Market Research-Sampling
Sample Design
sample structure
plans for analysing and interpreting the results
methods of selection
Defining the population
The target population is sampled using a sampling frame
sample size
The confidence level is the likelihood that the results obtained from the sample lie within a required precision: the higher the confidence level, the more certain you wish to be that the results are not atypical. Statisticians often use a 95% confidence level to provide strong conclusions
Population size does not normally affect sample size: in fact the larger the population size, the lower the proportion of that population needs to be sampled to be representative. It's only when the proposed sample size is more than 5% of the population that the population size becomes part of the formulae to calculate the sample size
To lower the margin of error usually requires a larger sample size: the amount of variability in the population, ie the range of values or opinions, will also affect accuracy and therefore size of the
No estimate taken from a sample is expected to be exact: assumptions about the overall population based on the results of a sample will have an attached margin of error
The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative.
Many sample designs are built around the concept of random selection.
The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible. This is to ensure that all elements within the population are represented.
types of sampling
Judgement sampling
Quota sampling
Convenience sampling
Simply random sampling
cluster sampling
Systematic sampling
Units in the population can often be found in certain geographic groups or "clusters" for example, primary school children in Derbyshire.
A random sample of clusters is taken, then all units within the cluster are examined.
Disadvantages: Expensive if the clusters are large. Greater risk of sampling error
Advantages: Quick and easy. Doesn't need complete population information. Good for face-to-face surveys
Uses those who are willing to volunteer and easiest to involve in the study.
Advantages: Subjects are readily available.Large amounts of information can be gathered quickly
Disadvantages: The sample is not representative of the entire population, so results can't speak for them - inferences are limited. future data.Prone to volunteer bias
A deliberate choice of a sample - the opposite of random
Advantages: Good for providing illustrative examples or case studies
Disadvantages: Very prone to bias.Samples often small.Cannot extrapolate from sample
The aim is to obtain a sample that is "representative" of the overall population. The population is divided ("stratified") by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.
Advantages :Quick and easy way of obtaining a sample
Disadvantages: Not random, so some risk of bias. Need to understand the population to be able to identify the basis of stratification
This makes sure that every member of the population has an equal chance of selection.
Advantages:Simple to design and interpret.Can calculate both estimate of the population and sampling error
Disadvantages :Need a complete and accurate population listing. May not be practical if the sample requires lots of small visits over the country
After randomly selecting a starting point from the population between 1 and n, every nth unit is selected. n equals the population size divided by the sample size.
Advantages: Easier to extract the sample than via simple random. Ensures sample is spread across the population
Disadvantages :Can be costly and time-consuming if the sample is not conveniently located