SAMPLING

Examine a Part of the Whole In most surveys access to the entire population is near impossible, The results from a survey with a carefully selected sample will reflect extremely closely those that would have been obtained had the population provided the data.

Bias The one thing above all to avoid. There is usually no way to fix a biased sample and no way to salvage useful information from it. The best way to avoid bias is to select individuals for the sample at random. The value of deliberately introducing randomness is one of the great insights of Statistics

There are essentiality two types of sampling: probability non-probability sampling.

Probability Sampling Methods Probability or random sampling gives all members of the population a known chance of being selected for inclusion in the sample and this does not depend upon previous events in the selection process. The selection of individuals does not affect the chance of anyone else in the population being selected. Many statistical techniques assume that a sample was selected on a random basis

Randomise Randomisation can protect you against factors that you know are in the data. It can also help protect against factors you are not even aware of. Randomising protects us from the influences of all the features of our population, even ones that we may not have thought about. Randomising makes sure that on the average the sample looks like the rest of the population

Randomize Individuals are randomly selected. No one group should be over-represented. Sampling randomly gets rid of bias. Random samples rely on the absolute objectivity of random numbers. There are tables and books of random digits available for random sampling. Statistical software can generate random digits (e.g., Excel)

Four basic types of random sampling techniques: Simple Random Sampling Systematic Sampling Stratified Sampling Cluster or Multi-stage Sampling

Simple Random Sampling This is the ideal choice as it is a ‘perfect’ random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.

Simple Random Samples To select a sample at random, we first need to define where the sample will come from. The sampling frame is a list of individuals from which the sample is drawn. E.g., To select a random sample of students from a college, we might obtain a list of all registered full-time students. When defining sampling frame, must deal with details defining the population; are part-time students included? How about current study-abroad students? Once we have our sampling frame, the easiest way to choose an SRS is with random numbers.

Non-probability Sampling Methods Non-probability sampling procedures are much less desirable, as they will almost certainly contain sampling biases. Unfortunately, in some circumstances such methods are unavoidable. In Consumer Research the most frequently-adopted form of non-probability sampling is known as quota sampling.

Quota Sampling Similar to cluster sampling in that it requires the definition of key subgroups. Main difference lies in the fact that quotas (i.e. the amount of people to be surveyed) within subgroups are set beforehand (e.g. 25% 16-24 yr olds, 30% 25-34 yr olds, 20% 35-55 yr olds, and 25% 56+ yr olds) Usually proportions are set to match known population distributions. Interviewers then select respondents according to these criteria rather than at random. The subjective nature of this selection means that only about a proportion of the population has a chance of being selected in a typical quota sampling strategy.