sampling design & procedure. population the aggregate of the all the elements, sharing some...

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


• 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


• 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


• 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


• Nonsampling errors are found to be major contributor to the total error

• Whereas random sampling errors have been relatively small in magnitude


• Hence, in most cases, accuracy considerations would favor a sample over a census


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


• 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


• 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

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 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



• 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



• 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



• 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



• 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

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



• Treating sampling frame error can be done in at least three ways



• 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



• Although this approach is simplistic, it does prevent the researcher from being mislead about the actual population being investigated



• Another way is to account for sampling frame error by screening the respondents in the data-collection phase



• 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



• 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



• 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

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



• In the traditional sampling approach, the entire sample is selected before the data collection begins

• Traditional sampling approach is most commonly used



• 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



• 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 most important decision about the choice of a sampling technique is whether to use a probability or nonprobability sampling



• 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



• 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?



• 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



• 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:

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



i. 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 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



ii. 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



iii. 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



iv. 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



v. 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



vi. 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



vii. 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



viii. 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



• 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

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

Execute the Sampling Process

Sampling Techniques

• Sampling techniques may be broadly classified as nonprobability and probability

Sampling Techniques

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

Nonprobability Sampling

Sampling Techniques

• The researcher can arbitrarily or consciously decide what elements to include in the sample

• Nonprobability sample may yield good estimates of the population characteristics


Sampling Techniques

• The estimates are not statistically projectable to the population

• Commonly used nonprobability sampling techniques include convenience sampling, judgmental sampling, quota sampling and snowball sampling


Sampling Techniques

• 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

Probability Sampling

Sampling Techniques

• 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

• 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


Sampling Techniques:

• Convenience Sampling attempts to obtain a sample of convenient elements

• The selection of sampling unit is left primarily to the interviewer

Nonprobability Sampling Techniques

Sampling Techniques

• 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

Convenience Sampling: Advantages

Sampling Techniques

• Many potential sources of selection bias are present

• Convenience samples are not representative of any definable population

Convenience Sampling: Disadvantages

Sampling Techniques

• 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


Sampling Techniques

• 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

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

Nonprobability Sampling Techniques

Sampling Techniques

• 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

Judgmental Sampling

Sampling Techniques

• 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,

Judgmental Sampling: Examples

Sampling Techniques

• (d) expert witnesses used in court, and (e) department store selected to test a new merchandising display system

Judgmental Sampling: Examples

Sampling Techniques

• Judgmental sampling is low cost, convenient, and quick

Judgmental Sampling: Advantages

Sampling Techniques

• 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

Judgmental Sampling: Disadvantages

Sampling Techniques

• Quota sampling may be viewed as two-stage restricted judgmental sampling

Quota Sampling

Sampling Techniques

• 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

Quota Sampling

Sampling Techniques

• 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

Snowball Sampling

Sampling Techniques

• 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

Simple Random Sampling

Sampling Techniques

• 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

Simple Random Sampling

Sampling Techniques

• 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

Simple Random Sampling: Procedure

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.

sampling design & procedure. population the aggregate of the all the elements, sharing some...

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sampling process slide

sampling frame slide

target population

census slide

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sampling units

sampling techniques

sampling frame error