Sampling method

Sampling method

1. Probability sampling method

   • Simple random sampling
     • population has an equal probability of being in sample
     • Example: Jack select a simple random sample of 100 employees of Company X. He assigns a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.
   • Stratified sampling
     • population is divided into subgroups and from those subgroups, a random sample is taken from it
     • Example: The company has 800 female employees and 200 male employees. Jack want to ensure that the sample reflects the gender balance of the company, so Jack sorts the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives a representative sample of 100 people.
   • Systematic sampling
     • it is usually slightly easier to conduct.
     • Example: All employees of the company are listed in alphabetical order. From the first 10 numbers, Jack randomly selects a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and end up with a sample of 100 people.
   • Cluster sampling
     • the whole population is divided into cluster or group
     • Example: The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). Jack doesn’t have the capacity to travel to every office to collect your data, so Jack use random sampling to select 3 offices – these are the clusters.
   • Multi-stage sampling
     • following a step by step process, a broad sample is moved to a narrow sample
     • Example: The Australian Bureau of Statistics divides cities into “collection districts”, then blocks, then households. Each stage uses random sampling, creating a need to list specific households only after the final stage of sampling.

2. Non-probability method

   • Quota sampling
     • non-random sampling technique in which participants are chosen on the basis of predetermined characteristics
     • For example, if the population consists of 45% female and 55% male, the sample should reflect those percentages.
   • Convenience sampling
     • participants are often readily and easily available
     • Example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly.
   • Snowball sampling
     • it uses a few cases to help encourage other cases to take part in the study, thereby increasing sample size
     • Example: Jack are researching experiences of homelessness in his city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. He meet one person who agrees to participate in the research, and she puts he in contact with other homeless people that she knows in the area.