Non probability Sampling
The methods of sampling previously discussed have all been probability sampling methods. In each case discussed, the basic requirement for probability sampling has been fulfilled namely every element in the universe sampled has a known chance of being chosen for the sample. Some methods of sampling in which this condition is not met will now be discussed. These are called non-probability samplings methods and include any sampling method in which the chance of choosing a particular universe elements is unknown.
Non-probability sampling includes a great variety of techniques ranging in complexity from a sample chosen purely on the basis of convenience to an elaborate ‘quota sample’ in which respondents are chosen on the basis of several socio-economic characteristics. Any sampling procedure that does not specify the chance of selecting any universe element is a non-probability sampling method, no matter what else is included in the specifications.
As the name implies a convenience sample is one chosen purely for expedience (e.g. items are selected because they are easy or cheap to find and measure). An extreme example is monitoring price trends in a nearby grocery store, with the objective of inferring national price movements. Another is evaluating public opinion issues, based on television news programs soliciting opinions from people conveniently in camera range.
While few analysts would find credibility in conclusions from such extreme cases, the inappropriateness of using convenience sampling to estimate universe values is not widely recognized. The major problem with this (and other no n-probability methods) is that one is unable to draw objectives inferences about a rigorously defined universe. In practice, it is often found that the response given by ‘convenient’ items in a universe differ significantly from the responses given by universe items that are less accessible. As a result, unless one is dealing with known highly homogeneous universe (virtually all items responding alike), convenience sampling should not be used to estimate universe values.
Convenience sampling is sometimes useful in marketing research for certain specialized purposes. If one has very little information about a subject, than a small scale convenience sample can be of value in exploratory work, to help understand the range of variability of response in a subject area. Just talking to a few consumers may help identify issues.
A second method of non-probability sampling that is sometimes advocated is the selection of universe items by means of expert judgment. Using this approach, specialists in the subject matter of the survey choose what they believe to be the best sample for that particular study. For example, a group of sales managers might select a sample of grocery stores in a city that they regarded as ‘representative’. This approach has been found empirically to produce unsatisfactory results. And, of course, there is no objective way to evaluating the precision of sample results. Despite these limitations, this method may be useful when the total sample size is extremely small.
One of the most commonly used non-probability sample designs is quota sampling, which enjoys its most widespread use in consumer surveys. This sampling method also uses the principle of stratification. As in stratified random sampling, the researcher begins by constructing strata. Bases for stratification in consumer surveys are commonly demographic e.g. age, sex, income and so on. Often compound stratification is used – for example age groups within sex.
Next, sample sizes (called quotas) are established for each stratum. As with stratified random sampling, the sampling within strata may be proportional or disproportional. Field workers are then instructed to conduct interviews with the designated quotas, with the identification of individual respondents being left to the field workers.
Example: a food manufacturer wished to sample current users of the company’s brand to obtain their reactions to proposed new packaging. A quota sample of brand users, stratified by age within sex, was designed with the following quotas.
Men, 18-34 50
Men, 35-49 50
Women 18-34 100
Women, 35-49 100
Field workers were instructed to fill their interview quotas in the most expeditious way possible.