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Sampling Techniques: Choosing a Representative Subset of Population



Sampling is one of the very important aspects of market research. From a general perspective, sampling involves selecting a relatively small number of elements (characteristics) from a larger defined group of elements and expecting that the information gathered from the small group will provide accurate judgement about the larger group.

Determining sample size is a complex task and involves much clarity with regard to the balance between the resources available and number or accuracy or information obtained. Since data collection is generally one of the most expansive components of any research project various factors play a crucial role in determining the final sample size. Several qualitative and quantitative factors are considered when determining the sample size. The qualitative issues considered may include factors such as:

  • Nature of research and expected outcome
  • Importance of the decision to organization
  • Number of variables being studied
  • Sample size in similar studies
  • Resource/budgetary constraints

Various quantitative measures are also considered when determining sample size such as:

  • Variability of the population characteristics (greater the variability, larger the sample required)
  • Level of confidence/ Degree of precision desired (higher the confidence desired, larger the sample required)

Classification of Sampling Techniques

 How to obtain a sample is an important issue relating to research design. There are two basic sampling designs:

  1. Probability sampling
  2. Non-probability sampling

Of these two techniques, probability sampling is more robust in comparison as in this technique each sampling unit has a known, certain chance of getting selected in the final sample. Nonprobability techniques on the other hand, do not use chance selection procedure. Rather, they rely on the personal judgement of the researcher. The results obtained by using probability sampling can be generalized to the target population within a specified margin of error through the use of statistical methods. Put simply, probability sampling allows researchers to judge the reliability and validity of the findings in comparison to the defined target population.

In case of nonprobability sampling, the selection of each sampling unit is unknown and therefore, the potential error between the sample and target population cannot be computed. Thus, generalizability of findings generated through non-probability sampling is limited. While probability sampling techniques are robust in comparison, one of the major disadvantages of such techniques is the difficulty in obtaining a complete and accurate listing of elements of target population.

Both probability and nonprobability sampling procedures can be further sub-divided into specific sampling techniques that are appropriate for different circumstances.

Sampling Classification.png

I. Probability Sampling

a. Simple random sampling

Simple random sampling is a probability sampling technique wherein each member of the population is assigned a number and the desired sample is determined by generating random numbers appropriate for the relevant sample size. This ensures that each sampling unit has a known, equal and nonzero chance of getting selected into the sample.

b. Systematic random sampling

In systematic random sampling the sample is chosen by selecting a random starting point and then picking each ith element in succession from the sampling frame. The sampling interval i, is determined by dividing the population size N by the sample size n and rounding to the nearest integer. For example, if there were 10,000 owners of a washing machine and a sample of 100 is to be desired, the sampling interval i is 100. The researcher than selects a number between 1 and 100. If, for example, number 50 is chosen by the researcher, the sample will consists of members 50, 100, 150, 200, 250 and so on.

c. Stratified sampling

Stratified sampling is distinguished by the two-step procedure it involves. In the first step the population is divided into mutually exclusive and collectively exhaustive sub-populations, which are called strata. In the second step, a simple random sample of members are chosen independently from each group or strata. This technique is used when there is considerable diversity among the population elements.

In proportionate stratified sampling, the sample size from each stratum is dependent on that stratum’s size relative to the defined target population. Therefore, the larger strata are sampled more heavily using this method as they make up a larger percentage of the target population. On the other hand, in disproportionate stratified sampling, the sample selected from each stratum is independent of that stratum’s proportion of the total defined target population.

d. Cluster sampling

Cluster sampling is quite similar to stratified sampling and the major difference is that in stratified sampling, all the subpopulations (strata) are selected for further sampling whereas in cluster sampling only a sample of subpopulations (clusters) is chosen.

 

II. Non-Probability Sampling

 The selection of probability and nonprobability sampling is based on various considerations including, the nature of research, variability in population, statistical considerations and operational efficiency. Nonprobability sampling is mainly used in product testing, name testing, advertising testing where researchers and managers want to have a rough idea of population reaction rather than a precise understanding.

a. Convenience sampling

As the name implies, in convenience sampling, the selection of the respondent sample is left entirely to the researcher. Many of the mall intercept studies use convenience sampling. The researcher makes assumption that the target population is homogenous and the individuals interviewed are similar to the overall defined target population. This in itself leads to considerable sampling error as there is no way to judge the representativeness of the sample. Furthermore, the results generated are hard to generalize to a wider population. However, it is the most cost-effective as well as least time-consuming among all methods.

 b. Judgement sampling

Judgement sampling is an extension to the convenience sampling. In this procedure, respondents are selected according to an experienced researcher’s belief that they will meet the requirements of the study. This method also incorporates a great deal of sampling error since the researcher’s judgement may be wrong however it tends to be used in industrial markets quite regularly when small well-defined populations are to be researched.

c. Quota sampling

Quota sampling is a procedure that restricts the selection of the sample by controlling the number of respondents by one or more criterion. The restriction generally involves quotas regarding respondents’ demographic characteristics (e.g. age, race, income) or specific behaviors (e.g. frequency of purchase, usage patterns).

d. Snowball sampling

In snowball sampling, an initial group of respondents is selected, usually at random. After being interviewed however, these respondents are asked to identify others who belong to the target population of interest. Subsequent respondents are then selected on the basis of referral. Therefore, this procedure is also called referral sampling. Snowball sampling is used in researcher situations where defined target population is rare and unique and identifying the target respondents is a difficult task. For example, if the target respondent are owners of second hand washing machines, it will be extremely difficult to identify and hence, snowball sampling may provide a way forward.

Topics: Market Research