Different sampling methods
There are a number of sampling methods and terminology to understand when collecting data these are as follows:
Random sampling – in this scenario, data is chosen as random (e.g. picking names from a hat). Therefore all members of a population has an equal chance of being chosen.
Pros for random sampling: every member of the population has an equal chance of being chosen.
Con for random sampling: can be very time consuming and usually impractical.
Systematic sampling – in this scenario, members of a population will be chosen at regular intervals (e.g. choosing every 20th person in a list).
Pro for systematic sampling: unlikely to get a biased sample
Con for systematic sampling: not 100% ramdom therefore certain members of a population cannot be selected once a decision has been made on where to start on a given list.
Quota sampling – in this scenario, a certain number of members of a population is sampled from each category (e.g. when taking a survey, the survey from an a person who is under the age of 21 isn’t asked as the quota for that sample has been met.)
Pro for quota sampling: easy to manage
Con for quota sampling: can be biased
Stratified sampling – Strata are values a variable can take (note, variable are also called relevant factors). For example: the variable “gender” can take two values, male and female.
Pro for stratified sampling: ideal method to reflect population accurately
Con for stratified sampling: time consuming and number of relevant variables limited to make it practical.