Research studies are distinct events that involve a particular group of participants. However, researchers usually intend on answering a general question about a larger population of individuals rather than a small select group. Therefore, the main aim of psychological research is to be able to make valid generalisations and extend their results beyond those who participate. For this reason, the selection of participants is a very crucial issue when planning research. Obviously, researchers cannot collect data from every single individual from their population of interest, since this would be extremely expensive and take a **very** long time! So instead they use a small group of individuals – called a sample. The sample is chosen from the population and is used to represent the population. Researchers use sampling techniques to select the participants for their sample – these techniques help to minimise cost whilst maximising generalisability. So, in this weeks blog I am going to be discussing the different sampling techniques and methods, and considering the issue of sampling bias and the problems associated in research.

There are a variety of different sampling methods available to researchers to select individuals for a study. Sampling method fall into two categories:

**Probability sampling:**Every individual in the population is known and each has a certain probability of being selected. A random process decides the sample based on each individual’s probability.**Nonprobability sampling:**The population is not entirely known, thus individual probabilities cannot be known. Common sense or ease is used to choose the sample, but efforts are made to avoid bias and keep the sample representative.

Simple random is an example of probability sampling. This is when a list containing all of the population is created and used to obtain participants by random selection. This random selection guarantees that each individual has an independent and equal chance of being selected. This method is very fair, unbiased and easy to carry out. However, with simple random sampling there is no assurance of complete representativeness of the sample. Another example of simple random sampling is cluster sampling. This is when the sample is gained by the random selection of clusters (pre-existing groups of individuals) from a list containing all of the clusters existing within a population. Cluster sampling is often used to estimate number of mortalities in events such as war and natural disaster¹. This method is easy for obtaining a large and relatively random selection of participants, however, the selections lack independence.

Convenience sampling is a method of nonprobability sampling. With convenience sampling, the sample is made up of individual participants who are easy to get. For example, Milgram (1963) used convenience sampling in his famous study ². The participants were individuals who had volunteered by responding to a newspaper article. Convenience sampling is easy to carry out, but one large disadvantage is that the sample is likely to be biased. Milgram’s participants were all male – which could be agued to be a biased sample. Finally, quota sampling is another method of nonprobability sampling. This is when different subgroups are identified and participants are selected through convenience from each different subgroup. For example, say a researcher wanted to select a sample of students to participate in a study using a convenience sample but wanted to ensure that an equal number of boys and girls were selected – quota sampling would be the best method for them to use. This type of sampling can help to control a convenience sample but may results in a biased sample, which would not be a good representative of the wider population.

As I mentioned earlier, the goal of research is to study a sample of participants and then generalise the results to the larger population. How far we can extend such results to generalise to a population is dependant on how closely the sample resembles the population – the representativeness. The main threat to representativeness is **bias**. A biased sample is one which contains characteristics that are different from those of the population. This bias may happen by chance, but usually is down to selection bias. Selection bias is when participants are selected in a way that increases the probability of acquiring a biased sample. For example, if a researcher recruits participants from a gym, they are more likely to be healthier and fitter than the rest of the general public.

I can definitely say that the selection of participants is a very vital part of planning research. Without carefully planning and choosing an appropriate method for sampling it is very easy to obtain a biased sample that does not represent the population. When this happens, it is difficult to extend findings to a wider population and the validity of the experiment decreases. In order to produce influential and meaningful results, researchers must ensure that they have chosen an appropriate sampling method to select a representative sample of participants.

¹. David Brown, Study Claims Iraq’s ‘Excess’ Death Toll Has Reached 655,000, *Washington Post*, Wednesday, October 11, 2006

². Milgram, S. (1963). Behavioral study of obedience. *Journal of Abnormal and Social Psychology, *72, 207-217.

checking my comments work.

This is a very interesting blog.

I have written in one of my blogs about generalisation, and have never mentioned or considered sampling methods. Like you have mentioned convenience sampling is a cheap and easy way to recruit participants, and can lead to biases, which is something that I get really angry about, I know that it is impossible to be perfect on representing the entire population, but I just feel like some methods that are used shut off a large amount of people.

It is unlikely that research will ever represent the whole population, but it is good that there are a number of methods so that representation can be increased, and at the same time reduce biases.

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thank you! this will help in my research.

I have a question related to the sampling techniques described here. in my recent study, I have collected the data from 100 faculty members in a public sector university who have undertaken program evaluation through self Assessment to study the effectiveness of the model. out of 100 total 93 members responded which I used in my study. if this is a convenience sampling, was it the right approach in choosing this sampling? and what is the difference between purposive sampling and convenience sampling??

I will be grateful if you respond my queries please.

Hi, suraiya khatoon, this was every intresting type of convenience sampling. The turn up was good hence less bias.i think representative was okay.purposive depends on aim of researcher, while convenience depends on pertispants.

I got what you intend, thankyou for putting up.Woh I am lucky to find this website through google. Being intelligent is not a felony, but most societies evaluate it as at least a misdemeanor. by Lazarus Long. fddefdcdkeke

hi,i have got a question which states that outline the basis of sampling techniques….I want to know what is the basis?

Hai I am Dr.Remya,I was searching sampling techniques as a part of my study,i found your blog useful,easily comprehensible.Thank you

Reblogged this on innocenttauzen.

Probability Sampling

In probability sampling it is possible to both determine which

sampling units belong to which sample and the probability that

each sample will be selected. The following sampling

methods, which are listed in Chapter 4 , are types of

probability sampling:

1. Simple Random Sampling (SRS)

2. Stratified Sampling

3. Cluster Sampling

4. Systematic Sampling

5. Multistage Sampling (in which some of the methods

above are combined in stages)

Of the five methods listed above, students have the most

trouble distinguishing between stratified sampling and cluster

sampling.

Stratified Sampling is possible when it makes sense to

partition the population into groups based on a factor that

may influence the variable that is being measured. These

groups are then called strata. An individual group is called a

stratum. With stratified sampling one should:

partition the population into groups (strata)

obtain a simple random sample from each group

(stratum)

collect data on each sampling unit that was randomly

sampled from each group (stratum)

Stratified sampling works best when a heterogeneous

population is split into fairly homogeneous groups. Under

these conditions, stratification generally produces more

precise estimates of the population percents than estimates

that would be found from a simple random sample.