In determining sample you need to decide whom you will survey and how many people you will survey. Researchers call this group the target popula-tion. In some cases, when doing an employee attitude survey for instance, the population is obvious. In other cases, such as when prospective customers are involved, determining the target group is more difficult. Correctly determining the target population is critical. A poorly defined target population will result in unrepresentative results.
To decide how many people need surveying is both a statistical and com-mercial decision. Surveying more people costs more money but does increase the accuracy, or precision, of the results (up to a point). To increase a sample from 250 to 1,000 requires four times as many people, but it only doubles the precision.
There are two basic types of sampling:
1. Probability (or random) samples – where individuals are drawn in some random fashion from among the population.
2. Non-probability (or non-random) samples – where individuals are selected on the basis of one or more criteria determined by the researcher.
Probability samples – within this category there are four sampling methods which are commonly employed in market research (Churchill, 2002):
Simple random sampling – individuals are randomly drawn from the popu-lation at large (for example, by selecting from the electoral register).
Systematic sampling – individuals (or households) are sampled at intervals based on a random start point. For instance, it might be decided to visit every tenth person on the electoral register starting at number 4. In this
case the sampling interval is 10. The individuals that would be sampled are thus numbers % 14, 24 and so on.
Stratified random sampling – the population is first divided into groups based on one or more criteria (let say age, gender, or other affiliation) and, from within these groups, individuals are randomly selected. For this method to be possible the data available on each individual must contain information about the criteria to be used to stratify the groups. This is not always the case.
Multistage sampling – the population is first divided into quite large groups, usually based on geography. A random selection of these large groups is then selected and sub-divided again. A random selection of groups is again made from the resulting sub-divisions and the process re-peated as many times as required by the survey. Eventually, individuals are randomly sampled from the small groups arising as a result of the final subdivision.
To select individuals on a random basis it is necessary to construct a sam-pling frame. This is a list of all the known individuals within the population from which the selection is to take place. Each individual is assigned a unique number then, using random number tables or the computer equivalent, indi-viduals are selected on the basis of random numbers produced.
Non-probability samples – when a sampling frame cannot be established, or would prove too expensive or time consuming, one of the following four non-random methods are usually used:
Judgement sampling – the researcher uses their judgement to select peo-ple that they feel are representative of the population or have a particular expertise or knowledge which makes them suitable. For example, business leaders, top scientists and so on. This method is commonly used with small sample sizes.
Convenience sampling – the most convenient population is chosen, which may be the researchers friends, work colleagues or students from a nearby college. This method is often used to save time and resources.
Cluster sampling – the population is repeatedly divided in to groups ra-ther like the process for multistage sampling. However, cluster sampling is different in that all individuals from the remaining small groups are inter-viewed rather than just a random sample of those remaining.
127
Quota sampling – the researcher selects a predetermined number of indi-viduals from different groups (i.e. based on age, gender and so on). This is perhaps the most popular non-probability sampling method used.
Table 6.6. Comparison of different sampling methods
Simple random Systematic random Stratified Multistage Convenience Judgement Cluster Quota
Requires Source: Phipps & Simmons (2008).
If you select a sample population which is not representative then your re-sults may be biased. That is, they will not represent responses in the wider population. For example, if you asked Ford employees whether they preferred Ford cars you would probably get biased results. Totally excluding all bias is extremely difficult but should be the goal in any survey. However, just being aware of bias will allow you to avoid the more obvious sources and interpret certain results more cautiously.
There are three main sources of bias:
1. Incomplete coverage – there may be a number of reasons for this:
a) sampling frame is incomplete, b) certain outlying areas are excluded,
c) the survey method used may place constraints on those that can be sampled, i.e. a telephone survey requires ownership of a telephone, 2. Non-response – low response rates are a problem in any survey. Whether it
is a street, telephone or postal survey a significant proportion of those ap-proached will refuse to answer questions.
3. Overrepresentation – some sampling methods deliberately overrepresent certain groups (the non-probability sampling techniques already men-tioned). Although this allows detailed examination of a certain subgroup of the population, there is no way of knowing how else the group characteris-tics might affect the survey responses.
ACTIVITY You are asked to gather opinions on a new design of carry cot for babies. What problems might you experience using your classmates as a convenience sample?