Snowball sampling: 

The researcher uses this technique to begin the research with the few respondents who are known and available to him. Subsequently, these respondents give other names who meet the criteria of research, who in turn give more new names. This process is continued until an ‘adequate’ number of persons is interviewed or until no more respondents are discovered.

For example, in studying wife battering, the researcher may first interview those cases whom he knows, who may, later on, give additional names, and who in turn may give still new names.

This method is employed when the target population is unknown or when it is difficult to approach the respondent in any other way.

5. Volunteer sampling: 

This is the technique in which the respondent himself volunteers to give the information he holds. Often, these have a strong interest in the main topic of the survey.

For example, in a news show, if a viewer asks to participate in an online poll. This would be a voluntary sample. The sample is chosen by the viewers, not by the survey administrator.

Non-probability sampling methods are helpful in some situations where probability sampling methods are not practical or feasible. However, they are generally considered less reliable and less generalizable than probability sampling methods because they do not provide equal chances of selection for every member of the population. It is important for researchers to carefully consider the advantages and disadvantages of different sampling methods before selecting the appropriate one for their study.

Significance of sampling in social research

Sampling is a critical aspect of social research because it allows researchers to study a subset of the population in order to make inferences about the larger population. Here are some of the key reasons why sampling is significant in social research:

1. Cost-effectiveness: 

Conducting research on an entire population can be prohibitively expensive, time-consuming, and sometimes impossible. Sampling allows researchers to study a smaller subset of the population that is still representative of the larger population, which can be more cost-effective and practical.

2. Generalizability: 

The goal of social research is often to make generalizations about a population based on the findings from a sample. Sampling techniques can help ensure that the sample is representative of the population, which can increase the generalizability of the findings.

3. Precision: 

Sampling techniques can help ensure that the sample is not biased, which can increase the precision of the findings. For example, if a sample is selected using a random sampling technique, it is less likely to be biased towards any particular group or characteristic in the population.

4. Ethical considerations: 

In some cases, it may be unethical or impractical to study an entire population. Sampling can help researchers avoid putting the entire population at risk or causing undue harm.

5. Feasibility: 

In some cases, it may simply be impossible to study the entire population, such as in cases where the population is very large, geographically dispersed, or otherwise difficult to access. Sampling can make it feasible to study the population by focusing on a smaller, more manageable subset.

6. Reduce errors: 

Sampling can help reduce errors and increase the accuracy of research findings. For example, if a researcher studies the entire population, they may encounter errors such as measurement error, response bias, or sampling bias. By using a sample, researchers can minimize these errors and increase the precision of their findings.

7. Variety of methods: 

There are various sampling techniques available to researchers, and different methods may be appropriate depending on the research question, population size, and other factors. By carefully selecting the appropriate sampling method, researchers can obtain a sample that is representative of the population and minimize biases.

8. Accessibility: 

Sampling allows researchers to study populations that may be difficult to access or rare. For example, researchers may be interested in studying a particular group of people with a rare disease or a group that is geographically dispersed. By using a sampling technique, researchers can access these populations and study them more easily.

9. Time-efficiency: 

Sampling can save time in social research by allowing researchers to obtain a smaller sample size that can be studied more easily and quickly than the entire population.

10. Replicability: 

Sampling makes it possible for other researchers to replicate studies and test the validity of findings. If the sampling method is described in detail, other researchers can use the same method to obtain a similar sample and test the validity of the original findings. This increases the reliability and credibility of social research.

Thus, sampling is significant in social research because it allows researchers to obtain accurate, representative, and generalizable findings while minimizing costs, time, and ethical concerns.

How is sample size determined

Determining the appropriate sample size is a critical step in any research study. The sample size is the number of participants or cases that are included in the study. Here are some of the factors that researchers consider when determining the sample size:

1. Research question: 

The research question will dictate the type of statistical analysis that will be used to analyze the data. The type of analysis will then determine the minimum sample size required to obtain statistically significant results.

2. Population size: 

The size of the population being studied can affect the sample size. Larger populations generally require larger sample sizes to ensure representativeness.

3. Desired level of precision: 

The level of precision desired in the research findings will influence the sample size. For example, if a high level of precision is required, a larger sample size may be needed.

4. Variability of the population: 

The variability of the population being studied can affect the sample size. If the population is highly variable, a larger sample size may be required to ensure representativeness.

5. Type of sampling: 

The type of sampling method being used can affect the sample size. For example, if a simple random sample is being used, a larger sample size may be required to ensure representativeness.

6. Statistical power: 

The statistical power of the study can affect the sample size. A higher level of statistical power requires a larger sample size to detect smaller effects.

7. Budget and time constraints: 

Budget and time constraints can also affect the sample size. Larger sample sizes may be more expensive and time-consuming to obtain.

Researchers typically use statistical calculations to determine the appropriate sample size based on these factors. The most common statistical methods used for determining sample size are power analysis and sample size determination using confidence intervals. These methods take into account the factors listed above to determine the minimum sample size required to achieve the desired level of precision and statistical power for the research question being investigated.

Differentiate between probability and non-probability sampling

Probability sampling and non-probability sampling are two different types of sampling techniques used in research studies. Here’s how they differ:

Probability sampling:

1. Probability sampling is a sampling technique in which every member of the population has an equal chance of being selected for the sample.

2. Probability sampling methods include simple random sampling, stratified random sampling, systematic sampling, and cluster sampling.

3. Probability sampling allows researchers to estimate the level of sampling error and the precision of the findings.

4. Probability sampling is often used in quantitative research studies because it allows for statistical analysis and generalization of the findings to the larger population.

Non-probability sampling:

1. Non-probability sampling is a sampling technique in which the probability of a member of the population being selected for the sample is not known or is unequal.

2. Non-probability sampling methods include convenience sampling, purposive sampling, snowball sampling, and quota sampling.

3. Non-probability sampling does not allow for the estimation of sampling error or precision of the findings.

4. Non-probability sampling is often used in qualitative research studies because it allows for the selection of participants based on specific characteristics or criteria.

Here are some additional differences between probability and non-probability sampling:

1. Probability sampling is considered more representative of the population, while non-probability sampling is often considered less representative.

2. Probability sampling requires a sampling frame, or a list of all members of the population, while non-probability sampling may not require a sampling frame.

3. Probability sampling often requires a larger sample size than non-probability sampling to achieve a similar level of precision and statistical power.

4. Probability sampling is often more time-consuming and expensive than non-probability sampling.