Marketing research: sampling
- Levels: AS, A Level
- Exam boards: AQA, Edexcel, OCR, IB, Other, Pre-U
What is sampling? In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. Let's look at sampling in more detail and discuss the most popular types of sampling used in market research.
It would be expensive and time-consuming to collect data from the whole population of a market. Therefore, market researchers make extensive of sampling from which, through careful design and analysis, marketers can draw information about their chosen market.
Sample design covers:
- Method of selection
- Sample structure
- Plans for analysing and interpreting the results.
Sample designs can vary from simple to complex. They depend on the type of information required and the way the sample is selected.
Sample design affects the size of the sample and the way in which analysis is carried out; in simple terms the more precision the market researcher requires, the more complex the design and larger the sample size will be.
The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative. It may be necessary to draw a larger sample than would be expected from some parts of the population: for example, to select more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.
Many sample designs are built around the concept of random selection. This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.
Defining the Population
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.
The target population is sampled using a sampling frame.
Often, the units in the population can be identified by existing information such as pay-rolls, company lists, government registers etc.
A sampling frame could also be geographical. For example, postcodes have become a well-used means of selecting a sample.
For any sample design, deciding upon the appropriate sample size will depend on several key factors:
- 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
- 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 sample
- 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
Types of Sampling
There are many different types of sampling methods, here's a summary of the most common:
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.
- Quick and easy
- Doesn't need complete population information
- Good for face-to-face surveys
- Expensive if the clusters are large
- Greater risk of sampling error
Uses those who are willing to volunteer and easiest to involve in the study.
- Subjects are readily available
- Large amounts of information can be gathered quickly
- 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
- Good for providing illustrative examples or case studies
- 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.
- Quick and easy way of obtaining a sample
- Not random, so some risk of bias
- Need to understand the population to be able to identify the basis of stratification
Simply random sampling
This makes sure that every member of the population has an equal chance of selection.
- Simple to design and interpret
- Can calculate both estimate of the population and sampling error
- 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.
- Easier to extract the sample than via simple random
- Ensures sample is spread across the population
- Can be costly and time-consuming if the sample is not conveniently located
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