Sampling Techniques Overview

Sampling Techniques

• Defining the Target Population is crucial for research project success.
• Rely on logic and judgment when defining the target population.
• The population should be defined in relation to the study's objectives.

Technical Terminology

• An element is something measured in a study.
• A population is the group of elements for study.
• Sampling units are non-overlapping groups of elements that form the population.
• A sampling frame is a list of sampling units.
• A sample is units taken from a sampling frame.
• Parameter is a characteristic of a population.
• Statistic is a characteristic of a sample.

Errors of Non-observation

• Sampling error is the difference between an ideal sample estimate and the true population value.
• Coverage error occurs when the sampling frame does not perfectly match the target population.
• Nonresponse error is a major problem.
  • It occurs when sampled elements cannot be contacted.
  • It occurs when sampled elements refuse to answer questions.
  • Elements cannot be contacted due to various reasons.

Errors of Observation

• Observation errors can arise from the interviewer, respondent, instrument, or data collection method.
• Interviewer bias can influence responses.
  • Interviewers of the same gender/racial/ethnic group as respondents are often more successful.
  • Friendly or neutral interviewers tend to have higher response rates.

Respondents

• Respondents vary in motivation and ability to answer correctly.
• Getting honest responses to sensitive questions can be challenging.
• Common response errors include recall bias (not remembering correctly), prestige bias (exaggerating), and intentional deception (lying).

Census Sample

• A census occurs when the whole population is surveyed.
• A census happens when the whole population is feasible to study.
• In a census study, data is collected from every member of the population.

Probability vs. Nonprobability Sampling

• Probability Sampling: Each population member has a known chance of selection.
  • Methods include random sampling, systematic sampling, and stratified sampling.
• Nonprobability Sampling: Members are chosen without randomization.
  • Methods include convenience sampling, judgment sampling, quota sampling, and snowball sampling.

Random Sampling

• Random sampling is the purest form of probability sampling.
• Each population member has an equal chance of selection.
• May be difficult to identify all members for very large populations.

Systematic Sampling

• Systematic sampling selects every Nth member from a list.
• This method is as accurate as random sampling if the list has no hidden patterns.
• It's simpler and potentially less expensive than random sampling.

Stratified Sampling

• Stratified sampling uses subgroups (strata) to ensure representation of all groups in the sample.
• Stratified sampling is especially helpful when subgroups have a low occurrence rate.
• This method is more accurate than random sampling and can minimize sampling error.

Cluster Sampling

• Cluster sampling divides the population into groups (clusters).
• All members of randomly selected clusters are surveyed.
• Useful when a good sampling frame of the whole population isn't available or is expensive.

Convenience Sampling

• Convenience sampling selects accessible people.
• This method is quick and inexpensive.
• Findings are not generalizable to a larger population.

Judgment Sampling

• Researchers use judgment to select participants who best represent the population.
• The chosen sample may not be truly representative.
• This can be useful for exploratory research.

Quota Sampling

• Quota sampling creates subgroups (strata) that mirror the population.
• Nonprobability methods (convenience or judgment) are used to select people in each stratum.
• This method mirrors proportions in the larger population, but the selections are not random

Snowball Sampling

• Selecting participants and asking them to refer other people similar to them.
• Useful for hard-to-reach populations.
• The sample may not be representative of the whole population.

Sample Size

• Larger samples are needed for more diverse populations.
• Larger samples for probability samples are more reliable for representing the population.

Response Rates

• Low response rates (<60-70%) decrease the reliability of a survey.
• Follow-up techniques can increase response rates.