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In quota samplinz, the population is first segmented into mutually exclusive sub-groups, just as

in stratified sampling. Then judgment is used to select the subjects or units from each segment based

on a specified proportion. For example, an interviewer may be told to sample 200 females and{

00

males between the age of 45 and 60.

It is this second step which makes the technique one of non-probabili ty sampling. In quota sampling,

the selection of the sample is non-random unlike random sampling and can often be found unreliable.

For example interviewers might be tempted to interview those people in the street who look most

helpful, or may choose to use accidental sampling to question those which are closest to them, for

time-keeping sake. The problem is that these samples may be biased because not everyone gets a

chance of selection. This non-random element is i ts greatest weakness and quota versus probability

has been a matter of controversy for many years.

Quota sampling is useful when time is limited, sampling frame is not available, research budget is

very tight or when detailed accuracy is not important. You can also choose how many of each

category is selected.

A quota sample is a convenience sample with an effort made to insure a certain distribution of

demographic variables. Subjects are recruited as they arrive and the researcher will assign them to

demographic groups based on variables like age and gender. When the quota for a given

demographic group is filled, the researcher will stop recruiting subjects from that particular group.

This is the non probability version of stratified sampling. Subsetsare chosen and then either

convenience or judgment sampling is used to choose people from each subset.

Stratified sampling is probably the most commonly used probability method. Subsets of the population

are created so that each subset has a common characteristic, such as gender. Random sampling

chooses a number of subjects from each subset.

Simple random sampleFrom Wikipedia, the free encyclopedia

This article does not cite any references or sources.Please help improve this article by adding citations to reliable sources. Unsourced material may

be challenged and removed. (April 2007)

In statistics, a simple random sample is a subset ofindividuals (a sample) chosen from a larger set

(a population). Each individual is chosenrandomly and entirely by chance, such that each individual

has the same probability of being chosen at any stage during the sampling process, and each subset

ofk individuals has the same probabili ty of being chosen for the sample as any other subset

ofk individuals (Yates, Daniel S.; David S. | oore, Daren S. Starnes (2008). The Practice of Statistics,


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3rd

Ed..} ~eeman. ISBN97

7

7

73

9

.

.

i

processand techniue is

nownassimple

random sampling, andshouldnot

econfusedwithandom Sampling.

Insmall populationsandoften in largeones, suchsampling is tpicall

done "withoutreplacement"

'S

S

'

, i.e., onedeli

eratelavoidschoosinganymemberof thepopulationmore thanonce.

Althoughsimplerandomsamplingcanbeconductedwithreplacement instead, this is lesscommon

andwouldnormallybedescribedmore fullyassimplerandomsamplingwith

replacement'S

S

'

. Samplingdonewithout replacement isno longer independent, but still

satisfiesexchangeability, hencemanyresultsstill hold.urther, forasmall sample froma large

population, samplingwithout replacement isapproximately thesameassamplingwithreplacement,

since theoddsofchoosing thesamesample twice is low.

Anunbiasedrandomselectionof individuals is important so that in the longrun, thesample

represents thepopulation. However, thisdoesnot guarantee that aparticularsample isaperfect

representationof thepopulation. Simplerandomsamplingmerelyallowsone todrawexternallyvalid

conclusionsabout theentirepopulationbasedon thesample.

onceptually, simplerandomsampling is thesimplest of theprobabilitysampling techni

ues. It

requiresacompletesampling frame, whichmaynot beavailableorfeasible toconstruct forlarge

populations. Even ifacomplete frame isavailable, moreefficient approachesmaybepossible ifother

useful information isavailableabout theunits in thepopulation.

Advantagesare that it is freeofclassificationerror, and it requiresminimumadvancenowledgeof

thepopulationotherthan the frame. Itssimplicityalsomakes it relativelyeasy to interpret data

collectedvia S

S.orthesereasons, simplerandomsamplingbest suitssituationswherenot much

information isavailableabout thepopulationanddatacollectioncanbeefficientlyconductedon

randomlydistributed items, orwhere thecost ofsampling issmall enough tomakeefficiency less

important thansimplicity. If theseconditionsarenot true,stratifiedsamplingorclustersamplingmay

beabetterchoice.

[edit] istinctionbetweenasystematicrandomsampleandasimple

randomsample

Inasimplerandomsample, onepersonmust takearandomsample fromapopulation, andnot have

anyorder inwhichonechooses thespecific individual.

Let usassumeyouhadaschool with students, dividedequally intoboysandgirls, andyou

wanted toselect

of them forfurtherstudy. Youmight put all theirnames inabucket and thenpull

namesout. Not onlydoeseachpersonhaveanequal chanceofbeingselected, wecanalso

easilycalculate theprobabilityofagivenpersonbeingchosen, sinceweknow thesamplesie

n)

and thepopulation

N)and it becomesasimplematterofdivision:

n/N or

/

=.

%)


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Scale social sciences) rom

ikipedia, the freeencyclopedia

In thesocial sciences, scaling is theprocessofmeasuringororderingentitieswithrespect to

quantitativeattributesor traits.orexample, ascaling techniquemight involveestimating individuals'

levelsofextraversion, or theperceivedqualityofproducts.ertainmethodsofscalingpermit

estimationofmagnitudesonacontinuum, whileothermethodsprovideonly forrelativeorderingof the

entities.

Seelevel ofmeasurement foranaccount ofqualitativelydifferent kindsofmeasurement scales.

Contents

[hide]

1 Comparati e and noncomparati e scaling

2 Composite measures

3 Data types

4 Scale construction decisions

5 Comparati e scaling techniques

6 Non-comparati e scaling techniques

7 Scale evaluation

8 See also

9 References

10 Lists of related topics

11 Links

[edit] omparativeandnoncomparativescaling

ithcomparativescaling, the itemsaredirectlycomparedwitheachother

example :

oyou

preferPepsior

oke?). Innoncomparativescalingeach item isscaled independentlyof theothers

example : Howdoyou feel about

oke?).

[edit] ompositemeasures

ompositemeasuresofvariablesarecreatedbycombining twoormore

separateempiricalindicators intoasinglemeasure.

ompositemeasuresmeasurecomplexconcepts

moreadequately thansingle indicators, extend therangeofscoresavailableandaremoreefficient at

handlingmultiple items.


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Inaddition toscales, thereare twoothertypesofcompositemeasures. Indexesaresimilartoscales

except multiple indicatorsofavariablearecombined intoasinglemeasure. The indexofconsumer

confidence, forexample, isacombinationofseveral measuresofconsumerattitudes. A typology is

similartoan indexexcept thevariable ismeasuredat thenominal level.

Indexesareconstructedbyaccumulatingscoresassigned to individual attributes, whilescalesare

constructed through theassignment ofscores topatternsofattributes.

hile indexesandscalesprovidemeasuresofasingledimension, typologiesareoftenemployed to

examine the intersectionof twoormoredimensions. Typologiesareveryuseful analytical toolsand

canbeeasilyusedasindependent variables, althoughsince theyarenot unidimensional it isdifficult

touse themasadependent variable.

[edit] ata types

The typeof informationcollectedcan influencescaleconstruction.

ifferent typesof informationare

measured indifferent ways.

. Somedataaremeasuredat thenominal level. That is, anynumbersusedaremere labels :

theyexpressnomathematical properties. Examplesare SKU inventorycodesand UP

bar

codes.

. Somedataaremeasuredat theordinal level. Numbers indicate therelativepositionof items,

but not themagnitudeofdifference. Anexample isapreferenceranking.

3. Somedataaremeasuredat theinterval level. Numbers indicate themagnitudeofdifference

between items, but there isnoabsoluteeropoint. Examplesareattitudescalesandopinion

scales.

. Somedataaremeasuredat theratio level. Numbers indicatemagnitudeofdifferenceand

there isa fixederopoint.

atioscanbecalculated. Examples include: age, income, price,

costs, salesrevenue, salesvolume, andmarket share.

[edit]Scaleconstructiondecisions

hat level ofdata is involved

nominal, ordinal, interval, orratio)?

hat will theresultsbeused for?

Shouldyouuseascale, index, or typology?

hat typesofstatistical analysiswouldbeuseful?

Shouldyouuseacomparativescaleoranoncomparativescale?

Howmanyscaledivisionsorcategoriesshouldbeused

to

;

to 7; 3 to +3)?

Should therebeanoddorevennumberofdivisions?

ddgivesneutral centervalue; even

forcesrespondents to takeanon-neutral position.)

hat should thenatureanddescriptivenessof thescale labelsbe?


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hat should thephysical formorlayout of thescalebe?

graphic, simple linear, vertical,

hori

ontal)

Shouldaresponsebe forcedorbe left optional?

[edit] omparativescaling techniques

Pairwisecomparisonscale arespondent ispresentedwith two itemsat a timeandasked to

select one

example :oyoupreferPepsi or

oke?). This isanordinal level techniquewhena

measurement model isnot applied. Krusand Kennedy

977)elaborated thepairedcomparison

scalingwithin theirdomain-referencedmodel. The BradleyTerryLuce

BTL)model

Bradleyand

Terry, 9

; Luce,9

9)canbeapplied inorder toderivemeasurementsprovided thedata

derived frompairedcomparisonspossessanappropriatestructure. Thurstone'sLawof

comparative judgmentcanalsobeapplied insuchcontexts.

aschmodelscaling respondents interact with itemsandcomparisonsare inferredbetween

items from theresponses toobtainscalevalues.

espondentsaresubsequentlyalsoscaledbasedon theirresponses to itemsgiven the itemscalevalues. The aschmodel hasaclose

relation to the BTL model.

Rank-orderscale arespondent ispresentedwithseveral itemssimultaneouslyandasked to

rank them

example :

ate the followingadvertisements from

to

.). This isanordinal level

technique.

Bogardussocial distancescale measures thedegree towhichaperson iswilling toassociate

withaclassor typeofpeople. It askshowwilling therespondent is tomakevariousassociations.

Theresultsarereduced toasinglescoreonascale. Therearealsonon-comparativeversionsof

thisscale.

Q-Sortscale Up to

itemsaresorted intogroupsbasedarank-orderprocedure.

Guttmanscale This isaprocedure todeterminewhetheraset of itemscanberank-orderedon

aunidimensional scale. It utilies the intensitystructureamongseveral indicatorsofagiven

variable. Statementsare listed inorderof importance. Therating isscaledbysummingall

responsesuntil the first negativeresponse in the list. The Guttmanscale isrelated to

asch

measurement; specifically,aschmodelsbring the Guttmanapproachwithinaprobabilistic

framework.

Constantsum scale arespondent isgivenaconstant sumofmoney, script, credits, orpoints

andasked toallocate these tovarious items example : Ifyouhad Yen tospendon food

products, howmuchwouldyouspendonproduct A, onproduct B, onproduct , etc.). This isan

ordinal level technique.

Magnitudeestimationscale Inapsychophysicsprocedure inventedbyS. S. Stevenspeople

simplyassignnumbers to thedimensionof judgment. Thegeometricmeanof thosenumbers

usuallyproducesapower lawwithacharacteristicexponent. Incross-modalitymatching instead

ofassigningnumbers, peoplemanipulateanotherdimension, suchas loudnessorbrightness to


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match the items. Typically theexponent of thepsychometric functioncanbepredicted from the

magnitudeestimationexponentsofeachdimension.

[edit]Non-comparativescaling techniques

Continuousrating scale

alsocalled thegraphicratingscale) respondentsrate itemsby

placingamarkona line. The line isusually labeledat eachend. Therearesometimesaseriesof

numbers, calledscalepoints,

say, fromero to

)under the line. Scoringandcodification is

difficult.

Likertscale espondentsareasked to indicate theamount ofagreement ordisagreement

fromstronglyagree tostronglydisagree)ona five- tonine-point scale. Thesame format isused

formultiplequestions. Thiscategorical scalingprocedurecaneasilybeextended toamagnitude

estimationprocedure that uses the full scaleofnumbersrather thanverbal categories.

Phrasecompletionscales

espondentsareasked tocompleteaphraseonan

-point

responsescale inwhich

represents theabsenceof the theoretical construct and

representsthe theori

edmaximumamount of theconstruct beingmeasured. Thesamebasic format isused

formultiplequestions.

Semantic differential scale espondentsareasked torateona 7 point scalean itemon

variousattributes. Eachattributerequiresascalewithbipolarterminal labels.

Stapel scale This isaunipolarten-point ratingscale. It ranges from + to andhasnoneutral

eropoint.

Thurstonescale This isascaling technique that incorporates the intensitystructureamong

indicators.

Mathematically derived scale esearchers inferrespondentsevaluationsmathematically.

Twoexamplesaremulti dimensional scalingandconjoint analysis.

[edit]Scaleevaluation

Scalesshouldbe tested forreliability, generali

ability, andvalidity. Generali

ability is theability to

make inferences fromasample to thepopulation, given thescaleyouhaveselected.eliability is the

extent towhichascalewill produceconsistent results. Test-retest reliabilitycheckshowsimilarthe

resultsare if theresearch isrepeatedundersimilarcircumstances. Alternative formsreliabilitychecks

howsimilartheresultsare if theresearch isrepeatedusingdifferent formsof thescale. Internal

consistencyreliabilitycheckshowwell the individual measures included in thescaleareconverted

intoacompositemeasure.

Scalesand indexeshave tobevalidated. Internal validationchecks therelationbetween the individual

measures included in thescale, and thecompositescale itself. External validationchecks therelation

between thecompositescaleandotherindicatorsof thevariable, indicatorsnot included in thescale.

ontent validation

alsocalled facevalidity)checkshowwell thescalemeasureswhat issupposed to

measure.riterionvalidationcheckshowmeaningful thescalecriteriaarerelative tootherpossible


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criteria.

onstruct validationcheckswhat underlyingconstruct isbeingmeasured. Thereare three

variantsofconstruct validity. Theyareconvergent validity, discriminant validity, andnomological

validity

ampbell and

iske,

9

9; Krusand Ney,

97

). Thecoefficient ofreproducibility indicates

howwell thedata from the individual measures included in thescalecanbereconstructed from the

compositescale.

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