Competency: Plans for Sampling Technique to identify criteria of the target respondents

Selecting the Sampling Technique

Sample selection is critical to the validity of the information that represents the populations that are being studied.

The sampling approach helps determine the focus of your study and allows you accept better the generalizations that are being made.

Careful use of biased sampling can be used if it is justified, and as long as it is noted that the resulting sample may not be a true representation of the population of the study. Non-Random Sampling Technique and Random Sampling Technique will be used.

Non-Random Sampling Technique

This method does not guarantee the chance that all the elements involved in the research will be included in the sample.

· You cannot calculate the probability that each element will be represented.

· This method only samples those who are available and willing to participate in the survey.

· The use of this approach gives you convenience, but a non-random sample may lose data validity due to a lack of representation.

§ Purposive or Criterion-based Non-Random Sampling – Your respondents are chosen based on pre-conceived qualifications, usually utilized for technical subjects such as those in the field of medical science, engineering, and information technology.

§ Networking or Snowballing Non-Random Sampling – Respondents are chosen based on referrals forming a network or a chain of respondents, usually utilized for sensitive or highly confidential subjects such as AIDS-HIV infection, graft and corruption, etc.

Random Sampling Technique

This method gives each element an equal chance of being included in the sample.

· This method is closer to a true representation of the population.

· It can be difficult to use due to the size of the sample and the cost to obtain, but the generalizations that come from it are more likely to be closer to the populations’ true representation.

§ Simple Random Sampling – Each element of the population has an equal chance of being included in the sample. (e.g., Lottery or Fish Bowl, where all the names of the members of the population are raffled off composing the sample size.

§ Stratified Random Sampling – The population is divided into subpopulations (called strata), and the random samples are then drawn from strata. This approach increases the representation of the population.