Sampling Design & Procedure

Population The aggregate of the all the elements, sharing some common set of characteristics that comprises the universe for the purpose of the marketing research problem The population parameters are usually numbers, such as proportion of consumers who are loyal to a certain brand of toothpaste

Census A complete of enumeration of the elements of a population or study objects The population parameters can be calculated directly in straightforward way after the census is enumerated

Sample A subgroup of the elements of the population selected for participation in the study Sample characteristics, called statistics, are then used to make inferences about the population parameters

Sample versus Census Budget and time limits are obvious constraints favoring the use of a sample A census is both costly and time consuming to conduct

Sample versus Census A census is unrealistic if the population is large In the case of many industrial products the population is small, making a census feasible as well as desirable

Sample versus Census Another reason for favoring census for small populations is the large variance in the characteristic of interest in small samples If the cost of sampling error is high, such as major population element is eliminated, a census, which eliminates such errors, is desirable

Sample versus Census High cost of non-sampling errors, on the other hand, would favor sampling A census can greatly increase nonsampling errors to the point that these errors exceed the sampling errors of a sample

Sample versus Census Nonsampling errors are found to be major contributor to the total error Whereas random sampling errors have been relatively small in magnitude

Sample versus Census Hence, in most cases, accuracy considerations would favor a sample over a census

Sample versus Census A sample may be preferred if the measurement process results in destruction or contamination of the elements sampled

Sample versus Census For example, product usage tests result in the consumption of the product. Therefore, taking a census in a study that requires respondents to consume milk in “blind taste tests” would not be feasible The need to keep the study secret, may favor a sample over a census

The Sampling Design Process Define the target population Determine the sampling frame Select sampling technique(s) Determine the sample size Execute the sampling process

The Sampling Design Process Define the Target Population Sample design begins with specifying the target population The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made

The Sampling Design Process Define the Target Population The target population must be defined precisely Defining the target population involves translating the problem definition into a precise statement of who should and should not be included in the sample

The Sampling Design Process Define the Target Population The target population should be defined in terms of elements, sampling units, extent and time An element is the object about which or from which information is desired. In survey research, the element is usually the respondent

The Sampling Design Process Define the Target Population A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process If potential respondents are directly sampled, sampling unit would be the same as an element

The Sampling Design Process Define the Target Population Alternatively, the sampling unit might be household. In the latter case, households would be sampled and potential respondents in each selected household would be interviewed In this case, the sampling unit and the population element are different

The Sampling Design Process Define the Target Population Extent refers to geographical boundaries. For instance, a target population may be limited to Karachi while a sampling unit may be situated within Karachi district only Time factor is the time period under consideration. For instance, if a marketing research project is to be completed in 15 weeks, 15 weeks is the time for the marketing research study

The Sampling Design Process Determine the Sampling Frame A sampling frame is the representation of the elements of the target population It consists of a list or set of directions for identifying the target population

The Sampling Design Process Determine the Sampling Frame In some cases the discrepancy between the population and sampling frame is small enough to ignore However, in most cases, the researcher should recognize and treat sampling frame error

The Sampling Design Process Determine the Sampling Frame Treating sampling frame error can be done in at least three ways

The Sampling Design Process Determine the Sampling Frame One approach is to redefine the target population in terms of the sampling frame. For instance, if the telephone directory is used as a sampling frame, the population of households could be redefined as those with a correct listing in the telephone directory in a given area

The Sampling Design Process Determine the Sampling Frame Although this approach is simplistic, it does prevent the researcher from being mislead about the actual population being investigated

The Sampling Design Process Determine the Sampling Frame Another way is to account for sampling frame error by screening the respondents in the data-collection phase

The Sampling Design Process Determine the Sampling Frame The respondents could be screened with respect to demographic characteristics, familiarity, product usage, and other characteristics to ensure that they satisfy the criteria for target population. Screening can eliminate inappropriate elements contained in the sampling frame, but it cannot account for elements that have been omitted

The Sampling Design Process Determine the Sampling Frame Yet another approach is to adjust the collected by a weighting scheme to counterbalance the sampling frame error Regardless of which approach is adopted, it is important to recognize any sampling frame error that exists, so that inappropriate population inferences can be avoided

The Sampling Design Process Select a Sampling Technique Selecting a sampling technique involves several decisions of a broader nature The researcher must decide to use a Bayesian or traditional sampling approach, to sample with or without replacement, and to use nonprobability or probability sampling

The Sampling Design Process Select a Sampling Technique In the Bayesian approach, the elements are added sequentially. After each unit is added to the sample, the data are collected, sample statistics computed, and sampling costs determined The approach is not widely used in marketing research because it explicitly requires prior information about costs and probabilities, which is not much available

The Sampling Design Process Select a Sampling Technique In the traditional sampling approach, the entire sample is selected before the data collection begins Traditional sampling approach is most commonly used

The Sampling Design Process Select a Sampling Technique In sampling with replacement, an element is selected from the sampling frame and appropriate data are obtained Then the element is placed back in the sampling frame. As a result it is possible for an element to be included in the sample more than once

The Sampling Design Process Select a Sampling Technique In sampling without replacement, once an element is selected for inclusion in the sample, it is removed from the sampling frame and, therefore, cannot be selected again Results from sampling with or without replacement do not vary much unless the sampling frame is large relative to the ultimate sample size

The Sampling Design Process Select a Sampling Technique The most important decision about the choice of a sampling technique is whether to use a probability or nonprobability sampling

The Sampling Design Process Select a Sampling Technique If the sampling unit is different from the element, it is necessary to specify precisely how the elements within the sampling unit should be selected

The Sampling Design Process Select a Sampling Technique In in-home personal interviews and telephone interviews, merely specifying the address or the telephone number may not be sufficient. For example, should the person answering the doorbell or the telephone be interviewed, or someone else in the household?

The Sampling Design Process Select a Sampling Technique Often, more than one person in a household may qualify When a probability sampling technique is being employed, a random selection must be made from all the eligible persons in each household A simple procedure for random selection is the next birthday method

The Sampling Design Process Determine the Sample Size Sample size refers to the number of elements to be included in the study Determining the sample size is complex and involves several quantitative and qualitative consideration Important qualitative factors that should be considered in determining the sample size include:

The Sampling Design Process Determine the Sample Size (i) the importance of the decision, (ii) the nature of the research, (iii) the number of variables, (iv) the nature of analysis, (v) sample sizes used in similar studies, (vi) incident rates, (vii) completion rates, and (viii) resource constraints

The Sampling Design Process Determine the Sample Size In general, for more important decisions, more information is necessary and the information should be obtained more precisely This calls for larger samples, but as the sample size increases, each unit of information is obtained at greater cost

The Sampling Design Process Determine the Sample Size The degree of precision may be measured in terms of the standard deviation of the mean The standard deviation is inversely proportional the square root of the sample size. The larger the sample, the smaller the gain in precision by increasing the sample size by one unit

The Sampling Design Process Determine the Sample Size The nature of research also has an impact on the sample size For exploratory research designs, such as those using qualitative research , the sample size is small For conclusive research, such as descriptive surveys, the sample size is typically large

The Sampling Design Process Determine the Sample Size Likewise, if the data is collected on a large number of variables, larger samples are required The cumulative effects of sampling error across variables are reduced in a large sample

The Sampling Design Process Determine the Sample Size If sophisticated analysis of data using multivariate techniques s required, the sample size should be large The same applies of the data are to analyzed in greater detail, such as at subgroup level or at segments level

The Sampling Design Process Determine the Sample Size Sample size is influenced by the average size of samples in similar studies The same applies of the data are to analyzed in greater detail, such as at subgroup level or at segments level

The Sampling Design Process Determine the Sample Size Incidence rate refers to the rate of occurrence or the percentage of persons eligible to participate in the study Incidence rate determines how many contacts need to be screened for a given sample size requirement

The Sampling Design Process Determine the Sample Size Completion rate denotes the percentage of qualified respondents who complete the interviews It enables researcher to take into account anticipated refusals by people who qualify

The Sampling Design Process Determine the Sample Size Resource constraint should guide the decision on the sample size In any marketing research project, money and time are limited Other constraints include the availability of qualified personnel for data collection

The Sampling Design Process Execute the Sampling Process Execution of sampling process requires a detailed specification of how the sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique, and sample size are to be implemented If households are sampling unit, an operation definition of a household is needed

The Sampling Design Process Execute the Sampling Process Procedures should be specified for vacant housing units and for callbacks in case no one is at home Detailed information must be provided for all sampling design decisions

Sampling Techniques Sampling techniques may be broadly classified as nonprobability and probability

Nonprobability Sampling Sampling Techniques Nonprobability Sampling Nonprobability sampling techniques relies on personal judgment of the researcher rather than chance to select sample elements

Nonprobability Sampling Sampling Techniques Nonprobability Sampling The researcher can arbitrarily or consciously decide what elements to include in the sample Nonprobability sample may yield good estimates of the population characteristics

Nonprobability Sampling Sampling Techniques Nonprobability Sampling The estimates are not statistically projectable to the population Commonly used nonprobability sampling techniques include convenience sampling, judgmental sampling, quota sampling and snowball sampling

In probability sampling, sampling units are selected by chance Sampling Techniques Probability Sampling In probability sampling, sampling units are selected by chance It is possible to prespecify every sample of a given size that could be drawn from the population as well as the probability of selecting each sample

Sampling Techniques Probability Sampling Because sample elements are selected by chance, it is possible to determine the precision of the sample estimates of the characteristics of interest

Sampling Techniques Probability Sampling Confidence intervals, which contain the true population value with a given level of certainty, can be calculated This permits the researcher to make inferences or projections about the target population from which the sample was drawn

Nonprobability Sampling Techniques Convenience Sampling attempts to obtain a sample of convenient elements The selection of sampling unit is left primarily to the interviewer

The sampling units are accessible, easy to measure and cooperative Sampling Techniques Convenience Sampling: Advantages Often, the respondents are selected because they happen to be in the right place at the right time Convenience sampling is least expensive and least time consuming of all sampling techniques The sampling units are accessible, easy to measure and cooperative

Many potential sources of selection bias are present Sampling Techniques Convenience Sampling: Disadvantages Many potential sources of selection bias are present Convenience samples are not representative of any definable population

Sampling Techniques Convenience Sampling: Disadvantages Hence it is not theoretically meaningful to generalize to any population from a convenience sample and convenience samples are not appropriate for marketing research project s involving population inference

But they can be used in generating ideas, insights, or hypotheses Sampling Techniques Convenience Sampling: Disadvantages Convenience samples are not recommended for descriptive or causal research. But they can be used in generating ideas, insights, or hypotheses Convenience samples can be used for focus groups, pretesting questionnaires, or pilot studies

Sampling Techniques Nonprobability Sampling Techniques Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher

Sampling Techniques Judgmental Sampling The researcher, exercising the 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

Sampling Techniques Judgmental Sampling: Examples Common examples of judgmental sampling include (a) test markets selected to determine potential of a new product, (b) purchase engineers selected in industrial marketing research because they are considered to be representative of the company, (c) bellwether precincts selected in voting behavior research,

Sampling Techniques Judgmental Sampling: Examples (d) expert witnesses used in court, and (e) department store selected to test a new merchandising display system

Judgmental sampling is low cost, convenient, and quick Sampling Techniques Judgmental Sampling: Advantages Judgmental sampling is low cost, convenient, and quick

Sampling Techniques Judgmental Sampling: Disadvantages Judgmental sampling is subjective and its value depends entirely on the researcher’s judgment, expertise, and creativity The results may not be directly generalized for populations as the population not defined explicitly

Sampling Techniques Quota Sampling Quota sampling may be viewed as two-stage restricted judgmental sampling

Sampling Techniques Quota Sampling The first stage involves developing control categories, or quotas, of population elements. Quotas are developed on the basis of control characteristics In the second stage, sample elements are selected based on convenience or judgment

Sampling Techniques Snowball Sampling In snowball sampling, an initial group of respondents is selected, usually at random After being interviewed, these respondents are asked to identify others who belong to the target population of interest

Every element is selected independently of every other element Sampling Techniques Simple Random Sampling In simple random sampling (SRS), each element in the population has a known and equal probability of selection Every element is selected independently of every other element

The sample is drawn by a random procedure a sampling frame Sampling Techniques Simple Random Sampling The sample is drawn by a random procedure a sampling frame This method is similar to a “lucky draw” in which names are placed in a container, the container is shaken, and the names of lucky winners are ten drawn out in an unbiased manner

Compile a sampling frame Sampling Techniques Simple Random Sampling: Procedure Compile a sampling frame Assign each element a unique identification number Random numbers are generated Unique ids assigned to each element are matched with the random number generated Elements whose id matched with the random number are selected in the sample

systematic random sample A systematic random sample is obtained by selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained. For example, there are 100 students in your class. You want a sample of 20 from these 100 and you have their names listed on a piece of paper may be in an alphabetical order. If you choose to use systematic random sampling, divide 100 by 20, you will get 5. Randomly select any number between 1 and five. Suppose the number you have picked is 4, that will be your starting number. So student number 4 has been selected. From there you will select every 5th name until you reach the last one, number one hundred. You will end up with 20 selected students.

stratified sample A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income of education. Like any body with ten years of education will be in group A, between 10 and 20 group B and between 20 and 30 group C. These groups are referred to as strata. You can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C.

cluster sample A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be some thing like a village or a school, a state. So you decide all the elementary schools in New York State are clusters. You want 20 schools selected. You can use simple or systematic random sampling to select the schools, then every school selected becomes a cluster. If you interest is to interview teachers on the opinion of some new program which has been introduced, then all the teachers in a cluster must be interviewed. Though very economical cluster sampling is very susceptible to sampling bias. Like for the above case, you are likely to get similar responses from teachers in one school due to the fact that they interact with one another.

A simple random sample A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a least of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly as discussed below. such a procedure is called systematic random sampling.