11 Basic Sampling Issues

Concept of Sampling Sampling: Process of obtaining information from subset (a sample) of a larger group (the universe or population). We then take the results from the sample and project them to the larger group. The motivation for sampling is to be able to make these estimates more quickly and at a much lower cost than would be possible by other means.

Concept of Sampling Population: Entire group of people about whom information is needed; also called universe or population of interest. Defining the population of interest is usually the 1 st step in the sampling process and often involves defining the target market for the product/service in question.

Population Example: The product concept test for a new nonprescription cold symptom-relief product such as Contac. The population of interest might includes everyone, because everyone gets colds from time to time. Although this is true, not everyone buys a nonprescription cold symptom relief product when he/she get cold. In this case, the 1 st task in the screening process would be to determine whether people have purchased/used one or more of a number of competing brands during some time period. Only those who had purchased/used one of these brands would be included in the population of interest. (Even this new product is really innovative, sales will have to come from current buyers in the product category.)

Concept of Sampling Sample vs. Census Census: Collection of data obtained from or about every member of the population of interest. Sample: Subset of all members of a population of interest. The popular belief that a census provides more accurate results than a sample is not necessary true. The researcher may not be able to obtain a complete and accurate list of the entire population, or certain members of the population may refuse to provide info. or be difficult to find.

Developing a Sampling Plan Step 1 Define the population of interest Step 2 Choose a data collection method Step 3 Identify a sampling frame Step 4 Select a sampling method Step 5 Determine sample size Step 6 Develop operational procedures for selecting sample elements Step 7 Execute the operational sampling plan

Developing a Sampling Plan Step 1: Define the Population of Interest The 1 st issue in developing a sampling plan is to specify the characteristics of those individuals/things (i.e. customers, companies, stores) from whom or about whom info. is needed to meet the research objectives. The population of interest is often specified in terms of geographic area, demographic characteristics, product/service usage characteristics, brand awareness measures, or other factors.

Developing a Sampling Plan Step 1: Define the Population of Interest In addition to defining who will be included in the population of interest, researchers should define the characteristics of individuals who should be excluded. For example: most commercial marketing research surveys exclude some individuals for so-called security reasons. Very frequently, one of the first questions on a survey asks whether the respondent or anyone in the respondent’s immediate family works in marketing research, advertising, or product/service area at issue in the survey. If the individual answers yes to this question, the interview is terminated. This type of question is called a security question because those who work in the industries in question are viewed as security risks (they may be competitors/ work for competitors).

Developing a Sampling Plan Step 2: Choose a Data-Collection Method Samples of implications we need to consider: Mail surveys suffer from biases associated with low response rates Telephone surveys have a less significant but growing problem with nonresponse, and suffer from call screening technologies used by potential respondents and increasing percentage of people have mobile phones only. Internet surveys have problems with professional respondents and the fact that the panel or lists used often do not provide appropriate representation of the population of interest.

Developing a Sampling Plan Step 3: Identify a Sampling Frame Sampling frame: List of population elements from which units to be sampled can be selected or a specified procedure for generating such a list. For example, a telephone book might be used as the sample frame for a telephone survey sample in which the population of interest is all households in a particular city. However, the telephone book does not include households that do not have telephones and those with unlisted numbers. Therefore, the researchers should use procedures that will that will produce samples including appropriate proportions of the households with unlisted numbers.

Developing a Sampling Plan Step 4: Select a Sampling Method “Classification of sampling methods” Sampling methods Probability sampling SystematicClusterSimple randomStratified Nonprobability sampling ConvenienceJudgmentQuotaSnowball

Developing a Sampling Plan Step 4: Select a Sampling Method The major alternatives in sampling methods can be grouped under 2 headings: Probability samples: Samples in which every element of the population has a known, nonzero likelihood of selection. Nonprobability samples: Samples in which specific elements from the population have been selected in a nonrandom manner.

Developing a Sampling Plan Step 4: Select a Sampling Method Advantages of probability samples: The researcher can be sure of obtaining info. From a representative cross section of the population of interest. Sampling error can be computed. The survey results can be projected to the total population. Disadvantages of probability samples: more expensive (high interview costs due to rules for selection, professional time spent in designing and executing the sample design)

Developing a Sampling Plan Step 5: Determine Sample Size Sample size: The identified and selected population subset for the survey, chosen because it represents the entire group. – Nonprobability samples rely on factors: available budget, rules of thumb, and number of subgroups. – Probability samples require formulas to calculate the sample size

Developing a Sampling Plan Step 6: Develop Operational Procedures for Selecting Sample Elements The procedures are much more critical to the successful execution of a probability sample, in which case they should be detailed, clear, and unambiguous and should eliminate any interviewer discretion (alternative) regarding the selection of specific sample elements.

Developing a Sampling Plan Step 7: Execute the Operational Sampling Plan This step requires adequate checking to ensure that specified procedures are followed:

Sampling and Nonsampling Errors Sampling error: Error that occurs because the sample selected is not perfectly representative of the population. – Administrative: problems in the execution of the sample plan. This can be avoided/minimized by careful attention to design and execution of the sample. – Random: due to chance and cannot be avoided Nonsampling error: All errors other than sampling error may cause inaccuracy and bias in the survey results; also called measurement error.

Probability Sampling Methods 1) Simple random sampling Probability sample selected by assigning a number to every element of the population and then using a table of random numbers to select specific elements for inclusion in the sample. Probability of selection= Sample size Population size Example: if the population size is 10,000 and the sample size is 400, the probability of selection is 4 percent. Simple random sampling guarantees that every member of the population has a known and equal chance of being selected for the sample. It begins with a current and complete listing of the population.

Probability Sampling Methods 2) Systematic sampling Probability sampling in which the entire populations is numbered and elements are selected using a skip interval. The researcher first numbers the entire population, as in simple random sampling. Then determines a skip interval and selects names based on this interval. Example: if you were using a local telephone directory and had computed a skip interval of 100, every 100 th name would be selected for the sample. The skip interval can be computed through use of the following formula: Skip interval = Population size Sample size

Probability Sampling Methods 3) Stratified sampling Probability sample that is forced to be more representative through simple random sampling of mutually exclusive and exhaustive subsets. This may be appropriate in certain cases. For example, if a political poll is being conducted to predict who will win an election, a difference in the way men and women are likely to vote would make gender an appropriate basis for stratification. Procedural steps: 1.The original, or parent, population is divided into two or more mutually exclusive and exhaustive subsets (e.g. male and female). 2.Simple random samples of elements from the two or more subsets are chosen independently of each other.

Probability Sampling Methods 4) Cluster sampling Probability sample in which the sampling units are selected from a number of small geographic areas to reduce data collection costs. Two basic steps: 1.The population of interest is divided into mutually exclusive and exhaustive subsets such as geographic areas. 2.A random sample of subsets (e.g. geographic areas) is selected.

Probability Sampling Methods Both stratified and cluster sampling involve dividing the population into mutually exclusive and exhaustive subgroups. However, in stratified samples the researcher selects a sample of elements from each subgroup, while in cluster samples, the researcher selects a sample of subgroups and then collects data either from all the elements in the subgroup (one-stage cluster sample) or from a sample of the elements (two-stage cluster sample).

Nonprobability Sampling Methods 1) Convenience samples Nonprobability samples based on using people who are easily accessible. Example: Frito-Lay often use their own employees for preliminary tests of new product formulations developed by their R&D departments. They are asking employees to provide gross sensory evaluations of new product formulations (e.g. saltiness, crispness, greasiness).

Nonprobability Sampling Methods 2) Judgment samples Nonprobability samples in which the selection criteria are based on the researcher’s judgment about representativeness of the population under study. Most test markets and many product tests conducted in shopping malls are essentially judgment sampling, one or a few markets are selected based on judgment that they are representative of the population as a whole.

Nonprobability Sampling Methods 3) Quota samples Nonprobability samples in which quotas, based on demographic or classification factors selected by the researcher, are established for population subgroups. Differences between a quota sample and a stratified sample: – Respondents for a quota sample are not selected randomly, as they must be for a stratified sample. – The demographic or classification factors of interest in a quota sample are selected on the basis of researcher judgment, while a stratified sample are selected based on the existence of correlation between the factor and behavior of interest.

Nonprobability Sampling Methods 4) Snowball samples Nonprobability samples in which additional respondents are selected based on referrals from initial respondents.