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Key terms in Sampling Sample: 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 is generally 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 from a 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.

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Sampling Techniques Probability SamplingNon-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

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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 is called, requires that the sample frame be known and that members or elements be selected independently 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 5 th person in the order 5 th, 10 th, 15 th, …, and so on until there are 40 people. Systematic sampling assumes some order in the sample frame. It is simpler and less costly than SRS.

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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 may also be used to select members, elements or units from the cluster to be included in the study Description of Sampling Techniques Probability Samples (Continued)

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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.

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Description of Sampling Techniques Non-Probability Samples (Continued) Quota Sampling: is similar to stratified sampling except it is done in a non-probabilistic way. It is a two-stage process where the stratums and their proportion as they exist in the population are first identified. Then, convenience or judgement sampling is used to assign 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 based on 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.

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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

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Sampling Technique ProsCons Probability Simple RandomEasy to understand; can project to the population of interest Difficult to construct sample frame; no guarantee of representativeness, expensive SystematicEasy to implement; greater representativeness Could reduce representativeness if not done properly StratifiedMore subpopulations are included; more precise results, reduce sampling errors Difficult to use many stratification variables ClusterEasy to implementRequires more statistical knowledge to compute & interpret results Non-probability ConvenienceLeast time consumingSelection bias, not representative JudgmentalLeast time consumingSubjective QuotaCan take important characteristics into account Selection bias, not representative SnowballUsed when sample characteristics are rare Very time consuming Comparisons of Sampling Techniques on Key Considerations

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Sources: http://www.statpac.com/surveys/sampling.htmhttp://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,

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