Sampling Method

• Population- Set of all possible values of a variable
• Sample- consists of one or more data drawn from the population
• Random Sampling- sampling method of choosing a representative from the population wherein every sample has an equal chance of being selected.
• Probability Sampling- random selection
• Non-Probability Sampling- Do not involve random selection
• SAMPLING METHODS

  Probability Sampling, Non-Probability Sampling
• Probability Sampling

  Simple Random Sampling, Systematic Sampling, Cluster Sampling Stratifies Sampling
• Non-Probability Sampling

  Snowball Sampling, Quota Sampling, Convenience Sampling, Volunteer Sampling
• Cluster Random Sampling- this is used when a population is huge or spread out over a large area.
• Convenience- The researcher gathers data from nearby sources of information exerting minimal effort. It is used by persons giving questionnaires on the streets to ask passersby.
• Snowball- the samples have traits that are rare to fine. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.
• Qouta Sampling- Sample units are placed for convenience but certain qoutas are given to interviews. This design is spezially used in maker research (traits/qualities)
• Volunteer Sampling- Sample units are volunteers in studies wherein the measuring process is painful or troublesome to a respondent.
• PARAMETER- Measurement or quantity that describes the population
• Statistics - Measurement of quantity that describes the sample.
• Stratifies Random Sampling- The population is divided into smaller groups, or "STRATA" that share a characteristics people from each smaller group are randomly selected to form the sample
• Systematic Random Sampling- This can be done by listing all the elements in the population and selecting KTH element in your population list.
• Simple Random Sampling- Most basic random sampling wherein each element in the population has an equal probability of being selected.