Sampling is the process of selecting a representative group from the population under study

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part

• Volunteer sample: where participants pick themselves through newspaper adverts, noticeboards or online

• Opportunity sampling: also known as convenience sampling, uses people who are available at the time the study is carried out and willing to take part. It is based on convenience

• Random sampling: when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat

• Systematic sampling: when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample

• Stratified sampling: when you identify the subgroups and select participants in proportion to their occurrences

• Snowball sampling: when researchers find a few participants, and then ask them to find participants themselves and so on

• Quota sampling: when researchers will be told to ensure the sample fits certain quotas, for example, they might be told to find 90 participants, with 30 of them being unemployed

Simple Random sampling:

• To carry out a simple random sample, you need a sampling frame, usually a list of people or things.

• Each person or thing is allocated a unique number and a selection of these numbers is chosen at random.

  • There are two methods of choosing the numbers: generating random numbers (using a calculator, computer or random number table) and lottery sampling.

  • In lottery sampling, the members of the sampling frame could be written on tickets and placed into a 'hat'. The required number of tickets would then be drawn out.

Simple Random sampling:

• Advantages:

  • Free of bias

  • Easy and cheap to implement for small populations and small samples

  • Each sampling unit has a known and equal chance of selection

• Disadvantages:

  • Not suitable when the population size or the sample size is large

  • A sampling frame is needed

Systematic sampling:

• Advantages:

  • Simple and quick to use

  • Suitable for large samples and large populations

• Disadvantages:

  • A sampling frame is needed

  • It can introduce bias if the sampling frame is not random

Stratified sampling:

• Advantages:

  • The sample accurately reflects the population structure

  • Guarantees proportional representation of groups within a population

• Disadvantages:

  • Population must be clearly classified into distinct strata

  • Selection within each stratum suffers from the same disadvantages as simple random sampling

Non-Random sampling:

• There are two types of non-random sampling that you need to know:

  • Quota sampling

  • Opportunity sampling

• In quota sampling, an interviewer or researcher selects a sample that reflects the characteristics of the whole population.

• The population is divided into groups according to a given characteristic. The size of each group determines the proportion of the sample that should have that characteristic.

• As an interviewer, you would meet people, assess their group and then, after the interview, allocate them to the appropriate quota.

• This continues until all quotas have been filled. If a person refuses to be interviewed or the quota into which they fit is full, then you simply ignore them and move on to the next person.

• Opportunity sampling consists of taking the sample from people who are available at the time the study is carried out and who fit the criteria you are looking for.

• This could be the first 20 people you meet outside a supermarket on a Monday morning who are carrying shopping bags, for example.

• There are advantages and disadvantages of each type of sampling.

Quota sampling:

• Advantages:

  • Allows a small sample to still be representative of the population

    • No sampling frame required

    • Quick, easy and inexpensive

    • Allows for easy comparison between different groups within a population

• Disadvantages:

  • Non-random sampling can introduce bias

  • Population must be divided into groups, which can be costly or inaccurate

  • Increasing the scope of the study increases the number of groups, which adds time and expense

  • Non-responses are not recorded as such

Opportunity sampling:

• Advantages:

  • Easy to carry out

  • Inexpensive

• Disadvantages:

  • Unlikely to provide a representative sample

  • Highly dependent on individual researcher

Systematic sampling:

• The required elements are chosen at regular intervals from an ordered list.

  • For example, if a sample of size 20 was required from a population of 100, you would take every fifth person since 100 ÷ 20 = 5.

• The first person to be chosen should be chosen at random. So, for example, if the first person chosen is number 2 in the list, the remaining sample would be persons 7, 12, 17 etc.

Stratified sampling:

• The population is divided into mutually exclusive strata (males and females, for example) and a random sample is taken from each.

• The proportion of each strata sampled should be the same. A simple formula can be used to calculate the number of people we should sample from each stratum:

  • The number sampled in a stratum = number in stratum/number in population × overall sample size

1. a. 40+60+80=180

   20% of 180= 36

   36/3 =12

   b.Guarantees proportional representation of groups within a population

2. a. It can introduce bias

   b. Stratified sampling because the population must be clearly classified into distinct strata