Determining How to Select a Sample

Determining How toSelect a Sample

Edited & Complied

By

Sanjeev S. MalageAssociate Professor

FMS Department , NIFT, Bangalore

Sanjeev Sadashiv Malage NIFT, Bangalore2

Learning Objectives

1. To understand the concept of sampling.

2. To learn the steps in developing a sampling plan.

3. To understand the concepts of sampling error andnonsampling error.

4. To distinguish between probability samples, andnonprobability samples.

5. To understand sampling implications of surveying over theInternet.


Definition of sampling

Procedure by which some membersof a given population are selected as

representatives of the entire population


Ø Sampling Defined:The process of obtaining informationfrom a subset of a larger group.

Ø A market researcher takes the results from the sample tomake estimates of the larger group.

Ø Sampling a small percentage of a population can result invery accurate estimates.

To understand theconcept of sampling.

The Concept of Sampling


Why do we use samples ?

Get information from large populations– At minimal cost

– At maximum speed

– At increased accuracy

– Using enhanced tools


What we need to know

• Concepts– Representativeness

– Sampling methods

– Choice of the right design


Sampling and representativeness

Sample

Target Population

SamplingPopulation

Target Populationè Sampling Populationè Sample


Step1.Define the

Population ofInterest

Step 2. ChooseData Collection

Method

Step 3.Choose Sampling

Frame

(4)Select a

Sampling Method

Step 5. DetermineSample Size

Step 6. DevelopOperational Plan

Step 7.Execute

Operational Plan

Steps in Developing a Sample Plan


Step One: Defining the Population of InterestSpecifying the characteristics from whom information isneeded.Define the characteristics of those that should beexcluded.

Step Two: Choose Data Collection MethodImpacts for the sampling process.

Step Three: Choosing Sampling FrameA list of elements or members from which we select unitsto be sampled.

To learn the steps indeveloping a sample plan.

Steps In Developing ASampling Plan

Sanjeev Sadashiv Malage, NIFT, Bangalore10

Basic Concepts in Sampling

• Population: the entire group under studyas defined by research objectives– Researchers define populations in

specific terms such as “heads ofhouseholds located in areas servedby the company who are responsiblefor making the decision.”



• Sample: a subset of the populationthat should represent the entire group

• Sample unit: the basic level ofinvestigation

• Census: an accounting of thecomplete population


Step Four: Select a Sampling MethodThe selection will depend on:

• The objectives of the study

• The financial resources available

• Time limitations

• The nature of the problem

To learn the steps indeveloping a sample plan.

Steps In Developing ASampling Plan


Step Five: Determine Sample Size• Available budget

• Rules of thumb

Step Six: Develop of Operational Procedures forSelecting Sample Elements

Specify whether a probability or nonprobabilitysample is being used

Step Seven: Execution the Sampling Plan

The final step of the operational sampling plan

Include adequate checking of specified procedures.

Steps In Developing A Sampling Plan


Samplingmethods

Probabilitysamples

Systematic

Cluster Stratified

Simplerandom

Nonprobabilitysamples

Convenience

Judgement

Snowball

Quota

Classification of Sampling Methods


Two Basic Sampling Methods

• Probability samples: ones in whichmembers of the population have aknown chance (probability) of beingselected into the sample

• Non-probability samples: instances inwhich the chances (probability) ofselecting members from thepopulation into the sample areunknown


Probability Sampling:Simple Random Sampling

• Simple random sampling:

• the probability of being selected intothe sample is “known” and equal forall members of the population– E.g., Blind Draw Method– Random Numbers Method


Simple random sampling

• Principle–Equal chance of drawing each unit

• Procedure–Number all units–Randomly draw units


Probability Sampling:1.Simple Random Sampling

– Advantage:• Known and equal chance of selection

– Disadvantages:• Complete accounting of population

needed• Cumbersome to provide unique

designations to every populationmember


Example: evaluate the prevalence of toothdecay among the 1200 children attendinga school

• List of children attending the school• Children numerated from 1 to 1200• Sample size = 100 children• Random sampling of 100 numbers

between 1 and 1200How to randomly select?





Probability SamplingSystematic Sampling

• Systematic sampling: way to select arandom sample from a directory orlist that is much more efficient thansimple random sampling– Skip interval=population list

size/sample size


Systematic sampling

• N = 1200, and n = 60Þ sampling fraction = 1200/60 = 20

• List persons from 1 to 1200• Randomly select a number between 1

and 20 (ex : 8)Þ 1st person selected = the 8th on

the listÞ 2nd person = 8 + 20 = the 28th

etc .....


Systematic sampling


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

46 47 48 49 50 51 52 53 54 55 ……..


Systematic sampling


Probability SamplingSystematic Sampling

– Advantages:• Approximate known and equal

chance of selection…it is a probabilitysample plan

• Efficiency…do not need to designateevery population member

• Less expensive…faster than SRS– Disadvantage:

• Small loss in sampling precision


Probability SamplingCluster Sampling

• Cluster sampling: method inwhich the population is dividedinto groups, any of which can beconsidered a representativesample– Area sampling


Cluster sampling

• Principle

– Random sample of groups (“clusters”)of units

– In selected clusters, all units orproportion (sample) of units included


Cluster Sampling

• In cluster sampling the population isdivided into subgroups, called“clusters.”

• Each cluster should represent thepopulation.

• Area sampling is a form of clustersampling – the geographic area isdivided into clusters.


Cluster Sampling

• One cluster may be selected torepresent the entire area with theone-step area sample.

• Several clusters may be selectedusing the two-step area sample.


A Two-Step Cluster Sample

• A two-step cluster sample (samplingseveral clusters) is preferable to aone-step (selecting only one cluster)sample unless the clusters arehomogeneous.


Example: Cluster sampling

Section 4

Section 5

Section 3

Section 2Section 1


cluster sampling

To evaluate vaccination coverage:

• Without list of persons

• Total population of villages

• Randomly choose 30 clusters

• 30 cluster of 7 children each= 210 children


Probability SamplingCluster Sampling

– Advantage:• Economic efficiency…faster and

less expensive than SRS– Disadvantage:

• Cluster specification error…themore homogeneous the clusters,the more precise the sampleresults


Stratified Sampling

• When the researcher knows theanswers to the research question arelikely to vary by subgroups…


Probability SamplingStratified Sampling

• Stratified sampling: method in whichthe population is separated intodifferent strata and a sample is takenfrom each stratum– Proportionate stratified sample– Disproportionate stratified sample


Stratified sampling

• Principle :

–Classify population into internallyhomogeneous subgroups (strata)

–Draw sample in each strata–Combine results of all strata


Stratified Sampling

– Research Question: “To what extentdo you value your college degree?”Answers are on a five point scale: 1=“Not valued at all” and 5= “Veryhighly valued”• We would expect the answers to vary

depending on classification. Freshersare likely to value less than Alumni. Wewould expect the mean scores to behigher as classification goes up.


Stratified Sampling

– Research Question: “To what extentdo you value your college degree?”• We would also expect there to be more

agreement (less variance) asclassification goes up. That is, seniorsshould pretty much agree that there isvalue. Freshers will have lessagreement.


Stratified Sampling

• Why is stratified sampling moreaccurate when there are skewedpopulations?– The less variance in a group, the less

sample size it takes to produce aprecise answer.

– Why? If 99% of the population (lowvariance) agreed on the choice of BrandA, it would be easy to make a preciseestimate that the population preferredBrand A even with a small sample size.


Stratified Sampling

– But, if 33% chose Brand A, and 23%chose B, and so on (high variance) itwould be difficult to make a preciseestimate of the population’s preferredbrand…it would take a larger samplesize…


Stratified Sampling

– Stratified sampling allows theresearcher to allocate more sample sizeto strata with less variance and lesssample size to strata with less variance.Thus, for the same sample size, moreprecision is achieved.

– This is normally accomplished bydisproportionate sampling. Seniorswould be sampled LESS than theirproportionate share of the populationand freshmen would be sampled more.


Probability SamplingStratified Sampling

– Advantage:• More accurate overall sample of

skewed population…see next slidefor WHY

– Disadvantage:• More complex sampling plan

requiring different sample size foreach stratum


Nonprobability Sampling

• With nonprobability samplingmethods selection is not based onfairness, equity, or equal chance.– Convenience sampling– Judgment sampling– Referral sampling– Quota sampling



• May not be representative but theyare still used very often. Why?– Decision makers want fast,

relatively inexpensive answers…nonprobability samples are fasterand less costly than probabilitysamples.



• May not be representative but theyare still used very often. Why?– Decision makers can make a

decision based upon what 100 or200 or 300 people say…they don’tfeel they need a probability sample.



• Convenience samples: samplesdrawn at the convenience of theinterviewer– Error occurs in the form of

members of the population who areinfrequent or nonusers of thatlocation



• Judgment samples: samples thatrequire a judgment or an “educatedguess” as to who should representthe population– Subjectivity enters in here, and

certain members will have asmaller chance of selection thanothers



• Referral samples (snowball samples):(snowball samples):samples which require respondentsto provide the names of additionalrespondents– Members of the population who are

less known, disliked, or whoseopinions conflict with therespondent have a low probabilityof being selected



• Quota samples: samples that use aspecific quota of certain types ofindividuals to be interviewed– Often used to ensure that

convenience samples will havedesired proportion of differentrespondent classes


Online Sampling Techniques

• Random online intercept sampling:relies on a random selection of Website visitors

• Invitation online sampling: is whenpotential respondents are alerted thatthey may fill out a questionnaire thatis hosted at a specific Web site


Online Sampling Techniques

• Online panel sampling: refers toconsumer or other respondent panelsthat are set up by marketing researchcompanies for the explicit purpose ofconducting surveys withrepresentative samples



• Sampling error: any error in a surveythat occurs because a sample is used

• A sample frame: a master list of theentire population

• Sample frame error: the degree towhich the sample frame fails toaccount for all of the population…atelephone book listing does notcontain unlisted numbers

Documents

Determining How to Select a Sample