Key terms in SamplingSample: A fraction or portion of the population of interest e.g. consumers, brands, companies, products, etc

Population: All the elements or units of the “universe” of interest to a study

Census: Studying the entire population. A census offers high precision and representativeness but isgenerally impractical, costly, and time consuming.

Sampling: The process of selecting a sample from the population

Sample Frame: A list of all the elements, units, or members of a population of interest

Sampling Error: Differences in characteristics (e.g. mean, variance, etc) obtained froma sample compared to that of the population of interest. Goal: minimize sample error

Probability Sample: Sampling methods in which each element in the population have a known chance or probability of being selected

Non-probability Sample: Sampling methods that does not use random selection but relies on the judgement of the researcher or the circumstances.

Sampling Techniques

Probability Sampling Non-Probability Sampling

1. Simple Random Sampling

2. Systematic Sampling

3. Stratified Sampling

4. Cluster Sampling

1. Convenience Sampling

2. Judgmental Sampling

3. Quota Sampling

4. Snowball Sampling

Description of Sampling Techniques

Probability Samples

Simple Random Sampling: A sample technique in which each element or member of the population has an equal chance of being selected at any stage of the process. SRS, as it iscalled, requires that the sample frame be known and that members or elements be selectedindependently of each other. Most commonly used statistical techniques assume SRS.

Systematic Sampling: This technique requires the construction of a sample frame and then selecting the jth element, unit, individual or record from the list in a very systematic way.For example, if the sample frame yield a population of 200 people with a particular trait, and the research wants a sample of 40 people, (s)/he can decide on picking the 5th person in the order 5th, 10th, 15th, …, and so on until there are 40 people. Systematic samplingassumes some order in the sample frame. It is simpler and less costly than SRS.

Stratified Sampling: Dividing the population into sub-populations, groups or strata based on certain characteristics (e.g. race, geography, education, income) in which it is believed that the strata or groups are different. Members within a strata must be as homogenous as possible while members between strata must be heterogeneous as possible.SRS procedures is then use to select samples from each group. Stratified sampling could improve the representativeness of the sample

Cluster Sampling: The population is first divided into groups or clusters and then SRS is used to select the clusters to be included in the study. SRS mayalso be used to select members, elements or units from the cluster to be included in the study

Description of Sampling Techniques

Probability Samples (Continued)

Description of Sampling Techniques

Non-Probability Samples

Convenience Sampling: The sample is selected based on convenience. It is often used in exploratory research where the researcher is trying to get a good estimate of the population in a relatively quick and inexpensive way. This technique suffers from self-selection bias and cannot be generalized.

Judgement Sampling: The sample is selected based on the judgement of the researcher. It is a form of convenience sampling. The researcher must ensure that the sample is representative of the population of interest. This type of sampling could be useful when identifying a test market for a product.

Description of Sampling Techniques

Non-Probability Samples (Continued)

Quota Sampling: is similar to stratified sampling except it is done in a non-probabilisticway. It is a two-stage process where the stratums and their proportion as they exist inthe population are first identified. Then, convenience or judgement sampling is used toassign quotas to each stratum and to select the elements from the stratums.

Snowball Sampling: is used when the desired sample characteristics are rare. It is basedon a system of referrals where the previous subject or individual is used to generate subsequent subjects or individuals. It can substantially reduce search costs but it has huge bias.

Key considerations when choosing a sampling technique

• The objective of the study

• The nature of the research e.g. is it exploratory or conclusive

• Statistical considerations e.g. sampling errors and non-sampling errors

• Difficulty/ease of constructing a sample frame

• Characteristics of population of interest

• Representativeness required by study

• Monetary costs of using a particular sampling technique

• Time required to select the sample and collect the data

• Difficulty/ease of implementing or using a particular sampling technique

• Difficulty/ease of computing and interpreting the results

As a rule, market researchers and analysts have to make trade-offs among the various considerations when designing a study

Sampling Technique

Pros Cons

Probability

Simple Random Easy to understand; can project to the population of interest

Difficult to construct sample frame; no guarantee of representativeness, expensive

Systematic Easy to implement; greater representativeness

Could reduce representativeness if not done properly

Stratified More subpopulations are included; more precise results, reduce sampling errors

Difficult to use many stratification variables

Cluster Easy to implement Requires more statistical knowledge to compute & interpret results

Non-probability

Convenience Least time consuming Selection bias, not representative

Judgmental Least time consuming Subjective

Quota Can take important characteristics into account

Selection bias, not representative

Snowball Used when sample characteristics are rare

Very time consuming

Comparisons of Sampling Techniques on Key Considerations

Sources:

http://www.statpac.com/surveys/sampling.htm accessed December 8, 2009 at 10:56 pm.

Naresh, Malhotral (2007), Marketing Research: An Applied Orientation, 5 th Edition, Prentice Hall,