SAMPLING

The Sampling Design Process

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process

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 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.

Sample

SamplingProcess of separating the representative part from population is known as sampling. The method of selecting a specified

portion, called a sample, from a population, from which information concerning the whole can be inferred.

A portion of the population that represents population characteristics is called as sample. They are the subset

of the population that should represent the entire population. They have similar characteristics of

population

Population SizePopulation Size: Total number of elements in population

Element:Element: Individual member of population

SampleSample: Representative part of the population

Sample frameSample frame: A list of elements from which the sample is actually drawn

.

Sample SizeSample Size: Total number of elements selected from population.

SubjectSubject: Individual member of sample

RespondentRespondent: Who answers the query

Categorization of Sampling method

There are two categories of sampling method.

Probability based:All those sampling methods in which each and every member of the population gets an equal chance to become the part of the sample.

Non probability based:In non-probability based sampling methods each and every member from the population does not get an equal chance of being selected in the sample.

Sampling

Probability

Simple Random stratified cluster

nonsystematic

Proportionate systematic Disproportionate

size variance

Non Probability

quota

snowball

judgment

convenience

Probability based sampling methods

Sampling procedure in which each and every element of population has a fixed probabilistic chance of being selected

for the sample.

Probability

Simple Random stratified cluster

nonsystematic

Proportionate systematic

Disproportionate

size variance

Simple random Sampling Method

With simple random sampling, the probability of selection into the sample is “ known” and equal for all members

of population. Sample is selected in such a way that every element of the

population has a known and equal chance of being chosen for the sample. Also called random sample. The sample is selected from the entire population i.e.

without dividing respondents into groups.

Systematic:

A probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every nth element in succession from the sampling frame.

Eg: pick 3, Then 6 ,9,12,15,18

Nonsystematic:

This implies that every number is selected independently of every other element. This method is equivalent to a lottery system.

Eg: A lucky draw.

Stratified Sampling Method

A probability sampling technique that uses two step process to partition into subpopulation, or strata .

samples are selected from each stratum by a random procedure.

Probability samples that force sample to be more representative of the population. It is obtained by dividing the population into groups called strata,

then simple random samples are taken from each of the strata. It can be done in two ways:

Proportionate & Disproportionate.

Proportionate: (Based on (Based on relationship)relationship)

Size:Group size matters a lot.The bigger the size of the strata the more you select, the smaller the size of strata the less you select.

Variance:It depends on the differences that exists in a group. More the difference more you select, the less the difference less you select.

Steps Involved in Stratified Sampling 1. Divide the population into stratas or groups.2. Identify the population in each strata.3. Select the number of respondents either proportionately or disproportionately. 4. Select final respondents by applying simple random sampling method

Total Population

Male

Female

60 students

10%

40 students

10%

100 students: 10%

6

4

10

• Selecting Numbers of Respondents by Proportionate

Proportionate ( Size )

Larger the size of the group the more we select, the smaller the size of strata the less we select.

Strata-1

Strata-2

• Selecting Numbers of Respondents by Proportionate

Proportionate ( Variance )

More the difference in a group more we select the less the differences in a group the less we select.

Total Population

Male

Female

60 students

40 students

100 students

3

6

d

dd

Here the differences in strata-2 are more than strata-1 and the

relationship is 1:2 so for every one respondent from strata-1

we’ll select two respondents from strata-2 untill the desired

sample size is achieved

Strata-1

Strata-2

Disproportionate

It is the sampling done without any relationship. Here importance formula is used

because the strata size doesn’t reflect the relative proportions of the population. It

depends on the own judgment of the researcher about the importance of each of the strata for the research. You choose the desired

sample size according to your judgment about the importance of the strata in the

research.

Total Population

Male

Female

60 students

40 students

100 students

In this type the Respondent are selected on the Judgment of the Researcher. Researcher decide which group is more important

5

5

Here the researcher thinks that both the strata

are equally important for the research.

Strata-1

Strata-2

Cluster Sampling Method

Population is divided into internally heterogeneous Population is divided into internally heterogeneous subgroups. Some are randomly selected for further subgroups. Some are randomly selected for further study.study.

Advantages:Advantages:Lowest cost per sample especially withLowest cost per sample especially with geographical clusters.geographical clusters.Easy to do without a population list.Easy to do without a population list.

Disadvantages:Disadvantages:Often lower statistical efficiency ( more error) due to Often lower statistical efficiency ( more error) due to subgroups being homogenous rather than being subgroups being homogenous rather than being heterogeneous.heterogeneous.

Cluster ( Area Sampling Method )

DHA

DHAPhase 1

DHA Phase 2

DHA Phase 3

DHAPhase 4

Street 1 Street 2 Street 1 Street 2 Street 1 Street 2 Street 1 Street 2

Khayaban Khayaban Khayaban Khayaban

Non probability based Sampling methods

In non-probability based sampling methods each and every member from the population does not get the equal

chance of being selected in the sample.It rely on the personal judgment or

convenience of the researcher.

Convenience

Convenience samples are sample drawn at the convenience of the researcher. According to most convenient location, time,

etc respondents are selected. Convenience sampling may misrepresent the population.

A sampling procedure that leaves the selection of respondents totally to the field researcher, with no quotas or qualifications imposed. It consists of those units of the population that are

easily accessible.

Judgment

Judgement sampling is a form of non-probability sampling in which the

population elements are selected based on the judgment of the researcher.

In judgment sampling researcher uses his/ her own educated guess or

judgment to identify who will be in the sample.

Snow ball

Snowball sampling is commonly used when it is difficult to identify members of the desired population. Make contact with one or two respondents in the population. Ask these new respondents to identify further new respondents and

so on. And this process of obtaining data by initial respondent ,and then from referral to referral is called as snow

ball.

E.g: Giving the questionnaire to the students who know other students of their batch and then asking them to identify other student whom they know.

Quota

The quota sample establishes a specific quota or percentage for various types of

individuals to be interviewed.

The size of the quota are determined by the researchers belief for relative size of each

class of respondent in the population. Often, quota sampling is used as means of

ensuring convenience sample size

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.

Population Samplecomposition composition

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

____ ____ ____100 100 1000

Strength and weakness of sampling techniques

Convenience

Sampling

Judgmental

Sampling

Quota

sampling

Snow Ball

sampling

strengthstrength weaknessweakness

Least expensive, least time Least expensive, least time consuming, most convenient consuming, most convenient

Selection biasness, sample is Selection biasness, sample is not representative of (P)not representative of (P)

Low cost, convenient , less Low cost, convenient , less time consumingtime consuming

Doesn’t allow generalization, Doesn’t allow generalization, subjective instead of objectivesubjective instead of objective

Sample can be controlled Sample can be controlled from certain characteristics.from certain characteristics.

Selection bias, no assurance Selection bias, no assurance of representative.of representative.

Can estimate rare Can estimate rare characteristicscharacteristics

Time consumingTime consuming

Strength and weakness of sampling techniques

Strength Strength Weakness Weakness

Easily understood,results are Easily understood,results are projectableprojectable

Difficult to construct sampling Difficult to construct sampling frame, expensive, lower frame, expensive, lower precision, no assurance of precision, no assurance of representativerepresentative

Can increase representative Can increase representative ness, easier to implement, than ness, easier to implement, than Srs, Sampling frame not Srs, Sampling frame not necessary.necessary.

Can decrease representativeCan decrease representative

Includes all important Includes all important subpopulation, precision.subpopulation, precision.

Difficult to select relevant Difficult to select relevant stratification variable, stratification variable, expensive,not feasible to expensive,not feasible to verify many variables.verify many variables.

Cost effective ,Cost effective ,

easy implementeasy implement

Low statistical efficiencyLow statistical efficiency

Simple

Random

Systematic

sampling

Stratified

sampling

Cluster

sampling

Factors to determine sample size

1. Cost2. Time3. Importance of decision4. Reliability requirements5. Population size6. Nature of the problem7. Diversity of population

Sample Sizes Used in Research Studies

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

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