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Marketing Research - Sampling

Author: Jim Riley  Last updated: Sunday 23 September, 2012

Introduction

Market research involves the collection of data to obtain insight and knowledge into the needs and wants of customers and the structure and dynamics of a market. In nearly all cases, it would be very costly and time-consuming to collect data from the entire population of a market. Accordingly, in market research, extensive use is made of sampling from which, through careful design and analysis, Marketers can draw information about the market.

Sample Design

Sample design covers the method of selection, the sample structure and plans for analysing and interpreting the results. Sample designs can vary from simple to complex and 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 will be the design and the larger the sample size.

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 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; for example, 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.

Sample Size

For any sample design deciding upon the appropriate sample size will depend on several key factors

(1) No estimate taken from a sample is expected to be exact: Any assumptions about the overall population based on the results of a sample will have an attached margin of error.

(2) To lower the margin of error usually requires a larger sample size. The amount of variability in the population (i.e. the range of values or opinions) will also affect accuracy and therefore the size of sample.

(3) The confidence level is the likelihood that the results obtained from the sample lie within a required precision. The higher the confidence level, that is the more certain you wish to be that the results are not atypical. Statisticians often use a 95 per cent confidence level to provide strong conclusions.

(4) Population size does not normally affect sample size. In fact the larger the population size the lower the proportion of that population that needs to be sampled to be representative. It is only when the proposed sample size is more than 5 per cent 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 technique. We have summarised the most popular below:

Sampling Method

Definition

Uses

Limitations

Cluster Sampling)

Units in the population can often be found in certain geographic groups or "clusters" (e.g. primary school children in Derbyshire. A random sample of clusters is taken, then all units within the cluster are examined

Quick & easy; does not require complete population information; good for face-to-face surveys

Expensive if the clusters are large; greater risk of sampling error

Convenience Sampling

Uses those who are willing to volunteer

Readily available; large amount of information can be gathered quickly

Cannot extrapolate from sample to infer about the population; prone to volunteer bias

Judgement Sampling

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

Quota Sampling

Aim is to obtain a sample that is "representative" of the overall population; the population is divided ("stratified") by the most important variables (e.g. income,. age, location) and a required quota sample is drawn from each stratum

Quick & easy way of obtaining a sample

Not random, so still some risk of bias; need to understand the population to be able to identify the basis of stratification

Simply Random Sampling

Ensures that every member of the population has an equal chance of selection

Simply to design and interpret; can calculate estimate of the population and the sampling error

Need a complete and accurate population listing; may not be practical if the sample requires lots of small visits all over the country

Systematic Sampling

After randomly selecting a starting point from the population, between 1 and "n", every nth unit is selected, where 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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