Sampling Techniques

  • Sampling techniques may be broadly classified as non-probability and probability.
  • Non-probability sampling relies on the personal judgment of the researcher rather than chance to
marketing research  Sampling Techniques

select sample elements. The researcher can arbitrarily or consciously decide what elements to

include in the sample. Non- probability sampling may yield good estimates of the population

characteristics. However, they do not allow for objective evaluation of the precision of the sample

results. The estimates obtained are not statistically projectable to the population.

Commonly used non-probability sampling techniques include:

  • Convenience sampling
  • Judgmental sampling
  • Quota sampling
  • Snowball sampling
  • In Probability sampling, sampling units are selected by chance. Every potential sample need not have the same probability of selection, but it is possible to specify the probability of selecting any particular sample of a given size. Commonly used probability sampling techniques include:

  • Simple random sampling
  • Systematic sampling
  • Cluster sampling
  • We will discuss in depth the above techniques and briefly touch on some others.

Non-Probability Sampling Techniques

Convenience Sampling

  • Convenience sampling attempts to obtain a sample of convenient elements. The selection ofsampling units is left primarily to the interviewer. Examples of convenience sampling include:
  • Use of students, church groups and members of social organizations
  • Mall intercept interviews without qualifying the respondents
  • Department stores using charge account lists
  • Tear-out questionnaires included in a magazine
  • “People on the street” interviews
  • Convenience sampling is the least expensive and least time consuming of all samplingtechniques. The sampling units are accessible, easy to measure and cooperative. In spite of theseadvantages, this form of sampling has serious limitations. Many potential sources of selection biasare present, including respondent self selection. Convenience samples are not representative of anydefinable population.

  • Hence, it is not theoretically meaningful to generalize to any population from a conveniencesample, and convenience samples are not appropriate for marketing research projects involvingpopulation inferences. Convenience samples are not recommended for descriptive or causalresearch, but they can be used in exploratory research for generating ideas, insights, or hypothesis.

Convenience samples can be used for focus groups, pre-testing questionnaires, or pilot studies.

Nevertheless, this technique is sometimes used even in large surveys.

Judgmental Sampling

  • Judgmental sampling is a form of convenience sampling in which the population elements are

selected based on the judgment of the researcher. The researcher, exercising judgment or expertise,

chooses the elements to be included in the sample, because he or she believes that they are

representative of the population of interest or are otherwise appropriate. Common examples of

Judgmental sampling include:

  • Test markets selected to determine the potential of a new product
  • Purchase engineers selected in industrial marketing research
  • Expert witness used in court
  • Department stores selected to test a new merchandising display system
  • Judgmental sampling is low cost, convenient and quick, yet it does not allow directgeneralizations to a specific population. Judgmental sampling is subjective and its value dependsentirely on the researcher’s judgment, expertise, and creativity. It may be useful if broadpopulation inferences are not required.

Quota Sampling

  • Quota sampling may be viewed as two-stage restricted judgmental sampling. The first stageconsists of developing control categories, or quotas of population elements. The relevant controlcharacteristics, which may include sex, age, and race, are identified on the basis of judgment. Inother words, the quotas ensure that the composition of the sample is the same as the compositionof the population with respect to the characteristics of interest. In the second stage, sampleelements are selected based on convenience or judgment.

Snowball Sampling

  • In snowball sampling, an initial group of respondents is selected, usually at random. After beinginterviewed, these respondents are asked to identify others who belong to the target population ofinterest. Subsequent respondents are selected based on the referrals. This process may be carriedout in waves by obtaining referrals from referrals, thus leading to a snowballing effect. A majorobjective of snowball sampling is to estimate characteristics that are rare in the population.Snowball sampling is used in industrial buyer-seller research to identify buyer-seller pairs. Themajor advantage of snowball sampling is that it substantially increases the likelihood of locatingthe desired characteristic in the population

Probability Sampling Techniques

Simple Random Sampling

  • In simple random sampling (SRS), each element in the population has a known and equalprobability of selection. Every element is selected independently of every other element. Thsample is drawn by a random procedure from a sampling frame. This method is equivalent to alottery system in which names are placed in a container, the container is shaken; and the names ofthe winners are then drawn out in an unbiased manner.
  • The researcher first compiles a sampling frame in which each element is assigned a uniqueidentification number. Then random numbers are generated to determine which elements toinclude in the sample.
  • SRS has many desirable features. It is easily understood. The sample results may be projected tothe target population. SRS suffers from at least four significant limitations. First, it is oftendifficult to construct a sampling frame that will permit a simple random sample to be drawn.Second. SRS can result in samples that are very large or spread over large geographic areas, thusincreasing the time and cost of data collection.
  • Third, SRS often results in lower precision with larger standard errors than other probabilitysampling techniques. Fourth, SRS may or may not result in a representative sample. Althoughsamples drawn will represent the population well on average, a given simple random sample maygrossly misrepresent the target population. For these reasons, SRS is nut widely used in marketingresearch.

Systematic Sampling

  • In systematic sampling, the sample is chosen by selecting a random starting point and thenpicking every ith element in succession from the sampling frame. The sampling interval, i, isdetermined by dividing the population size N by the sample size n and rounding to the nearestinteger.
  • Systematic sampling is less costly and easier than SRS, because random selection is done onlyonce. Moreover the random numbers do not have to be matched with individual elements as inSRS. Systematic sampling is often employed in consumer mail, telephone, mall intercept andinternet interviews.

Stratified Sampling

  • Stratified Sampling is a two-step process in which the population is partitioned into subpopulations,or strata. The strata should be mutually exclusive and collectively exhaustive in thatevery population element should be assigned to one and only one stratum and no populationelements should be omitted. Next, elements are selected from each stratum by a random procedure,usually SRS. Stratified sampling differs from quota sampling in that the sample elements areselected probabilistically rather than based on convenience or judgment. A major objective ofstratified sampling is to increase precision without increasing cost.
  • The elements within a stratum should be as homogeneous as possible, but the elements indifferent strata should he as heterogeneous as possible. Variables commonly used for stratificationinclude demographic characteristics, type of customer (credit card versus non-credit card), size offirm, or type of industry.
  • Stratified sampling can be proportionate or disproportionate.

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