Sampling

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APRESENTATION

ON

SAMPLING: DESIGN & PROCEDURES

Presented To:Prof. T. S. Shibin



PRESENTED BY:

P forPOWERFULL



SAMPLE or CENSUS

Population: Aggregate of all the elements that share some common set of characteristics.

Census: Complete involvement of elements of a population.

Sample: Subgroup of the population selected for the participation in the study.



SAMPLE Vs. CENSUSPARAMETER SAMPLE CENSUS

Budget Small Large

Time available Short Long

Population size Large Small

Variance in characteristics Small Large

Cost of sampling errors Low High

Cost of nonsampling errors High Low

Nature of measurement Destructive Nondestructive

Attention to individual cases Yes No



THE SAMPLING DESIGN PROCESS

Define the Target Population

Determine the Sampling Frame

Select a Sampling Technique

Determine the Sample Size

Execute the Sampling Process



Define the Target PopulationTarget population is the collection of elements or objects that possess the information sought by the researcher.

Defining the target population involves translating the problem definition into a precise statement of who should and should not be included in the sample.

Target population should be defined in terms of Elements, Sampling units, Extent and Time.



Determine the Sampling Frame

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.

e.g. Telephone directory, Yellow pages, Customer list etc.

Sampling frame error.



Select a Sampling TechniqueBayesian Approach: Elements are selected sequentially and it incorporates prior information about population parameters, costs and probabilities associated with making wrong decisions.

Traditional Approach: Most commonly used.

Sampling with replacement: After obtaining data from the element, it is placed back in the sampling frame, so it has chance of getting selected again.

Sampling without replacement: After collecting data from element, it is removed from the sampling frame and hence it cannot be selected again.

Probability or Nonprobability sampling.



Determine the Sample SizeSample Size: Number of elements to be included in the study.

Factors for selecting Sampling Size

Importance of the Decision

Nature of Research

Number of Variables

Nature of AnalysisSample sizes used in

similar studies

Incidence Rates

Completion Rates

Resource Constraints



Execute the Sampling Process

It 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.



Hardik



Classification of Sampling Methods

Sampling methods

Probability samples

Systematic

Cluster

Stratified

Simple random

Nonprobability samples

Convenience

Judgement

Snowball

Quota



Disadvantages of Nonprobability Samples

1. Sampling error cannot be computed

2. Representativeness of the sample is not known

3. Results cannot be projected to the population.

Advantages of Nonprobability Samples

4. Cost less than probability

5. Can be conducted more quickly

6. Produces samples that are reasonably representative



Convenience Sampling

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.

Use of students, and members of social organizations.

“People on the street” interviews.

Olympic Convenience.



A Graphical Illustration of Convenience Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Group D happens to assemble at a convenient time and place. So all the elements in this Group are selected. The resulting sample consists of elements 16, 17, 18, 19 and 20. Note, no elements are selected from group A, B, C and E.



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

Purchase engineers selected in industrial marketing research.

Expert witnesses used in court.

Department store study in Metropolitan.



A Graphical Illustration of Judgemental Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

The researcher considers groups B, C and E to be typical and convenient. Within each of these groups one or two elements are selected based on typicality and convenience. The resulting sample consists of elements 8, 10, 11, 13, and 24. Note, no elements are selectedfrom groups A and D.



Quota SamplingQuota 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.

Population Samplecomposition composition

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

____ ____ ____100 100 1000



A Graphical Illustration of Quota Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

A quota of one element from each group, A to E, is imposed. Within each group, one element is selected based on judgment or convenience. The resulting sample consists of elements 3, 6, 13, 20 and 22. Note, one element is selected from each column or group.



Snowball SamplingIn 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.



A Graphical Illustration of Snowball Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Elements 2 and 9 are selected randomly from groups A and B. Element 2 refers elements 12 and 13. Element 9 refers element 18. The resulting sample consists of elements 2, 9, 12, 13, and 18. Note, there are no element from group E.



Kapil



Simple Random Sampling

Each elements in 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.

Sampling Frame or lottery system.



Limitations:Difficult to construct sampling frame.

Results in large geographical areas- data collection is costly.

Lower precision with highest standard error.

May not result in representative sample.



A Graphical Illustration of Simple Random Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Select five random numbers from 1 to 25. The resulting sample consists of population elements 3, 7, 9, 16, and 24. Note, there is no element from Group C.



Systematic SamplingSample is chosen by selecting a random starting point then every ith element is selected.

Sampling interval is selected by N/n.

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.



A Graphical Illustration of Systematic Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Select a random number between 1 to 5, say 2.The resulting sample consists of population 2, (2+5=) 7, (2+5x2=) 12, (2+5x3=)17, and (2+5x4=) 22. Note, all the elements are selected from a single row.



Stratified SamplingTwo step process in which population is partitioned in to sub population or strata.

Strata will be mutually exclusive and collectively exhaustive.

Samples or elements are selected from each stratum.

Objective of stratified sampling is to increase precision without increasing cost.



The variables used in partition the population in to strata is known as stratification variables.

The criteria for selecting variables are homogeneity, heterogeneity, relatedness and cost.

Elements within strata should be homogeneous but in strata should be heterogeneous as possible.

The stratification variables should also be costly.



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.

Both are identical if characteristics of interest has same standard deviation within each stratum.



A Graphical Illustration of Stratified Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Randomly select a number from 1 to 5 for each stratum, A to E. The resultingsample consists of population elements4, 7, 13, 19 and 21. Note, one elementis selected from each column.



Navin



Cluster Sampling

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 Simple Random Sampling.

For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically.



Types of Cluster Sampling

Cluster Sampling

One-StageSampling

MultistageSampling

Two-StageSampling

Simple ClusterSampling

ProbabilityProportionate

to Size Sampling



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 and the probability of selecting a sampling unit in a selected cluster varies inversely with the size of the cluster.



A Graphical Illustration of Cluster Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25



Other Probability Sampling Techniques

Sequential Sampling: A probability sampling technique in which the population elements are sampled sequentially, data collection and analysis are done at each stage, and a decision is made as to whether additional population elements should be sampled.

Double Sampling: A sampling technique in which certain population elements are sampled twice.



ChoosingNonprobability Vs. Probability

Sampling

FactorsConditions Favoring the Use of

Non probability 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



Uses

Nonprobability Sampling: Projection to population not needed.

Probability Sampling: Projection to population required.



Nilesh



A Classification of Internet SamplingInternet Sampling

Online InterceptSampling

Recruited OnlineSampling

Other Techniques

Non Random Random

Panel Non Panel

Recruited Panels

Opt in Panels

Opt in list Rentals



Internet SamplingAdvantages of Internet sampling:

Target respondents can complete the survey at their convenience.

Data collection is inexpensive.

The interview can be administered under software control.

The survey can be completed quickly.



Disadvantages of Internet InterviewingUsers of the internet are not representative of the general population.

No comprehensive and reliable source of email addresses exists.

EXAMPLE-

Customer evaluation

Online purchase evaluation

Student course evaluation



Ethics in Marketing Research

Ethics of the Sponsor

Overt and covert purposes

Dishonesty in dealing with suppliers

Misuse of research information

Ethics of the Supplier

Violating client confidentiality

Improper execution of research

Abuse of Respondents

Falsifying answers



THANK YOU


Sampling

Documents

nonprobability sampling

sampling technique

sampling units

shibin templates page

sampling frame error

convenience sampling

sampling design decisions

population parameters