Sampling (MR, unit 9)

Population: set of individuals or items from which a statistical sample is taken
Sampling: one of most important marketing research tools because population often too large to take complete survey (census)
Census: survey entire population

Once certain sample size reached, very little accuracy in examining more
Higher cost of census may exceed value of results
Changeability - census data often out of date by time it's collected

Choosing a sample
Must be complete (cover all relevant aspects of population to be examined) or will be biased.
3 types of sample:

1. Random sampling
2. Quasi-random sampling: systematic, stratified, multistage
3. Non-random sampling: convenience, quota, cluster

1) Random sampling
A simple random sample is selected in way that every item in the population has an equal change of being included
Not necessarily a perfect sample - only census would eliminate all chance of bias

Sampling frame required in random sampling: a numbered list of all the items in the population
Should be:

• complete - include all members of population
• accurate
• up-to-date
• convenient - accessible
  
• without duplication

Problems: can be too expensive

2) Quasi-random sampling: good approximation to random sampling

• Systematic sampling

Selects every nth item after a random start
e.g. sample of 20 from population of 800 (800/20 = 40). 40 = sampling interval. Choose every 40th item after random start.
Must ensure no regular pattern to the population which if it coincided with sampling interval would lead to bias. Avoided with multiple starting points and sampling intervals.

• Stratified sampling (often best method)

Population must be divided into strata (categories)
Removes possibility of sample being all from same demographic, or location. Random samples taken from each strata - in proportion to weight of each strata in the population.
Problem: requires prior knowledge of each item in the population.

• Multistage sampling

Divide population into large groups (usually on geographic basis). Small sample of groups taken at random. These are subdivided into smaller groups. A number is selected at random. Process repeated as many times as necessary. Random sample of individuals then taken in each of the smallest groups.
Problem: an approximation to a random sample

3) Non-random sampling: used when a sampling frame cannot be established

• Convenience sampling

Internet & e-mail based surveys use. Ask the most accessible members of the population. Useful for exploratory if composition of selected sample is reasonably similar to the population of interest. Cheap & simple. Problem: Not precise. Unlikely to reflect population as whole. NB. there's no reliable sample frame of email addresses. Becoming increasingly prevalent but unresolved concerns over its representativeness.

• Quota sampling

Like convenience but interviewers interview everyone they meet up to certain quota. Partly overcome bias by subdividing the quota into different types of people to ensure the sample measures the structure of the population.
Problem: prone to bias

• Cluster sampling

Clusters of population units are selected at random (each cluster intended to represent the total population in microcosm) e.g. random phonebook selections. Then some of these clusters are studied. Only need to produces sampling frame for those clusters.
Unlike stratified sampling where subsets have to be homogenous and significantly different from each other. Cluster can therefore be used if adequate sampling frame not available.
Problem: limited to situations where pop can be easily divided into representative clusters.