Chapter Eleven

Sampling:Design and Procedures

The objective of most marketing research projects is to obtain information about the characteristics or parameters of a population

Population: The aggregate of all the elements, sharing some common set of characteristics, that comprises the universe for the purpose of the marketing research problem

Sample or Census

Census: A complete enumeration of the elements of a population or study subjects

Sample: A subgroup of the elements of the population selected for participation in the study

Sample vs. CensusTable 11.1

Conditions Favoring the Use of

Type of Study

Sample Census

1. Budget

Small

Large

2. Time available

Short Long

3. Population size

Large Small

4. Variance in the characteristic

Small Large

5. Cost of sampling errors

Low High

6. Cost of nonsampling errors

High Low

7. Attention to individual cases Yes No

The Sampling Design ProcessFig. 11.1

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process

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 target population should be defined in terms of elements, sampling units, extent, and time.

◦ An element is the object about which or from which the information is desired, e.g., 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.

◦ Extent refers to the geographical boundaries.◦ Time is the time period under consideration.

Define the Target Population

A sampling frame is a representation of the elements of the target population

It consists of a list or set of directions for identifying the target population

Example – Telephone Directory, Mailing lists etc

Determine the Sampling Frame

Bayesian Approach the elements are selected sequentially

Sampling with Replacement

Sampling Without Replacement

Select a Sampling Technique

Sample size refers to the number of elements to be included in the study

Determining the sample size is complex and involves several qualitative and quantitative considerations

In general, for more important decisions, more information is necessary and the information should be obtained more precisely

Nature of Research also has some impact on the size

Determine the Sample Size

Important qualitative factors in determining the sample size

◦ the importance of the decision◦ the nature of the research◦ the number of variables◦ the nature of the analysis◦ sample sizes used in similar studies◦ incidence rates◦ completion rates◦ resource constraints

Define the Target Population

Sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique and sample size are to be implemented

Execute the Sampling Process

Sample Sizes Used in Marketing Research Studies

Table 11.2

Type of Study

Minimum Size Typical Range

Problem identification research (e.g. market potential)

500

1,000-2,500

Problem-solving research (e.g. pricing)

200 300-500

Product tests

200 300-500

Test marketing studies

200 300-500

TV, radio, or print advertising (per commercial or ad tested)

150 200-300

Test-market audits

10 stores 10-20 stores

Focus groups

2 groups 4-12 groups

Sampling is cheaper than a census survey

Both the execution of the field work and the analysis of the results can be carried out speedily

Sampling results in greater economy of effort

A sample survey enables the researcher to collect more detailed information

Quality of the interviewing, supervision and other related activities can be better

Advantages of Sampling

It fails to provide information on individual account

It gives rise to many types of errors

The extrapolation or generalization may not work some times

Limitations or Disadvantages

Classification of Sampling TechniquesFig. 11.2

Sampling Techniques

NonprobabilitySampling

Techniques

ProbabilitySampling

Techniques

ConvenienceSampling

JudgmentalSampling

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Other SamplingTechniques

Simple RandomSampling

Non Probability Sampling- Sampling techniques that do not use chance selection procedures.

They rely on personal judgment of the researcher

Probability sampling – A sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample

Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time.

The selection of sampling units is left primarily to the interviewer

◦ use of students, and members of social organizations◦ mall intercept interviews without qualifying the

respondents◦ department stores using charge account lists◦ “people on the street” interviews

Convenience Sampling

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

Convenience samples are not representative of any definable population

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 element to be included in the sample

◦ test markets◦ purchase engineers selected in industrial marketing

research ◦ bellwether precincts selected in voting behavior

research◦ expert witnesses used in court

Judgmental Sampling

Quota sampling may be viewed as two-stage restricted judgmental sampling. ◦ The first stage consists of developing control categories, or quotas,

of population elements. ◦ In the second stage, sample elements are selected based on

convenience or judgment.

Quota sampling involves fixation of certain quotas, which are to be fulfilled by the interviewer

Population Samplecomposition composition

ControlCharacteristic Percentage Percentage NumberSex Male 48 48 480 Female 52 52 520

____ ____ ____100 100 1000

Quota Sampling

Example Quota Sampling Project Telecom

Occupation/Age-Group

Farmers Salaried(Govt.) Salaried(Private) Students Home-makers Business-MenRetired/Unem

ployedTotal

18 - 24 50 5024 - 35 5 15 15 15 15 6536 - 45 15 15 15 15 15 7546 - 60 15 15 15 10 10 65

60 & above 10 5 5 45 65Total 45 45 45 50 45 45 45 320

Note: We should have atleast 30 samples in each circle, who owns the "Smart-phones" across categoriesNote: Try and Cover the respondents across all SEC levels

..\Telecom questionnaire_23May13_v6.docx

It is economical

It is administratively convenient

When the field is to be done quickly it is the most appropriate method

It is independent of the existence of sampling frames

Advantages

It is not based on random selection

It may not be possible to get a representative quota within the sample

Since too much of latitude is given to interviewers, the quality of work suffers if they are not competent

It may be extremely difficult to supervise and control the field investigation under quota sampling

Disadvantages

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.

◦ Subsequent respondents are selected based on the referrals.

Snowball Sampling

Are you satisfied with your Mosaic Glasses?

Tera Emotional Atyaachar

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Practical's at Dexter

Probability sampling techniques vary in terms of sampling efficiency

Trade off between Sampling cost and Precision

The researcher should strive for the most efficient sampling design, subject to the budget allocated

Probability Sampling Techniques

They vary in terms of sampling efficiency

Trade off between sampling cost and precision

Every element is selected independently of every other element

The sample is drawn from a random procedure from a sampling frame

Probability Sampling Techniques

Each element in the population has a known and equal probability of selection.

Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.

This implies that every element is selected independently of every other element.

Simple Random Sampling

The sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.

The sampling interval, i, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.

When the ordering of the elements is related to the characteristic of interest, systematic sampling increases the representativeness of the sample.

If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample.

For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, i, is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.

Systematic Sampling

A two-step process in which the population is partitioned into subpopulations, or strata.

The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted.

Next, elements are selected from each stratum by a random procedure, usually SRS.

A major objective of stratified sampling is to increase precision without increasing cost.

Stratified Sampling

A variable used to partition population into strata are referred to as “Stratification”

Variables usually used for stratification include demographic characteristics, type of customer, size of firm, type of industry etc

The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible.

The stratification variables should also be closely related to the characteristic of interest.

Finally, the variables should decrease the cost of the stratification process by being easy to measure and apply.

In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate to the relative size of that stratum in the total population.

In disproportionate stratified sampling, the size of the sample from each stratum is proportionate to the relative size of that stratum and to the standard deviation of the distribution of the characteristic of interest among all the elements in that stratum.

Stratified Sampling

For example, imagine you want to create a council of 20 employees that will meet and recommend possible changes to the employee handbook. Let's say 40% of your employees are in Sales and Marketing, 30% in Customer Service, 20% of your employees are in IT, and 10% in Finance. You will randomly select 8 people from Sales and Marketing, 6 from Customer Service, 4 from IT, and 2 from Finance. As you can see, each number you pick is proportionate to the overall percentage of people in each category (e.g., 40% = 8 people).

Read more: http://www.alleydog.com/glossary/definition.php?term=Proportionate+Sampling#ixzz2xawOKRDH

Proportionate Sampling Example

The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters.

Then a random sample of clusters is selected, based on a probability sampling technique such as SRS.

For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage).

Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. Ideally, each cluster should be a small-scale representation of the population.

In probability proportionate to size sampling, the clusters are sampled with probability proportional to size. In the second stage, the probability of selecting a sampling unit in a selected cluster varies inversely with the size of the cluster.

Cluster Sampling

Types of Cluster SamplingFig. 11.3 Cluster Sampling

One-StageSampling

MultistageSampling

Two-StageSampling

Simple ClusterSampling

ProbabilityProportionate

to Size Sampling

In some cases, several levels of cluster selection may be applied before the final sample elements are reached. For example, household surveys conducted by the Australian Bureau of Statistics begin by dividing metropolitan regions into 'collection districts' and selecting some of these collection districts (first stage). The selected collection districts are then divided into blocks, and blocks are chosen from within each selected collection district (second stage). Next, dwellings are listed within each selected block, and some of these dwellings are selected (third stage)

Multistage Cluster Sampling Example

Potential Sources of Error inResearch Designs

Surrogate Information ErrorMeasurement ErrorPopulation Definition ErrorSampling Frame ErrorData Analysis Error

Respondent Selection ErrorQuestioning ErrorRecording ErrorCheating Error

Inability ErrorUnwillingness Error

Fig. 3.2Total Error

Non-sampling Error

Random Sampling Error

Non-response Error

Response Error

Interviewer Error

Respondent Error

Researcher Error

The total error is the variation between the true mean value in the population of the variable of interest and the observed mean value obtained in the marketing research project.

Random sampling error is the variation between the true mean value for the population and the true mean value for the original sample.

Non-sampling errors can be attributed to sources other than sampling, and they may be random or nonrandom: including errors in problem definition, approach, scales, questionnaire design, interviewing methods, and data preparation and analysis. Non-sampling errors consist of non-response errors and response errors.

Errors in Marketing Research

Non-response error arises when some of the respondents included in the sample do not respond.

Response error arises when respondents give inaccurate answers or their answers are misrecorded or misanalyzed.

Response errors can be made by researchers, interviewers or respondents

Errors in Marketing Research

Surrogate Information error may be defined as the variation between the information needed for the marketing research problem and the information sought by the researcher

Consumer choice and consumer preference

Measurement error may be defined as the variation between the information sought and the information generated by the measurement process employed by the researcher

Population Definition error may be defined as the variation between the actual population relevant to the problem at hand and the population as defined by the researcher

Sampling Frame Error may be defined as the variation between the population defined by the researcher and the population as implied by the sampling frame

Data analysis error encompasses errors that occur while raw data from questionnaires are transformed into research findings

Example – Inappropriate sampling error

Respondent Selection error occurs when interviewers select respondents other than those specified by the sampling design or in a manner inconsistent with the sampling design

Questioning error denotes errors made in asking questions of the respondents or in not probing when more information is needed

Example- While asking questions the investigator may not use the exact wording as given in the questionnaire

Recording Error arises due to errors in hearing, interpreting, and recording the answers given by the respondents

Cheating Error arises when the interviewer fabricates answers to a part or all of the interview

Inability Error results from the respondents inability to provide accurate answers

Respondents may provide inaccurate answers because of unfamiliarity, fatigue, boredom, faulty recall, question format, content etc

Unwillingness error arises from the respondents unwillingness to provide accurate information

A common form of cluster sampling in which the cluster consists of geographic area such as countries, housing tracts, blocks or other area descriptions

Area Sampling

Technique Strengths WeaknessesNonprobability Sampling Convenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability sampling Simple random sampling(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.

Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, costeffective

Imprecise, difficult to compute andinterpret results

Table 11.3

Strengths and Weaknesses of Basic Sampling Techniques

Procedures for Drawing Probability SamplesFig. 11.4

Simple Random Sampling

1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N (pop. size)

3. Generate n (sample size) different random numbers between 1 and N

4. The numbers generated denote the elements that should be included in the sample

Procedures for DrawingProbability SamplesFig. 11.4 cont. Systematic

Sampling1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N (pop. size)

3. Determine the sampling interval i:i=N/n. If i is a fraction, round to the nearest integer

4. Select a random number, r, between 1 and i, as explained in simple random sampling

5. The elements with the following numbers will comprise the systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i

1. Select a suitable frame

2. Select the stratification variable(s) and the number of strata, H

3. Divide the entire population into H strata. Based on the classification variable, each element of the population is assigned to one of the H strata

4. In each stratum, number the elements from 1 to Nh (the pop. size of stratum h)

5. Determine the sample size of each stratum, nh, based on proportionate or disproportionate stratified sampling, where

6. In each stratum, select a simple random sample of size nh

Procedures for DrawingProbability SamplesFig. 11.4 cont.

nh = n

h=1

H

Stratified Sampling

Procedures for DrawingProbability SamplesFig. 11.4 cont.

Cluster Sampling

1. Assign a number from 1 to N to each element in the population2. Divide the population into C clusters of which c will be included in the sample3. Calculate the sampling interval i, i=N/c (round to nearest integer)4. Select a random number r between 1 and i, as explained in simple random sampling5. Identify elements with the following numbers: r,r+i,r+2i,... r+(c-1)i6. Select the clusters that contain the identified elementsinterval i*.

7. Select sampling units within each selected cluster based on SRS or systematic sampling

8. Remove clusters exceeding sampling interval i. Calculate new population size N*, number of clusters to be selected C*= C-1, and new sampling

Procedures for Drawing Probability Samples

Repeat the process until each of the remaining clusters has a population less than the sampling interval. If b clusters have been selected with certainty, select the remaining c-b clusters according to steps 1 through 7. The fraction of units to be sampled with certainty is the overall sampling fraction = n/N. Thus, for clusters selected with certainty, we would select ns=(n/N)(N1+N2+...+Nb) units. The units selected from clusters selected under PPS sampling will therefore be n*=n- ns.

Cluster Sampling

Fig. 11.4 cont.

Sequential Sampling

Double Sampling – Two phase sampling

Other Sampling Methods

Choosing Nonprobability vs. Probability Sampling

Conditions Favoring the Use of

Factors

Nonprobability sampling

Probability sampling

Nature of research

Exploratory

Conclusive

Relative magnitude of sampling and nonsampling errors

Nonsampling errors are larger

Sampling errors are larger

Variability in the population

Homogeneous (low)

Heterogeneous (high)

Statistical considerations

Unfavorable Favorable

Operational considerations Favorable Unfavorable

Table 11.4 cont.

Concept tests, package tests, name tests and copy tests where projections to the population are not generally needed

Highly accurate estimate of market share or sales volume etc

Uses of Non Probability and Non Probability

The respondents may not be representative

The internet present with only some part of population

Some products online interviewing is not a feasible option

Online sampling techniques

Internet Sampling

Any Questions?