Week 5 - Sampling in quantitative research

Question 1

Inclusion and exclusion criteria

Answer 1

• Inclusion criteria are defined as the key features of the target population that the investigators will use to answer their research question.
• Typical inclusion criteria include socio-demographic, clinical, and geographic characteristics. Also consider ability to give consent, language.
• Criteria set boundaries for your study and relate to the research question.

Question 2

Probability sampling

Answer 2

• Based on probability theory
• Random selection of participants (simple, stratified, cluster, systematic)
• Process or procedure that assures that the different units in your population have
  equal probabilities of being chosen
• Manual ways of random selection
• Computer generated selection

Question 3

Non-probability sampling

Answer 3

• Sampling technique where the samples are gathered in a process that does not give
  all the individuals in the population equal chances of being selected
• Quota, convenience, purposeful, snowball, self-selection/ volunteer

Question 4

Sample size calculations and statistical power

Answer 4

• Needs to be calculated at design stage ‘a priori’
• Statistical formula
• Statistical power: measure of how likely the study is to produce statistically significant results for a difference between groups. Ie true difference and not difference due to chance
• But consider social and clinical significance too

Question 5

Simple random sampling

Answer 5

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.

Question 6

Stratified random sampling

Answer 6

• Stratified random sampling is a method of sampling that involves dividing a population into smaller groups–called strata. The groups or strata are organized based on the shared characteristics or attributes of the members in the group. The process of classifying the population into groups is called stratification.
• Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied.
• Stratified random sampling involves dividing the entire population into homogeneous groups called strata.
• Stratified random sampling differs from simple random sampling, which involves the random selection of data from an entire population, so each possible sample is equally likely to occur.

Question 7

Cluster sampling

Answer 7

• In cluster sampling, researchers divide a population into smaller groups known as clusters. They then randomly select among these clusters to form a sample. Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed.
• Cluster sampling divides the population into groups, then takes a random sample from each cluster.

Question 8

Systematic sampling

Answer 8

While the starting point may be random, the sampling involves the use of fixed intervals between each member.

Question 9

Sampling issues to consider:

Answer 9

• Population specification
• Random does not mean accidental
• Sampling frame and sampling plan
• Non-response bias
• Too many hypotheses for too small sample